Search Results (120)

Al₂O₃:Cr³⁺ coatings were synthesized on tungsten substrates by plasma electrolytic oxidation in a phosphate-aluminate electrolyte containing dispersed Cr₂O₃ nanoparticles, and their structural, photoluminescent, and temperature-sensing properties were investigated. The coatings exhibited a typical porous PEO morphology with a uniform thickness of approximately 31 μm, and EDS analysis confirmed the incorporation of Cr species from the electrolyte, with Cr content increasing with the concentration of Cr₂O₃ particles. XRD analysis showed that the coatings were composed predominantly of α-Al₂O₃, with minor contributions from metastable γ-Al₂O₃, confirming that our previously established process for forming the thermodynamically stable α-Al₂O₃ phase directly on a non-aluminum substrate remains robust upon the introduction of dopant nanoparticles. The Al₂O₃:Cr³⁺ coatings displayed characteristic ruby-like photoluminescence, including broad excitation bands associated with the ⁴A₂⟶⁴T₁ and ⁴A₂⟶⁴T₂ transitions and sharp R-line emission arising from the spin-forbidden ²E⟶⁴A₂ transition. The strongest emission was obtained for coatings prepared with 0.05 g/L Cr₂O₃, while higher concentrations resulted in concentration quenching. Temperature-dependent photoluminescence revealed two complementary thermometric mechanisms: R-line spectral shifting and thermally induced redistribution between the ²E and ⁴T₂ emissions. The deconvolution-based intensity-ratio approach provided a stronger temperature response than simple spectral partitioning, demonstrating the potential of PEO-derived Al₂O₃:Cr³⁺ coatings on tungsten as robust luminescent temperature-sensing layers. Full article

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up. Full article

The accuracy of shallow-water multibeam bathymetry is critically dependent on precise heave correction. However, sensor limitations often lead to incomplete correction, leaving periodic along-track stripe noises (heave residuals) that distort seabed morphology. Traditional filtering methods suppress this noise at the expense of genuine topographic detail. This paper proposes an innovative Data Feedback Correction (DFC) method that corrects the error at its source. DFC establishes a closed-loop framework: it diagnoses the residual’s dominant frequency from central beam data, extracts the residual signal via targeted filtering, and feeds it back as a compensation term into the original sensor heave sequence. This drives a recomputation of the geometric positioning, achieving source-level correction. In a field case, DFC demonstrated targeted, high-fidelity performance. Across all 34 survey lines, DFC achieved an average spectral attenuation of 1.85 dB (range: 1.0–3.7 dB) in the dominant residual band and reduced the RMSE of overlap discrepancies from 0.0923 m to 0.0773 m (a 16.25% improvement). Independent validation using 94,999 control line intersections further demonstrates a 14.31% RMSE improvement relative to an uncorrected control line reference, confirming that the correction improves both internal consistency and external accuracy, significantly enhancing internal consistency. Compared to moving average and wavelet denoising, DFC achieved comparable quantitative improvement while effectively suppressing visual stripes and features that are consistent with the original data, avoiding the over-smoothing or residual noise of traditional methods. This study confirms that closed-loop feedback of data residuals can fundamentally address spectrally aliased stripe noise, shifting the paradigm from “masking noise” to “correcting the source.” The method enhances data consistency in the tested scenario without sacrificing topographic authenticity, providing a promising new tool that warrants further validation across diverse survey conditions. Full article

Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work. Full article

Transmission tower-line systems spanning active faults are simultaneously subjected to the “dual characteristic seismic actions” of permanent ground displacement (PGD) and spatially varying near-fault ground motions, rendering their failure mechanisms far more complex than those under conventional site-specific seismic actions. This paper investigates a 500 kV double-circuit “two-tower, three-line” coupled system by establishing a high-fidelity finite element model. An analytical framework is proposed, centered on indexing seismic action and structural response by key parameters: “Permanent Ground Displacement–Peak Differential Displacement–Velocity Pulse Period” (“PGD–Δmax–Tp”). By employing synthesized ground motions, the displacement time history is decomposed into three components—a velocity pulse, high-frequency background noise, and permanent displacement—thereby achieving a strict decoupling of these three control variables. Based on this methodology, three sets of controlled-variable scenarios were constructed to systematically reveal the independent influence of ground motion spectral characteristics, permanent displacement, and peak differential displacement on the system’s response. The research indicates that: spectral characteristics modulate the failure mode (the whiplash effect is triggered when the period ratio μ is approximately 1–2, whereas tower leg buckling occurs when μ ≫ 1); a threshold PGD value exists that triggers a shift in the structural force-resisting mechanism; and the peak differential displacement (Δmax) causes the system’s response to transition from being dominated by conductor slackening and unloading to being governed by inertia and P-Δ effects. The insights gained into the asymmetric response characteristics of towers on opposite sides of the fault provide a quantitative reference for the revision of seismic design codes for cross-fault power transmission projects. Full article

