Search Results (209)

Near-infrared (NIR)-responsive nanocarriers offer spatially and temporally controlled heating, drug release, and imaging for biomedical applications. Gold nanorods (AuNRs) and polydopamine (PDA) are two widely investigated platforms with complementary properties: AuNRs provide spectrally tunable plasmonic heating and strong optical imaging capability, whereas PDA offers broadband absorption, adhesive surface chemistry, high cargo-loading capacity, and compatibility with hydrogels and regenerative matrices. Polysaccharide modification can improve physiological stability, reduce nonspecific biological interactions, and introduce receptor targeting, mucoadhesion, or microenvironment responsiveness. This review compares the conjugation chemistry, therapeutic mechanisms, pharmacokinetics, biosafety, and biomedical applications of polysaccharide-modified AuNR and PDA systems, with emphasis on tumor therapy, antibacterial treatment, wound repair, and local drug delivery. AuNR-based platforms are most suitable when rapid wavelength-selective heating and image guidance are primary requirements, whereas PDA-based systems are generally better suited to sustained delivery, tissue adhesion, and long-term local integration. We further discuss AuNR–PDA hybrid architectures and identify key translational barriers, including inconsistent characterization, uncertain long-term fate, nonstandardized photothermal dosimetry, and limited manufacturing reproducibility. This function-oriented comparison provides a practical framework for selecting polysaccharides and carrier platforms according to administration route, biological barrier, therapeutic objective, and safety requirements. Full article

Long-period waveguide gratings (LPWGs) are key wavelength-selective components with broad applications in optical communications and sensing. Silicon nitride (SiN) is an attractive platform due to its wide transparency window, low optical loss and CMOS compatibility; however, conventional SiN-based LPWGs lack efficient high-speed tuning mechanisms, which limits their use in dynamically reconfigurable systems. In this work, an electro-optically tunable LPWG based on a SiN/electro-optic polymer (EOP) hybrid structure is proposed and optimized. An intermediate Epocore isolation layer is introduced between the SiN core and the EOP cladding to suppress the electric-field-induced modulation of the core mode. This configuration enables more efficient EO tuning of the effective index difference between the core mode and cladding mode. A systematic parametric optimization is carried out, including waveguide geometry, grating period, and electrode configuration. The simulation results show that the proposed device exhibits a high tuning efficiency of 1.41 nm/V. The proposed design combines compact footprint, CMOS compatibility, and efficient electro-optic tunability, providing a practical approach for reconfigurable integrated photonic filters. Full article

To achieve rapid and non-destructive detection of watercore in apples, the apple Vis–NIR spectral data were used as the research object in this study. A total of 256 × 2 wavelength-based spectral features were extracted, followed by dimensionality reduction via PCA. Subsequently, four machine learning classification models—PLS-DA, SVM, Logistic Regression, and Random Forest—were constructed to identify apples with varying severities of watercore. The results showed that the SVM model achieved the optimal comprehensive performance, with an accuracy of 85.30% and a precision of 91.67% on 34 independent test samples. The five-fold cross-validation accuracy of SVM reached 87.20%, indicating the best generalization stability among the four models. PCA analysis showd that after dimensionality reduction of the core wavelength bands obtained via feature screening, eight principal components can cumulatively explain 95.1% of the total spectral variance; normal apples and watercore-affected apples exhibited significant distribution differences in the principal component space. This study adopted a preprocessing scheme that combined feature selection with SMOTE sample balancing, which effectively alleviated the impact of class imbalance on model performance. It also indicated that insufficient fine-grained classification accuracy and the low recall rate of watercore samples was the key directions for subsequent optimization. Full article

