Search Results (419)

The study of degradation processes is of fundamental and applied interest. To understand the degradation of cellulose nitrate and to develop sensitive diagnostic methods, combined experimental and theoretical investigations are essential. Sensitive, non-destructive, and contactless methods for diagnosing the state of cellulose nitrate include IR absorption and Raman spectroscopy. While significant experimental work exists, the theoretical modeling of degradation processes, including the prediction of potential products, remains underdeveloped. Therefore, in this work, the structures and vibrational properties of molecular clusters representing segments of the cellulose nitrate chain were modeled using density functional theory (DFT). This approach yielded simulated IR and Raman spectra, allowing for the identification of peaks corresponding to the nitrate group. The elimination of this group to form a ketone was shown to alter peak contours across a broad spectral range. The most significant changes in the Raman spectra were observed at 605 and 851 cm⁻¹. Correspondingly, the key changes in the IR absorption spectra occurred at 836, 1034, 1169, 1283, and 1767–1781 cm⁻¹. The frequency trends for these diagnostic peaks across different model structures were analyzed and compared with experimental spectra from the literature. The demonstrated correlation between specific peak-frequency changes and the modeled degradation products constitutes the principal novelty of this work. Full article

Complex surfaces exhibit hierarchical morphological organizations that cannot be fully described by conventional roughness parameters alone. In this study, a fractal–statistical framework is proposed to elucidate the substrate-controlled morphological evolution of 2,6-diphenyl anthracene (DPA) thin films deposited on chemically modified dielectric substrates, including hexamethyldisilazane (HMDS), octyltrimethoxysilane (OTMS), octadecyltrichlorosilane (OTS), and bare silicon dioxide (SiO₂). A multidimensional morphological descriptor vector (M_DPA) is introduced by integrating ISO 25178 areal surface parameters (H_ISO), fractal dimension (D_f), texture direction parameters (T_d), power spectral density (PSD), and scale-sensitive fractal analysis (SSFA) descriptors to quantify amplitude-based, spatial-frequency, and scale-dependent morphological information. Atomic force microscopy (AFM) topographies of 5 nm and 50 nm thick films were analyzed using complementary approaches, including ISO 25178 areal surface parameters, texture direction analysis, peak statistics, morphological envelope fractal analysis, two-dimensional Fourier analysis, power spectral density (PSD), and scale-sensitive fractal analysis (SSFA). The results demonstrate that substrate chemistry governs not only the amplitude of surface roughness but also the lateral organization, spatial frequency distribution, and scale-dependent fractal complexity of DPA morphologies. The fractal dimension analysis revealed substrate-dependent variations in surface complexity, with values ranging from 2.11 to 2.45 for 5 nm films and from 2.19 to 2.52 for 50 nm films. PSD analysis identified distinct substrate-induced modifications in spectral organization, while SSFA revealed significant changes in smooth–rough crossover scales, maximum complexity scales, and fractal surface complexity during film growth. In particular, OTMS promoted the strongest hierarchical organization for thicker films, exhibiting the highest scale-sensitive fractal complexity, whereas OTS generated highly developed but less hierarchically correlated rough structures. The integrated fractal–spectral methodology establishes quantitative relationships between substrate functionalization and multiscale surface evolution, demonstrating that morphological complexity cannot be described solely by conventional height parameters. This framework provides a robust approach for characterizing hierarchical thin-film architectures and can be extended to other organic semiconductor systems where substrate-driven morphological control is critical. Full article

Background/Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) is the preferred imaging modality for evaluating pulmonary nodules because of its high spatial resolution; however, it primarily provides morphological information, including lesion size, shape, and density. Dual-energy CT (DECT), a more recent imaging technique, uses two different energy levels to enable material decomposition and quantitative parameter assessment. These parameters may provide additional information regarding tumor perfusion, vascularization, and tissue composition. This rapid review aimed to evaluate the current evidence regarding the clinical utility of DECT in the detection, characterization, and staging of lung tumors. Methods: This rapid review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed in the PubMed and Cochrane Library databases for studies published between 2005 and 2026. Studies were included if they evaluated the detection, characterization, or staging of lung tumors using quantitative DECT parameters. Case reports, editorials, duplicate studies, and studies without quantitative DECT data were excluded. Descriptive data analysis was performed using Microsoft Excel. Results: A total of 24 studies were included, comprising 18 retrospective (75%) and 6 prospective studies (25%). Only one study evaluated the role of DECT in lung tumor detection, demonstrating improved detection of mixed ground-glass nodules and invasive adenocarcinoma. Significant correlations were found between iodine uptake and tumor perfusion, highlighting the potential of DECT to improve differentiation between benign and malignant lesions. Several studies also demonstrated associations between DECT parameters and tumor biomarkers, including Ki-67 Proliferation Index (Ki-67) expression, Epidermal Growth Factor Receptor (EGFR) mutation status, Programmed Death-Ligand 1 (PD-L1) expression, and treatment response in non-small cell lung cancer. In addition, DECT provided complementary metabolic information regarding tumor malignancy and showed correlations between iodine uptake and fluorodeoxyglucose (FDG) parameters. Associations between iodine volume and tumor differentiation grade were also reported. One study demonstrated the potential role of DECT in tumor staging by predicting mediastinal lymph node metastasis. Across all included studies, iodine-based parameters (50%), radiomics and material decomposition parameters (16.67% each), and spectral attenuation parameters (12.50%) were the most frequently investigated DECT metrics. Conclusions: DECT appears to be a promising complementary imaging technique that provides quantitative perfusion-related and compositional surrogate information beyond the morphological assessment offered by conventional CT. However, the current evidence remains heterogeneous and is largely based on retrospective studies with relatively small patient cohorts. Larger prospective studies with standardized imaging protocols are necessary to further establish the clinical utility of DECT in lung tumors. Full article

Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50–70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50–70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article

We report on electric field measurements recorded onboard the China Seismo-Electromagnetic Satellite (CSES). In this study, we emphasize the whistler and transmitter very low frequency (VLF) radiations recorded in the frequency range between 1.8 kHz and 25 kHz. The electric field detector (EFD) instrument onboard the CSES works as a double probe instrument and allows access to the three electric components of VLF waves. Three frequencies were selected, two related to whistler hiss (i.e., 2.5 kHz channel) and chorus (i.e., 5 kHz channel) radiations and one to the NAA ground-based transmitter signal (i.e., 24 kHz). We investigate the corresponding power spectral density variations, from which we derive the Stokes intensity I and normalized q linear polarization components. This leads us to study their statistical fluctuations and to emphasize the behaviors of natural whistler hiss and chorus radiations and man-made transmitter emissions. The Stokes intensities of the natural whistler and NAA transmitter radiations are estimated, respectively, to be about 1 mV² m⁻² Hz⁻¹ and 0.05 mV² m⁻² Hz⁻¹. The correlation coefficients of the Stokes intensity polarizations are in the order of 98% powerfully coupled, contrary to the Stokes normalized linear polarizations, which are found to be relatively paired, i.e., less than 60%. The signal-to-noise ratio is estimated considering three intensity levels (i.e., high, medium, low). This analysis leads us to characterize the Stokes normalized linear components of VLF radio waves and to show different behaviors of the polarization when considering the Northern and Southern Hemispheres. The regions of enhanced whistler Stokes intensities of whistler hiss and chorus emissions are confined to the sub-auroral regions in both hemispheres, particularly at geomagnetically ranges linked to the NAA transmitter station and its conjugate region in the Southern Hemisphere. In this investigation, we point out the Stokes polarization parameters, which are essential for the characterization of whistler VLF radio waves, particularly when considering the CSES mission objectives and commitments. Full article

Accurate estimation of canopy water content (OWC) is essential for precision irrigation, crop growth monitoring, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral remote sensing provides a rapid and non-destructive approach for monitoring crop water status; however, the selection of effective spectral features and appropriate machine learning algorithms for robust OWC estimation remains insufficiently investigated, particularly across multiple growing seasons. This study evaluated the potential of UAV multispectral imagery for estimating oilseed rape canopy water content using two feature selection strategies and four representative machine learning algorithms. Field experiments were conducted during two consecutive growing seasons (2023–2024 and 2024–2025). Different sowing dates, nitrogen application rates, and planting densities were used to create a broad range of canopy water conditions. UAV multispectral images were acquired at ten representative growth stages during the reproductive period, from stem elongation to physiological maturity. Fourteen vegetation indices (VIs) were extracted from the multispectral imagery. Pearson correlation analysis and principal component analysis (PCA) were used to select informative features. These features were then used to develop multiple linear regression (MLR), partial least squares (PLS), support vector machine (SVM), and random forest (RF) models. Model performance was evaluated using each single-year dataset and the combined two-year dataset to assess robustness under different seasonal conditions. The RF model consistently achieved the highest prediction accuracy. The correlation-based RF model developed from the combined two-year dataset produced the best performance. It achieved an R² of 0.966, an RMSE of 1.734%, and an RRMSE of 2.360% for the training dataset. For the independent testing dataset, the corresponding values were 0.901, 2.794%, and 3.830%, respectively. The PCA-based models showed similar performance and effectively reduced feature redundancy. However, they did not consistently outperform the correlation-based models. These results indicate that combining UAV multispectral imagery with appropriate feature selection and machine learning algorithms can accurately estimate oilseed rape canopy water content under field conditions. Integrating data from multiple growing seasons further improves model robustness and provides a practical basis for UAV-assisted crop water monitoring and precision agricultural management. Full article

