Search Results (3,155)

Background: Vestibular migraine (VM) is a common cause of episodic vertigo, yet its diagnosis remains primarily clinical and is often complicated by the absence of reliable objective biomarkers. Pupillary nystagmus, reflecting spontaneous oscillations of pupil diameter, has been proposed as a potential clinical sign of VM, but its quantitative characterization remains limited. Objective: The objective of this study is to evaluate the diagnostic value of pupillary nystagmus in VM and to provide a quantitative assessment using infrared pupillometry. Methods: In this case–control study, 137 patients with vestibular migraine and 102 healthy controls underwent comprehensive neuro-otological evaluation, including vestibular testing and pupillometric assessment. Pupillary activity was recorded using a dedicated infrared pupillometer, and oscillatory dynamics were quantified using the Pupillary Unrest Activity Level (PUAL), which was derived through spectral analysis (Larson–Neice algorithm). Statistical comparisons were performed using non-parametric methods. Results: PUAL values differed significantly between VM patients and controls (Mann–Whitney test p < 0.001), demonstrating a clear separation between groups. A cut-off value of 0.325 was identified as the upper limit of normality, suggesting that elevated PUAL values may indicate vestibular migraine. Conclusions: Pupillary nystagmus represents a clinically accessible sign that can be objectively quantified through infrared pupillometry. The PUAL index provides a measurable parameter reflecting altered vestibulo–autonomic dynamics in VM and may serve as a promising neuro-otological biomarker. The integration of pupillometric analysis with clinical evaluation may improve diagnostic accuracy and support the development of objective diagnostic tools in vestibular migraine. Full article

This paper describes the comprehensive molecular characterisation and application of a commercially available, but structurally undefined, photochromic pigment for the development of textile sensors. The commercial pigment was successfully identified using a multianalytical approach, including analysis using nuclear magnetic resonance (NMR), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). The identified pigment, ethyl-3′-methyl-3′-phenyl-1′-(propan-2-yl)-1′,3′-dihydrospiro[[4,1,2]benzoxadiazine-3,2′-indole], was used to develop a textile sensor by screen printing on a natural fibre fabric surface. The developed sensor exhibited a reversible colour change from white to pink upon exposure to UVA radiation (369 nm). The sensor is characterised by high sensitivity with a linear dose–response of 0–0.005 J/cm² and a dynamic range of up to 0.05 J/cm². Furthermore, the sensor’s molecular safety profile was assessed, including elemental composition and cytotoxicity tests on human dermal fibroblasts, which confirmed the sensor’s biocompatibility with occasional skin contact. In addition to its use in decorative and security elements for product authentication, this study demonstrates the sensor’s ability to map the 2D UVA radiation dose distribution. This research highlights the importance of precise molecular identification in the design of functional, safe, and intelligent textile systems. Full article

Green synthesis of gold nanoparticles is an effective approach to create biocompatible nanomaterials. In this study, gold nanoparticles (BC-AuNPs) were prepared by reducing chloroauric acid with black corncob (BC) extract at relatively low temperatures. The optimal preparation conditions were obtained through a single-factor experiment, which included 5 mL of black corncob extract and 0.12 mL of 3% HAuCl₄ solution at a pH of 5.0, and the reaction was carried out at 50 °C in a water bath for 3 h. The prepared BC-AuNPs were characterized by ultraviolet–visible (UV-Vis) spectroscopy, Fourier-transform infrared (FTIR) analysis, transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), dynamic light scattering (DLS), and Zeta-potential measurement, which showed that they were dispersed spherical particles with an average size of approximately 23.0 nm and their surfaces were covered with various black corncob active components. The photothermal performance test indicated a good photothermal effect with a conversion efficiency of 41.3%. Antibacterial experiments revealed that BC-AuNPs had excellent antibacterial activity. The minimum inhibitory concentrations (MICs) for E. coli and Salmonella were 25.00 and 50.00 µg/mL, respectively. Overall, this study proved a potential application for gold nanoparticles in photothermal antibacterial fields. Full article

Infrared imaging technology has been widely adopted for industrial gas leak detection due to its capability for large field-of-view, long-range, and dynamic monitoring. However, in practical applications, natural object interference within the scene, together with the blurred contours and low contrast of infrared images, severely degrades the performance of gas detection and leakage region segmentation. To address these challenges, this paper proposes a gas leak detection method that integrates gas characteristics with spatiotemporal information. Specifically, the non-solid characteristics of gas are incorporated to constrain the foreground extraction process of the Gaussian Mixture Model (GMM), thereby suppressing interfering moving objects. Furthermore, by exploiting the spatiotemporal information in infrared image sequences, a multi-scale cross-attention fusion model is designed to fuse multi-scale and global feature representations, improving the accuracy of foreground detection. Finally, density-based clustering is employed to achieve complete segmentation of gas regions with irregular shapes. Experimental results demonstrate that the proposed method effectively suppresses interference from solid objects, accurately detects gas leakage, and successfully segments the diffusion regions. Compared with existing approaches, the proposed method shows significant advantages and provides a valuable reference for research on infrared imaging-based gas leak detection. Full article