The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021. Full article

This study addresses two key challenges in tea nutrient analysis: the limited range of detectable nutrient elements in tea gardens and the interference caused by moisture in fresh tea leaves during spectral data acquisition. To overcome these issues, hyperspectral technology combined with effective spectral intelligent processing algorithms was used to develop quantitative, non-destructive prediction models for four essential nutrients—nitrogen, phosphorus, potassium and carbon—in fresh tea leaves. This study utilises EPO to address the issue of moisture interference in the spectra of fresh tea leaves, and combines it with SG for spectral data processing, namely SG-EPO. Through comparative analysis with traditional pre-processing algorithms, this method was found to effectively reduce moisture interference in fresh tea leaves and enhance prediction accuracy (R²). Finally, based on a multi-stage feature selection strategy involving SG-EPO-VCPA-IRIV-SVM_RFE and SG-EPO-BOSS-SVM_RFE, and in combination with three machine learning models—XGBoost, BP and SVR—quantitative prediction models were developed. The results indicated that the R² for nitrogen is 0.896; phosphorus, 0.954; potassium, 0.913; and carbon, 0.928. The RMSEP values for N, P, K, and C were 0.062, 0.075, 0.438, and 0.157, respectively. Using this model, nitrogen, phosphorus, potassium and carbon contents were rapidly predicted in tea leaves after the exogenous application of GABA at different concentrations, enabling an assessment of the effects of exogenous GABA application on these nutrient levels. Furthermore, the Shapley Additive Explanation method was employed to identify the feature wavelengths that had the greatest contribution to the XGBoost model, effectively explaining the information underlying the improved model predictions regarding the correlation between spectral and chemical values. Finally, the accuracy of the predictions was confirmed using 20 samples from independent data, demonstrating that the proposed model can achieve rapid, non-destructive detection of multiple nutrient elements in fresh tea leaves under in situ conditions in tea plantations. Full article

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges. Full article

Soil organic carbon (SOC) is a key indicator of soil quality, farmland productivity, and the terrestrial carbon cycle. Visible and near-infrared (Vis-NIR) spectroscopy offers a rapid approach for SOC estimation, but wavelength point selection may disrupt continuous spectral structures, whereas conventional wavelength interval selection may fail to fully exploit complementary information across spectral regions. In this study, a synergistic interval-constrained Ridge regression framework, termed sicRidge, was developed for SOC prediction. Continuous candidate intervals were generated using a sliding-window strategy, and a dual-interval synergistic search with an overlap constraint was applied to identify complementary and low-redundancy interval combinations. The selected intervals were then used to construct Ridge regression models. Using Vis-NIR spectra from 168 soil samples, sicRidge was compared with full-spectrum Ridge regression, five wavelength point selection-based Ridge models, and several wavelength interval selection-related benchmark models. sicRidge achieved the best prediction performance using 140 selected bands, with an R²_P of 0.834, RMSE_P of 2.010 g kg⁻¹, RPD of 2.483, and RPIQ of 3.777. The optimal intervals were 570~649 nm and 1880~1939 nm. These results indicate that sicRidge can improve SOC prediction by preserving continuous spectral structures while exploiting complementary cross-region information. Full article

Photosynthetic phenotypic parameters of winter wheat are important indicators of canopy physiological status, photosynthetic function, and crop growth. However, canopy-scale hyperspectral estimation of these parameters remains affected by canopy structural heterogeneity, environmental variation, and mixed spectral signals. This study evaluated the contribution of visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions and their combinations to estimating photosynthetic phenotypic parameters of winter wheat. Field experiments were conducted under three nitrogen application levels and 65 winter wheat genotypes, and a total of 507 valid canopy-level samples were used for model development and validation. Competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelengths, and partial least squares regression (PLSR), Bayesian ridge regression (BR), and backpropagation neural network (BPNN) were applied to construct estimation models. Model performance was assessed using R², RMSE, and RPD. Results showed that NIR-based models achieved the best overall performance, with the highest validation R² of 0.828 for photosynthetic rate. The VIS + NIR combination showed stable predictive ability across multiple parameters, whereas SWIR-only models showed limited performance, with R² values below 0.5 for most parameters. Photosynthetic rate, intercellular CO₂ concentration, performance index on an absorption basis, and chlorophyll a content were predicted more accurately than the other traits. These findings indicate that canopy hyperspectral data can support quantitative monitoring of photosynthetic phenotypic parameters, and that NIR-related structural and scattering information plays a key role in winter wheat canopy phenotyping. Full article

