Spectral Model Research Articles

Dual Spectral Attention Model for Iris Presentation Attack Detection

Dual Spectral Attention Model for Iris Presentation Attack Detection

A UK MW catalogue derived from coda envelopes

Summary The United Kingdom (UK) experiences low-to-moderate levels of seismicity; only 12 onshore earthquakes with local magnitude (ML) ≥ 4.0 have been recorded in the past 20 years. It is therefore difficult to estimate moment magnitude (MW) using conventional moment tensor inversion for the majority of UK seismicity, resulting in limited reliable estimates of MW. To address this, we calibrated coda envelopes at 16 broadband seismic stations distributed across the UK, to produce a MW catalogue for 100 events with MW≥2.13 that occurred since 2006. This was achieved using the open-source Coda Calibration Tool, which requires independent source parameter estimates for calibration. For 13 UK events between 2006 and 2022, we used spectral modelling to estimate apparent stress (0.32 to 1.74 MPa), and moment tensor inversion to estimate MW (3.35 to 4.52). These independent source parameters formed a subset of the inputs into the final calibration, which used seismic data from 33 events with coda-derived values of 2.57$\le $Mw$\le $4.49. The resultant coda calibration parameters were applied to 67 further events (MW≥2.13). The coda envelopes exhibit slow seismic coda decay across the UK, with significant energy up to 20 Hz, consistent with other regions of low tectonic activity. This MW catalogue, and the application of the calibration to future UK seismic events, will be useful for both assessing seismic hazard and event characterisation.

Spectroscopic Constraints on the Mid-infrared Attenuation Curve. I. Attenuation Model Using Polycyclic Aromatic Hydrocarbon Emission

We introduce a novel model to spectroscopically constrain the mid-infrared (MIR) extinction/attenuation curve from 3–17 μm, using polycyclic aromatic hydrocarbon (PAH) emission drawn from an AKARI–Spitzer extragalactic cross-archival data set. Currently proposed MIR extinction curves vary significantly in their slopes toward the near-infrared, and the variation in the strengths and shapes of the 9.7 μm and 18 μm silicate absorption features make MIR spectral modeling and interpretation challenging, particularly for heavily obscured galaxies. By adopting the basic premise that PAH bands have relatively consistent intrinsic ratios within dusty starbursting galaxies, we can, for the first time, empirically determine the overall shape of the MIR attenuation curve by measuring the differential attenuation at specific PAH wavelengths. Our attenuation model shows PAH emission in most (ultra)luminous infrared galaxies is unambiguously subjected to attenuation, and we find strong evidence that PAH bands undergo differential attenuation as obscuration increases. Compared to preexisting results, the MIR attenuation curve derived from the model favors relatively gray continuum absorption from 3–8 μm and silicate features with intermediate strength at 9.7 μm but with stronger than typical 18 μm opacity.

Spectral and timing analysis of NGC 7314

The analysis of X rays observations from the active galactic nuclei is a good tool to study the properties of the supermassive black hole, the physics of the accretion disk and the corona, and to model their geometries. We present a detailed spectral and timing analysis of the Seyfert galaxy NGC 7314, we used all available public XMM-Newton and NuSTAR observations from 2001 to 2016. In order to improve the spectral modeling, we used both relativistic and non relativistic reflection models. We confirmed the presence of narrow and Broad Iron lines and we obtained constraints for other spectral proprieties. For the timing analysis, we first studied the behaviour of the power density spectrum, then we studied the time delays between the direct X rays emission and the reflected emission from the accretion disk, we measure reverberation in at least one observation, but we do not see it in others, suggesting it may be a transient phenomenon.

CdTe XG-Cam: A new high-resolution x-ray and gamma-ray camera for studies of the pharmacokinetics of radiopharmaceuticals in small animals.

The recent emergence of targeted radionuclide therapy has increased the demand for imagers capable of visualizing pharmacokinetics in developing radiopharmaceuticals in the preclinical phase. Some radionuclides emit hard x-rays and gamma-rays below 100 keV, in which energy range the performance of conventional NaI scintillators is poor. Multipinhole collimators are also used for small animal imaging with a good spatial resolution but have a limited field of view (FOV). In this study, a new imager with high sensitivity over a wide FOV in the low-energy band ( 100 keV) was developed for the pharmacokineticstudy. We developed an x-ray and gamma-ray camera for high-resolution spectroscopy, named "CdTe XG-Cam," equipped with a cadmium telluride semiconductor detector and a parallel-hole collimator using a metal 3D printer. To evaluate the camera-system performance, phantom measurements with single and dual nuclides ( , , and were performed. The performance for in vivo imaging was evaluated using tumor-bearing mice to which a nuclide ( or administered. We simultaneously obtained information on and , which emit emission lines in the low-energy band with peak energies close to each other (23-26 keV for and 27-31 keV for , and applied an analytical method based on spectral model fitting to determine the individual radioactivities accurately. In the small animal imaging, the distributions of the nuclide in tumors were accurately quantified and time-activity curves in tumors areobtained. The demonstrated capability of our system to perform in vivo imaging suggests that the camera can be used for applications of pharmacokineticsresearch.