Insufficient oxidative capacity can limit humification during municipal sludge composting. This study comparatively evaluated two Fenton-like amendment systems, a homogeneous copper-based treatment (CH) and a heterogeneous nano-iron-based treatment (NFH), for their effects on composting performance, humification-related indices, spectroscopic characteristics, and bacterial community succession. Both amended treatments improved composting performance relative to the control, reaching higher peak temperatures (68.5 °C for CH and 70.3 °C for NFH) and prolonging the thermophilic phase. NFH also showed stronger moisture removal, with the final moisture content decreasing to 58.1%, compared with 65.1% in CH and 64.1% in the control. CH showed the highest apparent humic acid accumulation (1173 mg kg⁻¹), whereas NFH exhibited spectroscopic features commonly associated with lower E₄/E₆ ratios and more pronounced humic-like fluorescence characteristics. Ultraviolet–visible spectroscopy (UV–Vis), Fourier transform infrared spectroscopy (FTIR), and excitation–emission matrix fluorescence spectroscopy (EEM) analyses collectively indicated progressive transformation toward more aromatic and humified organic matter in the amended treatments. Bacterial community succession also differed across treatments, and several enriched taxa, including Rhodanobacter and Thermobifida, showed positive associations with reactive oxygen species (ROS)-related variables and humification indices. These results describe treatment-linked dynamics in humification and suggest corresponding changes in microbial succession during sludge composting, with potential implications for process outcomes. Full article

When poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is employed as the hole transport layer in visible/near-infrared photodetectors, the extraction and transport of holes are hindered by the accumulation of the PSS insulating phase at the interface. This accumulation results in an increase in contact resistance and creates a potential barrier for hole injection. This study introduces a self-assembled monolayer, (2-(9H-carbazol-9-yl)ethyl)phosphonic acid (2PACz), to modify PEDOT:PSS, effectively optimizing the interface of the hole transport layer. Such improvements lead to a reduction in recombination losses during charge transfer, a lower dark current, and improved energy level alignment in the device, thereby boosting the performance of visible/near-infrared photodetectors. The fabricated double hole layer photodetector exhibits a low dark current of (1.4 ± 0.6) × 10⁻⁵ A at −1 V bias and a switching ratio of up to 7.62 × 10⁵ at 0 V bias. The device achieves a responsivity of 0.31 A/W and a high specific detection rate of 3.23 × 10¹² Jones at a wavelength of 780 nm, which corresponds to the peak responsivity, showcasing enhanced detection capabilities. In comparison to a reference device based on PEDOT:PSS, the response speed, cutoff frequency, and linear dynamic range of the double hole layer device have been enhanced by 400%, 213%, and 81%, respectively, thereby better aligning with practical application requirements. This research presents a novel approach for the development of high-performance organic visible/near-infrared photodetectors. Full article

The increasing prevalence of drug-resistant microorganisms has prompted research into novel antimicrobial compounds, with 2-thiophene carboxylic acid thiourea derivatives showing promise for future therapeutic applications. However, the poor water solubility of these compounds limits their practical use. This study investigates the formation and characterization of inclusion complexes between 2-hydroxypropyl-β-cyclodextrin (HPβCD) and 2-thiophene carboxylic acid-halogenated (chlorine-, bromine-, and iodine-) thiourea derivatives, seeking to improve their physicochemical properties. The dynamic light scattering (DLS) measurements and UV-Vis spectroscopy provided information related to thiourea–HPβCD aggregates and stoichiometry. Solid-state inclusion compounds and physical mixtures were prepared in two different molar ratios (thioureas:HPβCD = 1:1 and 1:2), and the morphology of the resulting powders was observed by scanning electron microscopy (SEM). Thermogravimetry (TG) and differential scanning calorimetry (DSC) (TG-DSC) coupled analysis were used to analyze thermal profiles in the temperature range of 25 °C to 600 °C, while the spectral data obtained by Fourier transform infrared spectroscopy (FTIR) provided the characteristic vibrational bands of the pure guest molecules and data corresponding to the structural and chemical changes in the host–guest systems. The structural and thermal analyses revealed significant interactions between the host and thioureas molecules, with evidence of possible interactions involving two cyclodextrin molecules. The results demonstrate the presence of intermediate stoichiometry in the inclusion compounds, with possible enhancement of the therapeutic potential of these thiourea derivatives. Full article