Traditional methods for determining starch content in corn kernels are labor-intensive, destructive, and inefficient. To overcome these challenges, this work developed a rapid, non-destructive approach based on near-infrared hyperspectral imaging, applied to 58 rainfed corn varieties. A spectral preprocessing scheme combining wavelet transform, multiplicative scatter correction, and standard normal variate transformation was employed to enhance spectral quality. A two-stage wavelength selection framework was established using competitive adaptive reweighted sampling and sparrow search algorithm optimization. From the selected optimal wavelengths, four predictive models, namely partial least squares regression, artificial neural network (ANN), convolutional neural networks, and gradient boosting decision tree, were established, implemented, and systematically compared. The results identify 14 key wavelengths (1020.65–1647.71 nm) strongly correlated with starch content, with clear assignments to specific chemical bonds and good physical interpretability. Among these models, the ANN exhibited the best performance. The R², RMSE, and RPD of the test set were 0.826, 0.759%, and 2.40, respectively, indicating favorable prediction accuracy and generalization ability. These key wavelengths provide a foundation for developing portable detection instruments. This work supports corn quality grading, breeding of high-starch varieties, and rapid raw material screening, thereby enhancing the quality and efficiency of the corn industry. Full article

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for detecting, identifying, and quantifying analytes of interest in both environmental and clinical contexts. A key factor in SERS is the choice of substrate, which directly influences the enhancement factor and measurement reproducibility. A wide range of commercial SERS substrates is currently available, featuring tailored nanostructures and surface patterns designed to optimize signal enhancement. Recently, SERS has also been applied to the development of detection strategies for phenolic compounds. Within this framework, we aimed to evaluate several commercial SERS substrates and one homemade SERS substrate using catechol as a molecular probe. Each substrate was initially assessed by acquiring spectra of the bare substrate using the laser excitation wavelengths recommended by the manufacturers. Raman spectra of catechol solutions at relatively high concentrations were also acquired using the same wavelengths. These preliminary measurements guided the selection of experimental conditions for subsequent substrate performance evaluations. Hyperbola and linear function fitting were performed to quantitatively characterize the tested substrates in catechol detection. The proposed approach allowed for the identification of a parameter that can be used for estimating a substrate’s overall efficiency, along with the main sensing figures of merit. Full article

Reflectance by yellow to red carotenoid-based plumages in birds with ultraviolet-sensitive (UVS) color vision typically shifts to redder (longer) wavelengths as carotenoid consumption (Diet_c) increases. This apparent asymmetric red-shift response implies an overall bias against conceivable shifts to bluer (shorter) wavelengths. However, recent studies among species of Piciformes–Coraciiformes (e.g., woodpeckers, barbets, toucans, bee-eaters, and allies) with violet-sensitive (VS) color vision discovered two kinds of blue shifts between the same plumage and dietary traits. Compared to UVS absolute red shifts (positive slopes at higher Diet_c), VS express absolute (negative slopes at higher Diet_c for yellow and orange plumages) or relative (zero slope for red plumages) blue shifts. These contrasting patterns for different color vision systems suggest that generalized symmetry concepts of opposite (plumage shift) patterns that maintain invariant (Diet_c, perception) processes can be abstracted from physical to biological systems, with positive versus negative responses formalized as “antisymmetries” and relative responses formalized as “broken symmetries”. A subset of VS “true woodpecker” (Picinae) species with known yellow and red plumage-carotenoid chemistries demonstrated similar blue shifts for the main reflectance bands and their independence from phylogeny, sex, and specimen collection year, thus providing key chemical details to further test generalized plumage symmetries. Juxtapositions were facilitated because both yellow and red plumages in true woodpeckers contained comparatively blue- and red-shifted carotenoid types. Despite this parallel, each plumage deployed chemical variations in radically different ways. Yellow plumage contained only chemically defined yellow carotenoids, including intrinsically more red-shifted natives (i.e., hydroxy-xanthophylls) widespread among birds through their diets, and intrinsically more blue-shifted picofulvins (i.e., 7,8-di- and tetra-hydro-carotenoids) probably characteristic of and metabolized by VS birds. Higher absolute and relative (to natives) picofulvin concentrations were significant predictors of absolute blue shifts in yellow plumage reflectance, and were significantly predicted by higher Diet_c. Transitivity implied Diet_c ⟶ native ⟶ picofulvin ⟶ reflectance, such that picofulvins caused absolute blue shifts at higher Diet_c, and natives caused absolute red shifts at lower Diet_c. Moreover, opposite trends for picofulvin and native concentrations in feathers were consistent with the proposed endogenous synthesis of picofulvins from natives. Yellow plumages comprised mainly of picofulvins at very low (from very low Diet_c from ants and termites) or high (from very high Diet_c from fruits) concentrations were especially distinctive for some of these and other interrelationships, suggesting some heterogeneity in yellow pigmentation strategies from dietary idiosyncrasies. Red plumages contained only relatively low concentrations of yellow dietary natives (hydroxy-xanthophylls), but varied widely in the concentration of metabolites of comparatively intermediate (4-oxo-keto-carotenoids) or extreme (4,4′-oxo-keto-carotenoids) redness. However, different red chemistries lacked any corresponding significant relationships with variations in reflectance or Diet_c. Variations in reflectance based on chemical compositions were more visible to humans for yellow than red plumage types, setting minimum salience levels for the more discriminating diurnal avian color visions. Therefore, VS yellow plumage chemistries that emphasize deposition of easily obtained (cheaper) dietary natives at low Diet_c, and of more deliberately synthesized (costly) picofulvin metabolites at higher Diet_c were consistent with several forms of honest signaling in UVS from resource limitations based on Diet_c, including through potential costs and benefits and their trade-offs. Conversely, the diverse chemical compositions and costs of red plumages of similar physical reflectance properties, and evidence that intrinsically orange carotenoids intermediate between red and yellow ones were actively excluded from plumage, suggested that true woodpecker reds were under selection for a convergent appearance. In light of true woodpecker biology, sensory bias, and social and aposematic mimicry are likely mechanisms promoting resemblance. These results extend to the chemical level earlier interpretations of opposite shift patterns as antisymmetries of invariant processes and relative shift patterns as broken symmetries of altered processes for VS vis-à-vis UVS carotenoid-based systems. Full article