Improving the Spatiotemporal Transferability of Hyperspectral Remote Sensing for Estimating Soil Organic Matter by Minimizing the Coupling Effect of Soil Physical Properties on the Spectrum: A Case Study in Northeast China

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R2 value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions.

Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction

AbstractThe present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models’ performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models.

Effects of Surface Turbulence Flux Parameterizations on the MJO: The Role of Ocean Surface Waves

Abstract This study investigates the sensitivity of the Madden–Julian oscillation (MJO) to changes to the bulk flux parameterization and the role of ocean surface waves in air–sea coupling using a fully coupled ocean–atmosphere–wave model. The atmospheric and ocean model components of the Energy Exascale Earth System Model (E3SM) are coupled to a spectral wave model, WAVEWATCH III (WW3). Two experiments with wind speed–dependent bulk algorithms (NCAR and COARE3.0a) and one experiment with wave-state-dependent flux (COR3.0a-WAV) were conducted. We modify COARE3.0a to include surface roughness calculated within WW3 and also account for the buffering effect of waves on the relative difference between air-side and ocean-side momentum flux. Differences in surface fluxes, primarily caused by discrepancies in drag coefficients, result in significant differences in MJO’s properties. While COARE3.0a has better convection–circulation coupling than NCAR, it exhibits anomalous MJO convection east of the date line. The wave-state-dependent flux (COR3.0-WAV) improves the MJO representation over the default COARE3.0 algorithm. Strong easterlies over the Pacific Ocean in COARE3.0a enhance the latent heat flux (LHFLX). This is responsible for the anomalous MJO propagation after the date line. In COR3.0a-WAV, waves reduce the anomalous easterlies, leading to a decrease in LHFLX and MJO dissipation after the date line. These findings highlight the role of surface fluxes in MJO simulation fidelity. Most importantly, we show that the proper treatment of wave-induced effects in bulk flux parameterization improves the simulation of coupled climate variability.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Assessment of reliable wave climate at jetty on open coasts: role of 2D spatio-temporal spectrum

Assessment of reliable wave climate at jetty on open coasts: role of 2D spatio-temporal spectrum

Predicting foliar nutrient concentrations across geologic materials and tree genera in the northeastern United States using spectral reflectance and PLSR models

Predicting foliar nutrient concentrations across geologic materials and tree genera in the northeastern United States using spectral reflectance and PLSR models

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Sensitivities of Spectral Optical Properties of Dust Aerosols to Their Mineralogical and Microphysical Properties

AbstractDust aerosol is a key component in global radiative forcing and climate modeling. We developed a mineralogy‐resolved spectral optical property model for dust aerosols. The model incorporates nine mineral groups and applies the effective medium methods to obtain the spectral complex refractive indices of inhomogeneous dust particles. This study considers 5,000 sets of dust mineral composition mixtures along with the use of the bimodal particle size distribution. We calculate the bulk properties of these samples in a wide spectral range from 0.2 to 50 μm using a comprehensive database of hexahedral dust single‐scattering properties. Through sensitivity analyses, this study reveals that the illite and hematite components substantially affect the optical properties of dust. Furthermore, the effective radii of both the fine‐mode and the coarse‐mode dust particles have significant impacts on the bulk optical properties, albeit to varying degrees.

A novel spectral model for prediction of nongray radiative properties in gaseous combustion

Radiative heat transfer during gaseous combustion displays very strong spectral or “nongray” behavior, which is both very difficult to characterize and to evaluate. A novel spectral model based on the full-spectrum correlated k-distribution (FSCK) method combined with the multilayer perceptron (MLP), i.e., NFM model, is developed for the prediction of nongray radiative properties in gaseous combustion. Followed by the nature of FSCK method, the NFM model is designed as a simple MLP structure that has only two outputs to directly give both correlated k-value and corresponding ka-value. This not only avoids on-the-fly calculations of a-values but also saves storage by the simple structure. Moreover, by constructing training data with a number of small mole fractions, the errors caused by nonlinear effects are found to be mitigated during the use of NFM model. Several test cases have been carried out to compare the developed NFM model and other spectral tools including FSCK look-up tables and traditional FSCK MLP (TFM) model. Results show that the NFM model can achieve excellent accuracy that is even better than FSCK look-up tables at a tiny computational cost that is far less than that of TFM model.