The scalable production of high-quality nanoparticles is a significant challenge for advancing nanotechnology applications. This research introduces a continuous-flow liquid-plasma discharge reactor for the synthesis of silver nanoparticles at room temperature and atmospheric pressure, utilizing D-xylose as a dual-function reducing and stabilizing agent. The reactor effectively generated uniform xylose-capped silver nanoparticles (X-Ag NPs). Optimal conditions were established utilizing argon gas at a 1:100 molar ratio of Ag precursor to D-xylose, resulting in spherical X-Ag NPs with an average size of 16.89 nm, a zeta potential of −38.87 mV, and a polydispersity index of 0.22. The formation and properties of X-Ag NPs were confirmed through characterization techniques including UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FT-IR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). The findings demonstrate that uniform particle nucleation and growth occurred due to the homogeneous distribution of high-energy electrons and reactive gas species produced in the plasma phase. This environmentally sustainable, continuous-flow method shows considerable promise for the industrial-scale production of biomass-derived silver nanoparticles. Full article

We present a pedagogical and self-contained account of the functional formulation of non-Abelian gauge theories, aimed at the construction of a process-independent effective charge for Yang–Mills theory. Starting from the path integral quantization of gauge fields, we review gauge fixing and the emergence of Faddeev–Popov ghosts, illustrating how gauge invariance is preserved at the quantum level through Becchi–Rouet–Stora–Tyutin (BRST) symmetry. We then develop the BRST and anti-BRST formalisms and show how their simultaneous implementation leads to powerful functional identities that severely constrain the ghost and gluon sectors. Background field gauges are introduced as a natural framework in which these symmetries manifest themselves through Abelian-like Ward identities, allowing for a transparent separation between quantum and background degrees of freedom. This structure makes it possible to define renormalization group-invariant combinations of Green functions that generalize the QED effective charge to the non-Abelian case. The resulting effective charge is shown to be unique, gauge-invariant, and process-independent, providing a unified description of the theory from the ultraviolet down to the infrared. The interplay between functional identities, Dyson–Schwinger equations, and lattice results is discussed in detail, highlighting how dynamical mass generation and infrared saturation naturally emerge within this framework. Full article

The emerging threat of antibiotic-resistant pathogens and the limitations that conventional cancer chemotherapies display have created an urgent need for the development of innovative therapeutic strategies. Combining the pleiotropic biological roles of antimicrobial peptides (AMPs) and nanomaterials through their conjugation presents a promising possibility of targeting both microbial membranes and malignant cells. In the present study, we engineered a novel bioactive material by immobilizing the insect-derived AMP Papiliocin onto multi-walled—decorated with polyethylene–glycol—carbon nanotubes (PEG-MWCNTs) to prevent proteolytic degradation of the peptide and enhance its cellular delivery. Recombinant Papiliocin was cloned, heterologously expressed, purified and conjugated onto the PEG-MWCNT carrier. Successful expression and conjugation were validated via immunoblotting and Fourier transform infrared (FT-IR) spectroscopy, respectively. Further physicochemical characterization of the bionanocomposites was conducted using Dynamic Light Scattering (DLS) and Zeta potential measurements. Biologically, the biofunctionalized material exhibited potent, broad-spectrum antimicrobial activity both on Staphylococcus aureus and Escherichia coli, inhibiting almost 90% of the latter’s growth, highlighting the bioconjugate’s specific interactions with the Gram-negative pathogens’ membranes. Furthermore, it significantly reduced biofilm formation in Candida albicans, as indicated by the TCP assay. In parallel with its antimicrobial effects, CNTs-PEG–Papiliocin significantly reduced cancer cell viability and induced apoptosis via the extrinsic apoptosis pathway in HeLa cells, a response assisted by efficient intracellular delivery. Notably, cytotoxicity assays demonstrated lesser cytotoxic effect against non-tumorigenic HaCaT cells relative to the cancerous cell line. Collectively, these findings indicate the Papiliocin–biofunctionalized CNTs as a versatile, dual-action therapeutic agent with potential for antimicrobial activity and anticancer mode of action. Full article

The increasing demand for reliable and high-performance electric vehicle (EV) batteries requires precise and defect-free welding of battery components. Conventional Gaussian laser beam welding faces challenges such as keyhole instability, spattering, porosity, and brittle intermetallic compound formation, particularly in dissimilar Al-Cu joints. These issues significantly affect the electromechanical performance and durability of battery connections. Beam shaping technology has emerged as a core method for improving weld quality, process stability, and efficiency in laser welding, making laser beam welding increasingly vital for high-volume production of e-mobility components. This review systematically evaluates recent advancements in laser beam shaping for laser welding, especially static beam configurations, such as core-ring profiles, flat top, elliptical, and shaped beams; emphasis has been placed on how altering the intensity distribution influences the challenges associated with conventional welding and emerges as an effective solution to address these challenges. By tailoring the spatial energy distribution, beam shaping improves control of heat input, stabilizes melt pool dynamics, and enhances microstructural uniformity. Static beam shaping, compatible with cost-effective near-infrared continuous-wave laser systems, is already being adopted in industry, whereas dynamic beam shaping remains at an earlier stage of industrial maturity. This review highlights key welding challenges in EV battery manufacturing, evaluates beam shaping strategies as practical solutions, and identifies future research directions for large-scale industrial implementation. Full article

