Search Results (2,048)

The rapid proliferation of the Internet of Things (IoT) leaves terminal devices vulnerable to considerable security challenges, notably the absence of robust yet efficient identity authentication mechanisms. Traditional certificate-based approaches incur substantial management overhead and storage expenditure, whereas Identity-Based Cryptography poses inherent key escrow risks. To tackle these challenges, this paper proposes a PUF and SM2-based certificateless identity authentication mechanism that integrates SM2 Certificateless Public Key Cryptography (a Chinese national cryptographic standard) with Physical Unclonable Functions (PUFs). Initially, the proposed solution utilizes PUF technology to derive a unique hardware-generated “fingerprint” from an IoT device, which functions as a root key to generate a partial user private key. This approach essentially binds the terminal’s identity to its physical hardware, thereby effectively mitigating physical cloning attacks against nodes. Moreover, through the adoption of a Certificateless Public Key Cryptography (CLPKC) framework, the complete user private key is jointly generated by a semi-trusted Key Generation Centre (KGC) and the terminal device itself. The comprehensive security analysis proves that the proposed scheme is provably secure under the random oracle model, capable of resisting various common attacks such as physical cloning, man-in-the-middle, and replay attacks. Performance evaluation confirms that the implemented PUF + SM2 certificateless mechanism significantly reduces the size of user public key identifiers to within 64 bytes, offering a substantial advantage over the 1–2 KB certificates typically required in conventional PKI/CA systems, thereby enhancing efficiency in storage and communication. Full article

Analyzing classroom engagement is essential for developing effective smart learning environments. Conventional methods often face challenges in achieving reliable identification of individual students, accurately recognizing their behavioral states, and providing timely support. This paper presents a multimodal sensing and supportive feedback system built upon an end–edge–cloud collaborative architecture. By integrating RFID-based seat association, fingerprint verification, and computer vision-based activity analysis, the system establishes a reliable link between student identity and observed activities. Key computational tasks, including activity recognition, spatiotemporal context matching, and rule-based assessment, are executed locally on edge nodes. This enables low-latency, privacy-conscious feedback delivered via Bluetooth, effectively avoiding delays associated with cloud processing. Experimental results indicate that the proposed system significantly enhances both activity recognition accuracy and identity–behavior association reliability in typical classroom scenarios while substantially reducing the average feedback latency compared to traditional approaches. Full article

This study comprehensively analyzed the effects of gamma irradiation (GI) on the flavor profile of aged light-flavor tartary buckwheat Baijiu (LTB) using E-nose, E-tongue, and high-sensitivity headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS). A total of 30 volatile organic compounds (VOCs) were identified, with concentrations showing significant dose-dependent correlations with GI treatment. Aging alone reduced harsh and pungent VOCs (e.g., 1-propanol, 2-methyl butanoic acid ethyl ester), while GI followed by aging further decreased undesirable compounds (e.g., butanal-D, pyrrolidine) and enhanced beneficial flavor components, such as 1,1-diethoxy ethane-D and butanoic acid propyl ester. Notably, this treatment partially restored 1-propanol, triethylamine, and 2-butanone-M, though their levels remained significantly lower than in newly brewed LTB, achieving a more balanced purity and flavor complexity. The significantly elevated levels of tetrahydrofuran-M/D, 1,1-diethoxy ethane-D, and cyclohexane in GI-treated aged LTB, along with their dose-dependent accumulation patterns, suggest their potential as reliable markers. Multivariate analysis confirmed that all three techniques (E-nose, E-tongue, and HS-GC-IMS) effectively differentiated LTB samples, with strong correlations between E-nose and HS-GC-IMS data, as well as between E-tongue and HS-GC-IMS results. This work provides flavor fingerprints and potential markers for gamma-irradiated LTB identification, while proposing an innovative technical approach for rapid flavor assessment of light-flavor Baijiu. Full article

Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC₅₀ values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines. Full article

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO_2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics. Full article