A micro-confocal Raman spectrometer is developed using four tiled sub-gratings, each with a groove density of 1200 lines/mm, and a single optimized cylindrical lens. The system achieves a broad Raman shift coverage from 99.8 to 4701 cm⁻¹ with a spectral resolution of approximately 4 cm⁻¹, and operates without any mechanical moving parts. The cylindrical lens corrects multi-channel astigmatism, compressing the spot size to one quarter of its original dimension and improving the signal-to-noise ratio by a factor of four. The prototype successfully detects Raman signals from silicon, sodium nitrite, cyclohexane, and sodium sulfate solutions with concentrations as low as 0.01 mol/L. It also resolves inorganic mixtures, minerals including celestine, rhodonite, and calcite, as well as microplastics such as polyamide, polytetrafluoroethylene, polyethylene, and styrene-ethylene-butylene-styrene block copolymer. These results demonstrate that the spectrometer offers reliable qualitative and quantitative detection capabilities with both broad spectral bandwidth and high resolution. Full article

Background/Objectives: Color vision deficiency (CVD) arises from the absence or dysfunction of one or more cone photoreceptors in the retina, resulting in impaired color discrimination. Although inherited CVD cannot be cured, optical compensation strategies such as color-filtering glasses have been developed to enhance color perception. However, quantitative clinical evaluations of their corrective efficacy remain limited. This study aimed to assess the effectiveness of notch filter-based color blind glasses in improving color perception and discrimination in individuals with CVD. Methods: Notch filters were employed as color correction lenses, and clinical assessments were conducted to evaluate their impact on human color perception. Subjects underwent standardized color vision tests, including the Color Bridge test, Farnsworth-Munsell 100 Hue test, and D-15 panel test, both before and after wearing the glasses. Outcomes were quantitatively analyzed using total error score (TES), confusion angle, and confusion index (C-index) to determine changes in color discrimination performance. Results: Quantitative analysis demonstrated that wearing the notch filter glasses amplified color differences along confusion lines. In clinical trials, 83% of subjects showed improved color discrimination in the F-M 100 Hue test, with TES reductions between 6.67% and 50.00%. Furthermore, D-15 panel testing revealed that 67% of participants exhibited a decreased C-index and reduced scatter index (S-index), with specific cases shifting from deficient to normal color perception (C-index < 1.6). These results indicate that the filters effectively mitigate symptoms of color vision deficiency by increasing perceptual contrast. Conclusions: Notch filter-based color correction glasses can enhance chromatic discrimination in individuals with CVD by increasing perceptual color contrast. These findings provide practical insights for the optimization and fabrication of color vision correction eyewear utilizing spectral notch filtering strategies. Full article

Accurate permittivity characterization at terahertz frequencies is important for material analysis and device design, yet it remains challenging for small-volume samples and compact test structures. In this work, a terahertz permittivity sensor based on a spoof surface plasmon polariton (SSPPs) transmission line coupled to a backside split-ring resonator (SRR) is proposed and numerically studied. The SSPPs line is patterned on the top side of the substrate, while the SRR is etched on the backside, with the sample loaded into the SRR gap. The SSPPs mode penetrates through the substrate and excites the SRR, producing a pronounced transmission notch. Changes in the sample permittivity modulate the effective capacitance of the resonator, resulting in a monotonic shift in the notch center frequency. For relative permittivities from 1 to 8, the notch center frequency decreases from 152.1 GHz to 117.8 GHz, corresponding to a total shift of 34.3 GHz and an average sensitivity of about 4.90 GHz/ε_r. The minimum S₂₁ remains within approximately −23.80 to −21.56 dB, while the Q-factor stays in the range of 94.33–108.23, indicating good spectral readability. Tolerance analysis further shows that the resonance frequency is sensitive to critical structural dimensions and layer alignment, and practical implementation is therefore more suitable for single-device calibrated frequency-shift sensing. These results demonstrate the feasibility of the proposed dual-layer SSPPs–SRR configuration for compact permittivity sensing in the terahertz regime. Full article

A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method’s robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. Full article