Accurate paper fiber identification is essential for cultural heritage conservation. Traditional staining methods are destructive, while macroscopic AI models often lack physicochemical interpretability. This study explores the feasibility of a non-destructive analytical approach using micro-hyperspectral imaging (Micro-HSI) to overcome both limitations. Three traditional Japanese bast fibers, Kozo, Mitsumata, and Gampi, were analyzed as standard reference samples. Relative reflectance spectra were extracted from microscopic fiber regions using Micro-HSI. Dynamic normalization and Savitzky–Golay first-derivative filtering were applied to suppress scattering effects and baseline drift. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied in parallel for dimensionality reduction and supervised classification, respectively. The results indicated that unsupervised PCA exhibited substantial inter-class overlap because of the shared cellulose matrix among the fiber types. In contrast, supervised LDA amplified subtle chemical differences and achieved clear separation among the three fibers. Feature-loading analysis indicated that the classification was mainly associated with visible range reflectance characteristics, lignin π→π* absorption bands in the 400–450 nm region, and near-infrared O−H and C−H overtone vibrations near 835 nm. Leave-One-Specimen-Out Cross-Validation yielded an overall accuracy of 77.8%, with error-free classification of Kozo (F1 = 1.00) and misclassification limited to the chemically similar Gampi and Mitsumata pair. This proof-of-concept study demonstrates that combining Micro-HSI with chemometric analysis enables non-destructive fiber discrimination while retaining physicochemically interpretable spectral features. The findings also establish a microscopic spectral reference framework for future non-destructive analysis of historical paper materials. Full article

In all-weather Earth observation and complex unstructured environments, traditional single-modal remote sensing object detection often fails due to low illumination and strong background interference. While RGB–infrared fusion provides complementary information, existing methods are typically computationally intensive and struggle with dense small objects and modality discrepancies, limiting their deployment on resource-constrained platforms. To address these challenges, we propose ISDG-Net, a lightweight and efficient visible-infrared dual-modal object detection framework specifically tailored for edge deployment. ISDG-Net integrates four core components: (1) a channel-separated inverted bottleneck backbone (IBC-Conv) that reduces parameter redundancy while preserving modality-specific semantics; (2) a dynamic sparse attention module (DySparse) based on Bi-Level Routing Attention, enabling long-range dependency modeling with low computational cost; (3) an adaptive spatial fusion detection head (Detect-SASD) that aligns visible and infrared features at the pixel level to resolve semantic inconsistency and scale mismatch; and (4) a geometry-aware IoU selector (GIS) that mitigates over-suppression in crowded scenes by incorporating multi-dimensional geometric constraints into post-processing. Extensive experiments on the VEDAI, M3FD, and LLVIP datasets demonstrate the effectiveness and efficiency of ISDG-Net. It achieves 55.1% and 77.1% mAP@0.5 on VEDAI and M3FD, respectively, and 93.7% mAP@0.5 with 89.7% recall on LLVIP, while maintaining a compact model size of 4.2 M parameters and 11.3 GFLOPs. These results validate that accurate RGB–infrared detection is achievable under strict resource constraints, making ISDG-Net well-suited for deployment in edge-based remote sensing systems. Full article

Personal thermal management (PTM) requires materials that can adapt to dynamically changing environments, yet most existing systems are limited to single-mode cooling or heating and lack tunable optical appearance. Here, we report a structurally colored photonic Janus film that enables switchable radiative cooling and solar heating within a single material platform. The cooling side is based on cellulose nanocrystal (CNC) photonic structures, providing high mid-infrared emissivity (up to 0.91) while enabling tunable structural color through pitch modulation, thereby achieving radiative cooling without sacrificing visual appearance. The heating side consists of a carbon nanotube (CNT) layer with high solar absorptivity (~92%), enabling efficient photothermal conversion, further enhanced by low-voltage Joule heating. By simply flipping the film, reversible switching between cooling and heating modes is achieved, allowing adaptation to varying thermal conditions. In addition, the system is compatible with continuous fabrication, enabling scalable production. This work demonstrates a dual-mode photonic thermal management system that integrates optical tunability with switchable thermal regulation, providing a viable pathway toward wearable microclimate control. Full article

Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems. Full article