Biochemical detection is fundamental to various scientific disciplines, yet conventional methods still face inherent bottlenecks in achieving rapid, ultrasensitive, and simultaneous multi-target analysis. Terahertz (THz) waves, characterized by their unique spectral fingerprinting capabilities and non-destructive properties, have emerged as a compelling platform for advanced biochemical sensing. This review outlines the evolution of THz biochemical sensing over the past two decades, tracing its progression from passive identification toward intelligent perception. We structure this technological trajectory around four core themes: sensitivity enhancement, specific recognition, multi-target visualization, and system intelligence. We first evaluate the fundamental limitations of direct detection techniques, such as THz time-domain spectroscopy (THz-TDS). Building on this, we examine how metamaterial-assisted architectures utilize high-quality-factor resonances to achieve trace-level detection, pushing the limits of detection (LOD) down to the ng/mL or even pg/mL scale, and how surface chemical functionalization provides a molecular lock mechanism for selective targeting in complex samples. Furthermore, we highlight the paradigm shift from single-point spectral measurements to spatially resolved multi-target imaging using pixelated metasurfaces. Finally, the review addresses emerging directions, including dynamically tunable intelligent metasurfaces, multimodal on-chip integration platforms, and the growing integration of artificial intelligence (AI) in inverse design and data interpretation, which achieves classification accuracies exceeding 95% even in complex matrices. By synthesizing these developments, this review provides a comprehensive perspective on the future trajectory of THz sensing technologies. Full article

Neurotoxicity from drugs and environmental pollutants poses serious risks to brain function, yet existing computational models mainly target general neurotoxicity and lack specialized tools for brain-specific assessment. This study aimed to develop and validate a high-performance, brain-focused neurotoxicity prediction framework to improve drug safety evaluation and toxicity screening. We systematically analyzed molecular features, clustering patterns, and target predictions of brain-toxic compounds. Multiple feature representations were compared, including traditional molecular fingerprints, knowledge-guided pre-trained graph Transformer (KPGT) embeddings, and transformer-based MolFormer embeddings, combined with machine learning classifiers. Model performance was evaluated using multiple metrics, and SHAP analysis was conducted to identify influential molecular substructures. Toxic molecules showed physicochemical properties favoring central nervous system (CNS) penetration, including lower molecular weight, lower LogP, fewer hydrogen bond donors/acceptors, fewer rotatable bonds, and lower polar surface area (PSA). The KPGT-MLP model achieved the best balanced performance, with an accuracy (ACC) of 0.8928 and an ROC-AUC of 0.9459, clearly outperforming traditional fingerprint-based models, MolFormer-based models, and general prediction tools such as DI-NeuroT and ADMETlab 3.0. Overall, this study establishes a robust framework for brain-specific neurotoxicity prediction, with the KPGT-MLP model demonstrating strong accuracy and robustness. The proposed approach provides an effective strategy for early neurotoxicity screening and risk assessment, offering valuable insights for safer drug design and advancing computational toxicology and drug discovery. Full article

Xinhui Citri Reticulatae Pericarpium (CRP, “Chenpi”) is highly valued but is frequently challenged by origin-related adulteration and mislabeling. In this study, a rapid fingerprinting strategy based on direct analysis in real-time quadrupole time-of-flight mass spectrometry (DART-QTOF-MS) coupled with chemometric analysis was developed for the geographical characterization of CRP. DART-QTOF-MS enabled fast acquisition of mass spectral fingerprints with minimal sample preparation, and characteristic compounds were tentatively assigned on the basis of accurate mass and library matching. Comparative analysis showed that the high-mass region was dominated by polymethoxylated flavones and exhibited relatively limited between-region variation. In contrast, the low-mass region contained more evident origin-related differences and provided more informative variables for classification. Among the low-molecular-weight compounds, methyl N-methylanthranilate was markedly enriched in Xinhui samples, and 2-indolinone was identified as a promising candidate marker and further confirmed in CRP extracts by UPLC–MS/MS. OPLS-DA based on selected low-molecular-weight markers supported the discrimination of core Xinhui CRP and non-core Xinhui CRP. Overall, these results demonstrate the potential of DART-QTOF-MS as a screening tool for CRP origin authentication and highlight the value of low-molecular-weight markers for future quality-control applications. Full article