Background/Objectives: With the increasing incidence of drug counterfeiting and the emergence of personalized medicine, the need for unique marking of solid dosage forms, e.g., tablets, has attracted considerable interest in the current research and development landscape. Besides traditional printing methods, laser marking offers several advantages, as it eliminates the need for organic solvents and enables the generation of precise patterns. However, laser exposure may raise safety concerns regarding the stability of photosensitive drugs in the irradiated dosage forms. Therefore, the aim of the present study was to test the photoprotective effect of titanium dioxide (TiO₂) and its various alternatives, e.g., talc, calcium carbonate (CaCO₃), zinc oxide (ZnO), and black iron oxide (Fe₃O₄), alongside a ready-to-use reference formulation, Opadry^® Brown, which contains TiO₂ (titanium-containing, TC) on nifedipine, a light-sensitive model drug. Methods: Laser marking or short-term laser ablation at different wavelengths (193 nm, 248 nm, 532 nm, and 781 nm) was applied to different coating formulations. As a positive control, prolonged exposure to daylight was applied. The properties and photostability of these formulations were evaluated using several analytical methods (i.e., surface profilometry, Raman spectroscopy, and high-performance liquid chromatography (HPLC)). Results: The TiO₂, ZnO, Fe₃O₄, and Opadry^® TC Brown coatings maintained their color during the long-term study under all conditions. Furthermore, the prepared formulations exhibited different ablation depths and morphological changes depending on the coating and laser type. HPLC measurements confirmed significant differences in the protective ability of various pigments against sunlight and different types of lasers. Nevertheless, the obtained Raman spectra were not in complete agreement with HPLC results, which can be attributed to spectral overlap between key nifedipine degradation markers and excipient signals in the tablet core. Conclusions: Overall, laser treatment of tablets containing photosensitive drugs may induce API decomposition; however, this effect can be minimized or avoided by careful selection of the appropriate combination of laser type and photoprotective pigment. Under the applied experimental conditions, Ti:Sa laser treatment was associated with the lowest degree of nifedipine degradation among all formulations, while ZnO-containing coatings demonstrated the most consistent photoprotective performance against the majority of the tested laser types, while Fe₃O₄-containing coatings provided superior protection during prolonged sunlight exposure and Nd:YAG laser irradiation. Full article