Global fit of electron and neutrino elastic scattering data to determine the strange quark contribution to the vector and axial form factors of the nucleon

We present a global fit of neutral-current elastic (NCE) neutrino-scattering data and parity-violating electron-scattering (PVES) data with the goal of determining the strange quark contribution to the vector and axial form factors of the proton. Previous fits of this form included data from a variety of PVES experiments (PVA4, HAPPEx, G0, SAMPLE) and the NCE neutrino and anti-neutrino data from BNL E734. These fits did not constrain the strangeness contribution to the axial form factor GAs(Q2) at low Q2 very well because there was no NCE data for Q2<0.45 GeV2. Our new fit includes for the first time MiniBooNE NCE data from both neutrino and antineutrino scattering; this experiment used a hydrocarbon target and so a model of the neutrino interaction with the carbon nucleus was required. Three different nuclear models have been employed: a relativistic Fermi gas model, the superscaling approximation model, and a spectral function model. We find a tremendous improvement in the constraint of GAs(Q2) at low Q2 compared to previous work, although more data is needed from NCE measurements that focus on exclusive single-proton final states, for example from MicroBooNE. Published by the American Physical Society 2024

Investigation on the exopolysaccharide production from blueberry juice fermented with lactic acid bacteria: Optimization, fermentation characteristics and Vis-NIR spectral model

The exopolysaccharide production from blueberry juice fermented were investigated. The highest exopolysaccharide yield of 2.2 ± 0.1 g/L (increase by 32.5 %) was reached under the conditions of temperature 26.5 °C, pH 5.5, inoculated quantity 5.4 %, and glucose addition 9.1 % using the artificial neural network and genetic algorithm. Under the optimal conditions, the viable cell counts and total acids were increased by 2.0 log CFU/mL and 1.6 times, respectively, while the content of phenolics and anthocyanin was decreased by 9.26 % and 7.86 %, respectively. The changes of these components affected the exopolysaccharide biosynthesis. The absorption bands of –OH and –CH associated with the main functional groups of exopolysaccharide were detected by Visible near-infrared spectroscopy. The prediction model based on spectrum results was constructed. Competitive adaptive reweighted sampling and the random forest were used to enhance the model's prediction performance with the value of RC = 0.936 and RP = 0.835, indicating a good predictability of exopolysaccharides content during fermentation.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique

BackgroundLaser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed. ResultsWe proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples. Significance and noveltyIn this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Fast Graph Generation via Spectral Diffusion.

Generating graph-structured data is a challenging problem, which requires learning the underlying distribution of graphs. Various models such as graph VAE, graph GANs, and graph diffusion models have been proposed to generate meaningful and reliable graphs, among which the diffusion models have achieved state-of-the-art performance. In this paper, we argue that running full-rank diffusion SDEs on the whole graph adjacency matrix space hinders diffusion models from learning graph topology generation, and hence significantly deteriorates the quality of generated graph data. To address this limitation, we propose an efficient yet effective Graph Spectral Diffusion Model (GSDM), which is driven by low-rank diffusion SDEs on the graph spectrum space. Our spectral diffusion model is further proven to enjoy a substantially stronger theoretical guarantee than standard diffusion models. Extensive experiments across various datasets demonstrate that our proposed GSDM turns out to be the SOTA model, by exhibiting both significantly higher generation quality and much less computational consumption than the baselines.

Feasibility Analysis of Using CO2 as a Building Fire Detection Indicator

CO2 has rarely been examined as a fire indicator owing to the uncertainty in its background concentration. Nevertheless, for a specific building space with a detector, the background CO2 concentration signal can be determined and documented using artificial intelligence technology. Based on the aforementioned concept, this study explored the feasibility of using CO2 signal features as fire indicators for building fire detection. First, CO2 concentrations under fire and no-fire conditions were obtained to generate study databases. Second, the deep temporal clustering (DTC) model was used to classify CO2 signal features between fire and no-fire scenes, and the clustering accuracy and purity were selected to determine the clustering performance. Third, the k-means, agglomerative clustering, and spectral bi-clustering models were employed to validate the clustering advantages of the DTC model. The results proved the CO2 concentration to be more suitable as a combustion indicator for fire detection compared to the CO2 concentration rate, exhibiting higher clustering accuracy and purity values. Additionally, for time-series clustering, the DTC model outperformed traditional clustering models. The findings of this study represent a fundamental step toward the realization of space-exclusive multi-sensor fire-detection technology for assisting smart building construction in a cost-effective and accurate manner.

Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China

Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.