Brewer’s spent grain (BSG) is an abundant lignocellulosic by-product whose valorization can support circular bioeconomy strategies. This study evaluated BSG bioconversion by Aspergillus oryzae ATCC 10124 under solid-state fermentation (SSF) to produce lignocellulolytic enzymes and release second-generation (2G) sugars relevant to biorefinery applications. SSF was monitored over 0–10 days, and FPase, endo-cellulase, β-glucosidase, xylanase, mannanase, amylase, and ligninolytic enzyme activities were quantified. Enzymatic crude extracts were further assessed in SDS-PAGE analysis. Glucose, cellobiose, xylose and arabinose release and consumption were tracked throughout fermentation, and substrate transformation was supported by FTIR. The secretome exhibited a predominantly hydrolytic profile, with maximal hemicellulolytic and cellulolytic activity around days 2–4, as well as sustained amylase activity. Ligninolytic activity was not detected. Sugar profiles indicated rapid early hydrolysis of glucose, followed by progressive pentose release. The stabilization and decline were consistent with fungal uptake. Changes in the carbohydrate fingerprint and SDS–PAGE banding supported structural polysaccharide remodeling and hydrolytic protein secretion. Thus, this SSF platform confirmed certain potential for low-cost cellulolytic and hemicellulolytic enzyme generation. However, because sugar accumulation was temporary and followed by consumption, this system is best interpreted as a biological pretreatment and enzyme-generation step that supports subsequent downstream valorization. Full article

Background/Objectives: Recently, a randomized clinical trial evaluated whether a six-month probiotic administration could reduce symptom severity in preschool children with Autism Spectrum Disorders (ASD), with (GI) or without (NGI) gastrointestinal symptoms. Significant positive changes were observed only in NGI children. A second explorative study on children prior to intervention identified a fecal metabolome fingerprint associated with ASD severity. Building on these findings, the present study aimed to assess whether metabolomics could monitor changes in ASD severity following probiotic administration using a subset of samples from the same trial. Second, this study aimed to identify fecal metabolites to be monitored in children to predict whether their autism severity may decrease after probiotic or placebo treatment. Methods: Evaluations of the fecal metabolome and microbiota could be completed on 57 children before and after a double-blind administration of a probiotic mixture or a placebo. Results: In NGI children the probiotic was found to influence the concentration of the amino acids aspartate, leucine, tryptophan, and valine, together with nicotinate and the short chain fatty acids acetate, butyrate, isobutyrate, and propionate. Lactobacilli and Sutterella showed significant changes in response to probiotic administration (p < 0.05). Acetate, 4-hydroxyphenyl, galactose, proline, and tyramine were identified as key fecal metabolites for prediction purposes. Conclusions: The present exploratory analysis, despite the small sample size, suggests that fecal metabolomics may provide a useful approach for monitoring and potentially for predicting changes in ASD severity following probiotics administration. Full article

Municipal solid waste management remains a major environmental challenge across Africa, where rapid urbanisation has outpaced formal waste infrastructure and routine landfill monitoring is often absent. Rather than proposing a classification algorithm, this study investigates whether spaceborne hyperspectral imagery can reveal robust spectral fingerprints of landfill surfaces suitable for automated detection. Eight landfill sites across seven African countries were analysed using Hyperspectral Imager Suite (HISUI) data (400–2500 nm, 20 m resolution). A segment-based framework was applied after masking low signal-to-noise regions, combining brightness analysis, L2-normalised spectral shape comparison using Spectral Contrast Angle (SCA), and derivative spectroscopy across 109,275 pixels from six land-cover classes. Brightness-based discrimination exhibited strong inter-site variability, limiting its general applicability. In contrast, shape-based metrices revealed consistent separability between landfill-active surfaces and soil or urban classes in the shortwave infrared (SWIR), particularly within the 1538–1750 nm and 2075–2474 nm regions. Derivative analysis further identified stable extrema near approximately 1700 nm and 2200–2300 nm across all sites, indicating reproducible curvature-based fingerprints associated with exposed municipal solid waste. These results demonstrate that landfill surfaces exhibit intrinsic SWIR spectral characteristics that persist across diverse African environments. This study establishes the first multi-site hyperspectral library of African landfill surfaces, providing a physical basis for developing generalised landfill detection frameworks. Full article

Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%. Full article

Cinnamomum zeylanicum (C. zeylanicum) is rich in bioactives, such as cinnamaldehyde and phenols, which are susceptible to thermal degradation, volatilisation, and oxidative deterioration during processing and storage, thereby reducing chemical stability and limiting bioavailability. Encapsulation using lecithin and chitosan-based systems mitigates these instabilities by forming a protective barrier against oxygen, light, and heat while enhancing structural stability. In this study, freeze-dried extracts of C. zeylanicum were encapsulated into lecithin-based primary liposomes (PL) and chitosan-coated secondary liposomes (CH/L). The coating of liposomes with chitosan improves the liposome stability, mucoadhesion, and provides protection in the gastric pH while facilitating electrostatic bonding with the biological membrane. The high compatibility and low toxicity of chitosan also make it a suitable carrier in food and nutraceutical applications. The formed liposomes were characterised for particle size, polydispersity index, zeta potential, encapsulation efficiency (EE), and storage stability over 8 weeks. CH/L showed superior EE (89.027%) compared to the PL (84.154%; p < 0.05). The particle size, polydispersity index, and zeta potential of the cinnamon-loaded lecithin-based primary liposome (CZ-PL) upon formation were 161.93 nm, 0.13, and −37.597 mV. In comparison, those of the cinnamon-loaded chitosan-coated liposomes (CZ-CH/L) were 591.7 nm, 0.27, and +28.17 mV. The particle size of CZ-PL and CZ-CH/L was 175.90 and 588.60 nm after 8 weeks of storage. The TEM confirmed the spherical morphology of the liposomes. The differential scanning calorimetry analysis demonstrated the disappearance of the characteristic cinnamon melting peak and shifts in liposomal transition temperatures, confirming successful encapsulation. FTIR analysis showed reduction or disappearance of characteristic cinnamon fingerprint peaks and slight band shifts, indicating successful encapsulation and non-covalent interactions, including hydrogen bonding and electrostatic effects, within the liposomal systems. These findings imply that lecithin-based and chitosan-coated liposomes could be employed to successfully carry C. zeylanicum bioactives. Full article

Surface-enhanced Raman spectroscopy (SERS) amplifies Raman scattering by placing molecules in the near-field of plasmonic nanostructures, enabling label-free molecular fingerprinting. While attractive for living cell phenotyping, many cellular SERS works rely on internalized colloidal nanoparticles, leading to variable uptake/localization, aggregation-driven hotspot fluctuations, and potential cellular perturbation. Here, we report a chip-like Au/SiO₂ nanolaminate SERS substrate that supports direct culture and label-free measurements of living cells on spatially defined hotspots without nanoparticle uptake. The periodic nanolaminate forms dense nanogaps and is engineered for 785 nm excitation, providing uniform enhancement over a large, culture-compatible area with high hotspot uniformity. By engineering the cell–substrate nano–bio interface, the platform enables reproducible acquisition of intrinsic cellular vibrational fingerprints under physiological conditions without Raman tags. Using MCF-7 and MDA-MB-231 breast cancer cells, we collected hundreds of spectra per line, and MDA-MB-231 exhibited broader spectral variations, indicating greater heterogeneity. Principal component analysis and linear discriminant analysis achieved 99% classification accuracy for MCF-7 and MDA-MB-231, and bright-field imaging confirmed preserved adhesion and canonical morphologies. This chip-based, label-free living cell SERS platform enables scalable, nonperturbative phenotyping and may support rapid malignancy classification and treatment response screening across subtle cancer states. Full article

Serum contains diverse proteins whose concentrations vary with pathological conditions such as cancer, liver disease, neurological disorder, and infections. Conventional methods like serum protein electrophoresis (SPEP) and enzyme-linked immunosorbent assay (ELISA) are gold standards for protein identification; however, they are time-consuming and can miss abnormal serum protein levels. Inspired by chemical nose sensing based on selective sensor–analyte interactions, we synthesized five pyrene-conjugated fluorescent polymers (PFPs) with distinct side-chain head groups to construct a multichannel fluorescence sensor array. These polymers were screened for sensitivity to changes in serum protein levels using linear discriminant analysis (LDA), a machine learning method. This process led to the successful discovery of two PFPs that effectively detect protein level fluctuations. These PFPs provided a sensitive sensor array capable of generating a high-content response pattern (fingerprint) with six fluorescence channels. This sensor array successfully discriminated protein level fluctuations in serum with 98% jackknife classification accuracy and 95% unknown identification accuracy. This polymer sensor array holds strong potential as a diagnostic tool for serum-based samples and can be extended to other applications related to protein identification. Full article