This research analyzes the wavy, axisymmetric flow of a Ree–Eyring non-Newtonian nanofluid, infused with motile microorganisms, within a porous, tapered cylindrical channel under a transverse magnetic field. This investigation presents a theoretical framework that may inform the improvement of energy efficiency and thermal management in biomedical engineering applications, such as drug delivery systems and microfluidic biosensors. The work provides an extended insight by a contribution to the evaluation of entropy generation, explicitly considering the influence of motile microorganisms, thereby bridging a gap in the existing literature. The comprehensive physical model further incorporates the combined effects of Joule heating, viscous dissipation, nonlinear thermal radiation, and chemical reactions. Methodologically, the governing nonlinear equations of the system were rendered tractable under long-wavelength and low-Reynolds-number assumptions and subsequently solved using the numerical Runge–Kutta–Fehlberg technique. The key conclusion is that, based on the present numerical model, careful selection of magnetic field strength and microorganism motility parameters may reduce irreversible energy losses, potentially improving the net usable work in advanced nanofluid transport systems for biomedical applications, subject to experimental validation. The most significant finding reveals that the magnetic field serves as a dual-purpose control parameter: increasing its strength boosts total entropy generation by 20–30% while simultaneously raising the Bejan number, confirming heat transfer as the dominant irreversibility mechanism in the system. Additionally, nanoparticle concentration diminishes substantially with elevated chemical reaction rates and Schmidt numbers, while microorganism density is highly sensitive to the Péclet number, which causes flow disruptions. Full article

The misuse of antibiotics is causing widespread antibiotic resistance, creating an urgent need for new treatment options such as nanoparticle-based therapies. This study aimed to compare silver nanoparticles (AgNPs) produced via green synthesis methods with those made through traditional chemical processes. Furthermore, the study investigated and contrasted the bacterial responses to these two types of AgNPs over a 21-day period of selection pressure using experimental evolution techniques. Analysis using scanning electron microscopy and transmission electron microscopy revealed a consistent, uniform morphology among the AgNPs produced via chemical methods. In contrast, AgNPs synthesized through green methods displayed an irregular morphology. Despite these morphological differences, all nanoparticles from both synthesis approaches were under 100 nm in diameter. These findings were further supported by the absorption spectrum data, which showed a maximum absorption peak between the 400 and 500 nm wavelength range. E. coli exposed to green synthesized AgNPs for 21 days adapted to their presence, exhibiting both enhanced resistance to the green synthesized AgNPs themselves and the development of cross-resistance to ionic silver, a pattern not observed in chemically synthesized AgNP-selected populations. Populations selected using chemical synthesized AgNPs did not develop increased resistance to either chemically or green synthesized AgNPs; however, they showed a slight increase in resistance to ionic silver. Genomics analysis identified polymorphism in genes in a green synthesized AgNP-resistant line including but not limited to the multidrug efflux transporter system (EmrAB), DUF4756 family protein (D1792_RS05680), putative zinc-binding protein YnfU/cold shock-like protein (ynfU/cspB) and imcF-related family protein (D1792_RS10035). Bacterial resistance to chemical AgNPs involves specific polymorphisms in key bacterial components like the RNA polymerase sigma factor (RpoE) and the EmrAB efflux pump. Collectively, the method used to synthesize the AgNPs influences their antibacterial efficacy and the likelihood of bacteria developing resistance. Understanding this interaction is vital for developing effective and resistance-controlled applications of AgNPs across medicine, environmental science, and industry. Full article

Populus pruinosa Schrenk is a keystone species in arid riparian ecosystems, where its physiological status is critical for biodiversity and soil stabilization. In this study, spectral reflectance, leaf chlorophyll density (CHD), and leaf water content (LWC) were measured for Populus pruinosa in the Tarim River headwater region and Awati County, Xinjiang, from July to October 2023. The aim was to estimate CHD using hyperspectral data combined with machine learning and to evaluate the effect of LWC on model accuracy. Raw spectra were preprocessed using Savitzky–Golay (SG) smoothing and continuous wavelet transform (CWT). A two-step feature selection strategy comprising Random Frog and iterative retaining informative variables (IRIV) was applied to extract characteristic bands. Three machine learning models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—were developed for CHD estimation with and without LWC as an additional input. Incorporating LWC consistently improved the predictive performance of all models. Without LWC, the RF model achieved the best accuracy (training R² = 0.842, test R² = 0.830), whereas after LWC integration, XGBoost reached the optimal performance (training R² = 0.871, test R² = 0.865). SHAP analysis identified the 687 nm wavelength and its interaction with LWC as the most important predictors. These results indicate that combining spectral information with LWC effectively improves the accuracy and stability of CHD estimation for Populus pruinosa, providing a reliable non-destructive approach for assessing forest ecosystem physiological status—a key contribution to the sustainable management of arid riparian forests. Full article