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article

Finite-dimensional density matrices contain two representation-intrinsic sectors after the real part is diagonalized, namely ordered intrinsic populations and antisymmetric imaginary coherences. This article develops exact complementarity identities showing how these sectors determine purity, spectral concentration, and entropy. Populations are described by indices of population asymmetry, while coherences are described by the Youla spectrum of the dimensionless metaspin tensor and by correlation-asymmetry indices. In the aligned class, where Youla two-planes coincide with pairs of intrinsic axes, normalized purity splits into a population hierarchy and pairwise coherence terms weighted by products of intrinsic populations. For arbitrary orientations, the coherence term is expressed as a positive semi-definite bilinear form in population-weighted Plücker coordinates. For fixed populations and pairing, increasing any Youla value sharpens the spectrum by majorization and decreases all Rényi entropies, including the von Neumann limit. For fixed ordered populations, maximum aligned cohesion is obtained by saturating adjacent population pairs. The dimensional transition of the discriminating-component cohesion bound is then interpreted as the change from one to two simultaneously saturating metaspin pairs. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

Background/Objectives: Alzheimer’s disease (AD) is associated with progressive hippocampal circuit dysfunction, but electrophysiological measures of state-dependent abnormalities and treatment responsiveness remain incompletely characterized. We examined hippocampal theta–gamma coupling in 5xFAD mice across behavioral states and after donepezil. Methods: Local field potentials were recorded from CA1 in freely behaving wild-type (WT) and 5xFAD mice during home-cage activity, open-field exploration, and Y-maze testing. Power spectral density and theta–gamma phase–amplitude coupling (PAC) were quantified at the animal level at baseline and after seven days of donepezil. Results: Untreated 5xFAD mice showed reduced theta–low-gamma coupling during home-cage and open-field activity, but not Y-maze exploration, and elevated theta–high-gamma coupling in all three contexts. Donepezil increased theta–low-gamma coupling during open-field and Y-maze exploration and produced a partial numerical shift toward WT levels in the home cage. For theta–high-gamma coupling, the treated group did not differ significantly from either comparator. Untreated 5xFAD mice also showed reduced center exploration and distance traveled in the open field, while Y-maze spontaneous alternation was unchanged. Theta–high-gamma coupling correlated positively with open-field center time in WT mice only. Conclusions: Hippocampal theta–gamma coupling shows frequency- and context-dependent abnormalities in 5xFAD mice. Theta–low-gamma coupling is sensitive to short-term cholinergic modulation during exploration, whereas donepezil’s effect on theta–high-gamma coupling remains inconclusive. Animal-level PAC may provide a candidate functional readout, but longitudinal and cross-model validation is required before biomarker claims are justified. Full article

This publication presents the results of studies on exhaust emissions and fuel consumption from spark-ignition engines in the NEDC test, which applies to the vast majority of passenger cars in use in the European Union. This emissions testing procedure was selected based on an analysis of the number of existing engines meeting specific emission standards. A statistical analysis of the studied processes was conducted. Additionally, the product of speed and acceleration modulus was examined as quantities characterizing the dynamic characteristics of the vehicle speed process during testing. Correlation studies of vehicle speed and exhaust emission rates, particle number rates, and mass fuel consumption rates were presented. Average distance-specific emissions, average distance-specific particulate number, and average distance-specific fuel consumption were determined in the NEDC test. The probability density distributions of the investigated processes in the NEDC test were analyzed. Based on an assessment of the conformity of the studied sets with the normal distribution using the Kolmogorov–Smirnov, Lilliefors, and Shapiro–Wilk hypotheses, it was concluded that there was no basis for accepting the hypotheses that the sets conform to the normal distribution. Moreover, the power spectral density of the investigated processes was evaluated. Significant variation in spectral characteristics, especially at high frequencies, was identified, revealing substantial dynamic differences between the processes. The average road emission values in the NEDC test were significantly lower than even the limits for Euro 7. Full article

Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. However, existing research often overlooks parameters reflecting crop population structure and fails to account for dynamic shifts in the contributions of multidimensional agronomic variables across growth stages, thereby limiting prediction accuracy and model stability. To address these limitations, a winter wheat yield estimation model was developed. This model integrates multi-source and multi-temporal data, incorporates stem tiller density, a key population structure parameter, and accounts for dynamic variation across growth stages. Unmanned aerial vehicle multi-spectral images were collected at four key growth stages: jointing (stem elongation with detectable nodes), booting (flag leaf sheath swelling preceding heading), heading (spike emergence) and filling (grain filling with dry matter accumulation). Three growth indicators, stem tiller density, leaf area index and above-ground biomass, were measured. Two comprehensive growth indicators were derived using the coefficient of variation and the CRITIC weighting methods, respectively (CGI_CV and CGI_CR). Correlation and feature importance analyses were used to identify sensitive vegetation indices (VIs), which were subsequently integrated with the comprehensive growth indicators. Single-stage, multi-source feature fusion and multi-temporal yield estimation models were established using the Kernel Extreme Learning Machine and its optimised algorithm using the Crested Porcupine Optimizer. The results showed the following: (1) among the individual growth stages, features from the filling stage achieved the highest prediction accuracy; (2) the fusion of multi-source features (VIs + CGI_CR) enhanced the prediction accuracy of the model, achieving a validation set R² of 0.884 and a relative prediction deviation of 2.916 at the filling stage; and (3) the multi-temporal model further improved predictive performance, with the validation R² reaching 0.920, indicating that information from different growth stages contributed complementarily to yield prediction and improved overall model performance. By contrast, the model exhibited relatively weak predictive capability at the early growth stages and was better-suited to early risk identification. Meanwhile, its generalisation ability under cross-regional and inter-annual conditions still requires further validation. Overall, integrating multi-source and multi-temporal data can enhance the precision and stability of predicting winter wheat yield, thereby facilitating precision agriculture management. Full article

The accurate and nondestructive quantification of subgrade moisture content is a core demand for highway construction quality control and long-term performance maintenance. In order to study the response relationship between subgrade moisture content and ground-penetrating radar (GPR) signal eigenvalues, this study constructs the volumetric moisture content–dielectric constant relationship of Guangxi high-plasticity clay and carries out gprMax forward numerical simulations. Fourteen radar signal eigenvalues are extracted from preprocessed signals via time-domain waveform analysis, Hilbert transform analysis, and power spectral density analysis. Seven key eigenvalues are screened out through Pearson correlation coefficient-based dimensionality reduction. Three machine learning algorithms—artificial neural network (ANN), random forest (RF), and light gradient boosting machine (LightGBM)—are adopted to optimize the subgrade moisture-content inversion model, which is finally validated through indoor model box tests and field subgrade tests. The results show that: (1) The linear fitting formula is the most suitable for describing the volumetric moisture content–dielectric constant relationship of Guangxi clay, with a coefficient of determination (R²) of 0.979 and a mean absolute error (MAE) of 0.31. (2) The feature matrix after dimensionality reduction effectively alleviates the degradation of model generalization ability and interpretability. (3) The LightGBM model achieves the highest prediction accuracy for clay volumetric moisture content, with an R² of 0.99926 and an MAE of 0.172%. (4) For gravimetric moisture-content inversion, the maximum relative error is 1.6% in the indoor model box test and 1.7% in the field test, both within the 2% tolerance of engineering requirements. This study verifies the feasibility of the proposed subgrade moisture-content identification method based on GPR signal eigenvalues. The proposed method provides an efficient technical path for the large-area and nondestructive detection of subgrade moisture and has promising application prospects in subgrade construction quality acceptance, daily maintenance monitoring and hidden disease early warning. Full article

Pulsar timing arrays have detected a nanohertz signal exhibiting the Hellings–Downs angular correlation, the expected angular symmetry of an isotropic stochastic gravitational-wave background. This angular symmetry fixes the tensor correlation class of the signal, so its physical origin must be inferred from the frequency spectrum. Using the public NANOGrav 15-year free-spectrum products, we compare five spectral hypotheses through Bayesian evidence: the canonical scale-free power law from purely gravitational-wave-driven supermassive black hole binaries (SMBHBs), a free-slope power law, an environmental SMBHB turnover model, a first-order phase transition template, and an effective cosmic string spectrum. The evidence favors spectra with physical curvature or a characteristic scale, while the strict scale-free SMBHB law is strongly disfavored. Within the tested physical templates, the phase transition model gives the largest compressed spectral evidence; within astrophysical source models, environmental SMBHB hardening is the leading interpretation and links the spectral bend to parsec-scale nuclear stellar densities. The effective cosmic string spectrum shows little evidence gain under the baseline prior. An orbit- and foreground-aware LISA continuation forecast gives this ranking a multi-band check: the PTA-selected QCD-scale phase transition posterior has no appreciable millihertz continuation, whereas an unchanged broad cosmic string extrapolation is LISA-bright and needs additional spectral structure. Full article