Calcium phosphate–poly(methyl-methacrylate) composite layers have been synthetized on silicon substrates in magnetron sputtering discharge by adjusting the radio-frequency power. The electron energy distribution function measured at holder substrate position shifts to lower energies when the radio-frequency power applied to the magnetron source increases from 50 to 150 W and the poly(methyl-methacrylate) molecule dissociation is augmented. The optical emission spectral analysis indicated the dynamics of the excitation and ionization processes in the Ar–calcium phosphate–poly(methyl-methacrylate) plasma mixture, as well as the dissociation patterning of the polymer molecules. The Ca I, P I, and H_α atomic lines and CaO, PO, POH, CO, CH and C₂ molecular bands characteristic to the calcium phosphate and poly(methyl-methacrylate) decomposition were evidenced. At 150 W radio-frequency power a reduction in the polymer content in the composite layer volume was observed even if the α-CH₃ main chain and the C=O molecular bands are still present. More C-C/C-H, C-OH/C-O-C polymeric bonds were revealed at the layer surface, indicating the formation of plasma polymers. The Ca/P ratio changes from 1.72 to 1.9 at 50 to 150 W, respectively, maintaining the amorphous structure of the layers. In this power range, the transition of layer surface morphologies from grain-like to worm-like plasma polymer characteristics is connected to an increase in plasma ion density and layer thickness. Full article

Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring. Full article

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems. Full article

We analyze the time-dependent physical spectrum of a driven Jaynes–Cummings model in which both the two-level system and the quantized cavity mode are subject to coherent classical driving. The time-dependent Hamiltonian is mapped, via well-defined unitary transformations, onto an effective stationary Jaynes–Cummings form. Within this framework, we derive closed-form expressions for the two-time correlation functions of both the atomic and field operators. These correlation functions are subsequently used to evaluate the time-dependent physical spectrum according to the Eberly–Wódkiewicz definition, which properly accounts for finite spectral resolution and transient emission dynamics. We show that the external driving leads to substantial modifications of the atomic spectral response, including controllable frequency shifts and asymmetric line shapes. Importantly, we identify a regime in which the driving parameters are chosen such that the coherent displacement induced in the cavity field exactly cancels out the initial coherent amplitude. In this limit, the system dynamics reduce to that of an effectively vacuum-initialized Jaynes–Cummings model, and the standard vacuum Rabi splitting is recovered. This behavior provides a clear and physically transparent interpretation of the spectral features as arising from coherent field displacement rather than from modifications of the underlying atom–cavity coupling. Full article

A novel series of benzoxazole-derived iminocoumarins was synthesized via a Knoevenagel condensation and fully characterized using NMR, UV–Vis spectroscopy, and computational methods. Their photophysical properties were systematically examined in solvents of varying polarity, revealing pronounced effects of both substituents and solvent environment on absorption maxima and intensity. Derivatives bearing electron-donating substituents on the coumarin core exhibited distinct and reversible pH-responsive spectral shifts, confirming their potential as optical pH probes. Experimental pK_a values derived from absorption titrations showed excellent agreement with DFT-calculated data, validating the proposed protonation-deprotonation equilibria and associated electronic structure changes. Structure–property relationships revealed that electron-donating groups enhance intramolecular charge transfer, while electron-withdrawing substituents modulate spectral response and stability. In parallel, the compounds were evaluated for antiproliferative, antiviral, and antifungal activities in vitro. Strong electron-donating substituents were associated with potent but non-selective cytotoxicity, whereas derivatives bearing electron-withdrawing groups displayed moderate and more selective antiproliferative effects against leukemia cell lines. Antifungal screening revealed moderate inhibition of phytopathogenic fungi, particularly for compounds with electron-withdrawing or methoxy substituents. Overall, these findings demonstrate that benzoxazole iminocoumarins represent a promising class of multifunctional heterocycles with potential applications as optical pH sensors and scaffolds for bioactive compound development. Full article

This review summarizes the use of Auger Electron Spectroscopy (AES) for microchemical analysis of two different types of dielectric/(Al,Ga)N-based systems: (i) extrinsic dielectric PECVD SiO₂, ALD Al₂O₃, and ECR-CVD SiN_x films on Al_xGa_1−xN/GaN structures in the context of their application in microelectronic power devices and (ii) intrinsic Al₂O₃ films on AlN epitaxial layers grown by high-temperature oxidation for nanostructured technology of various gas/ion sensors. Particular attention is given to AES depth profiling across complete multilayer cross-sections, combining qualitative analysis of spectral line shape and intensity evolution as well as kinetic energy shifts with quantitative elemental depth distributions. This approach enables identification of chemical states and oxidation-related transformations at dielectric/semiconductor interfaces. Reported results demonstrate that AES provides micro- to nanometer-scale chemical information essential for distinguishing interfacial from the bulk properties. The capabilities and inherent limitations of AES depth profiling, including sputter-induced artifacts are also addressed, highlighting the role of optimized experimental conditions in reliable interface analysis. Full article