Spectral Differences Research Articles

Interpreting Heat Flux Measurements in a Vitiated Backward-Facing Step Flow

The investigation of the relative impact of convective and radiative heat flux from vitiated gases to a combustor wall was performed in a backward-facing step combustor. Backward-facing step flows have many flow features of gas turbine combustors (recirculation, impingement, and boundary-layer development), but in a two-dimensional, nonproprietary geometry. Reynolds number, gas temperature, and plate temperature were varied as heat flux was measured in different regions of the flow. Measurements were made using a heat flux sensor and radiometer, which measure total and radiative heat flux, respectively. The highest total heat flux was measured at the impingement location and the lowest in the recirculation zone. Separating the total heat flux into the radiative and convective components is not straightforward and requires simulations of the experiment. Spectrally resolved Monte Carlo ray-tracing simulations are used to scale the radiative heat flux measurements for the separation of convective and radiative heat flux from the total heat flux measurements. These calculations show that only about 30% of incident radiation is captured by the radiometer due to spectral differences between the two sensors. Once the radiation measurement is corrected, experimental results show higher radiative than convective heat transfer at low Reynolds numbers (Reh=2000), highlighting the impact of radiative heat transfer in hot gas environments.

Tissue equivalent conversion of silicon-based microdosemeters in hadron therapy.

Silicon has been developed as a microdosemeter, as it can provide sensitive volumes at submicrometric levels, does not need a gas supply, has a fast response, and has low power consumption. However, since the energy response in silicon is not the same as that in tissue, a spectral conversion from silicon to tissue is necessary to obtain the probability distribution of energy deposition in tissue. In this work, we present a method for microdosimetric spectra conversion from silicon to tissue based on the scaled Fourier transformation and the geometric scaling factor, which shows relatively good results in the spectral conversion from diamond to tissue. The results illustrate that the method can convert the energy deposition spectra from silicon to tissue with proper accuracy. Meanwhile, the inconsistency between the converted and actual spectra due to the inherent difference was also observed. Whereas, the reasons for the disagreement are different. For the plateau part of the Bragg curve, the discrepancy between the converted and actual spectra is due to the poor tissue equivalent of silicon. For the proximal part of the Bragg curve, the spectral difference is attributed to the different shapes of the energy deposition spectra obtained in silicon and water, which is the same as that in the diamond. In summary, this method can be employed in the tissue equivalent conversion of silicon microdosemeter, but the poor tissue equivalent of silicon limited the accuracy of this method. In addition, the correction for the deviation between the converted and calculated spectra due to the difference in spectral shapes is required to improve the practicality of this mod.

Decipherment of text on foxed documents by Hyperspectral Imaging on VSC 6000

Foxing is a process of deterioration that generates spots and browning on ancient paper documents such as books, monographs, postage stamps, certificates, etc. It is believed that metals and bacteria are the primary causes of foxing production. According to scientific studies, physical, chemical, and biological processes degrade paper. Paper is especially prone to biodeterioration processes due to its organic composition and hygroscopic nature; as a result, foxed paper becomes weaker, more fragile, and more acidic than undamaged paper. In circumstances where the text of a book or other document becomes unreadable owing to foxing, measures must be established to make the content readable. In the present study, efforts were made to understand the unreadable text content of books damaged by foxing, and the capabilities of Hyperspectral Image on VSC 6000 employing Near-Infrared Multispectral imaging analysis were presented. It is a method for optimising spectral differences that is non-invasive. First, microbiological flora (fungal and bacterial) from old books were identified, followed by the selection of the mechanism of VSC. The results revealed a correlation between fungal foxing and bacterial foxing, leading to the conclusion that fungus foxing necessitates distinct wavelength ranges between 600 and 800 nm for the decipherment of text, but bacterial foxing allows for decipherment at 645nm. Purchase Article - $10

Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils

Soil, particularly in agricultural regions, has been recognized as one of the significant reservoirs for the emerging contaminant of MPs. Therefore, developing a rapid and efficient method is critical for their identification in soil. Here, we coupled HSI systems [i.e., VNIR (400–1000 nm), InGaAs (800–1600 nm), and MCT (1000–2500 nm)] with machine learning algorithms to distinguish soils spiked with white PE and PA (average size of 50 and 300 µm, respectively). The soil–normalized SWIR spectra unveiled significant spectral differences not only between control soil and pure MPs (i.e., PE 100% and PA 100%) but also among five soil–MPs mixtures (i.e., PE 1.6%, PE 6.9%, PA 5.0%, and PA 11.3%). This was primarily attributable to the 1st–3rd overtones and combination bands of C–H groups in MPs. Feature reductions visually demonstrated the separability of seven sample types by SWIR and the inseparability of five soil–MPs mixtures by VNIR. The detection models achieved higher accuracies using InGaAs (92–100%) and MCT (97–100%) compared to VNIR (44–87%), classifying 7 sample types. Our study indicated the feasibility of InGaAs and MCT HSI systems in detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soil. SynopsisOne of two SWIR HSI systems (i.e., InGaAs and MCT) with a sample imaging surface area of 3.6 mm² per grid cell was sufficient for detecting PE (as low as 1.6%) and PA (as low as 5.0%) in soils without the digestion and separation procedures.

Mind the gaps: horizontal canopy structure affects the relationship between taxonomic and spectral diversity

ABSTRACT Spectral diversity (SD) in reflectance can be used to estimate plant taxonomic diversity (TD) according to the Spectral Variation Hypothesis (SVH). However, contrasting relationships between SD and TD have been reported by different studies. Indeed, multiple factors may affect SD, including spatial and spectral scales, vegetation characteristics and the adopted SD computational method. Here, we tested the SVH over 171 plots within a large and heterogeneous forest area in North-Eastern Italy using Sentinel-2 data, aiming at identifying possible factors affecting the strength and direction of SD-TD relationship. SD was determined using ‘biodivMapR’ (BD) and ‘rasterdiv’ (RD) R packages and 38 possible combinations of SD indices, at both α (within a community) and β (among communities) levels, and computational parameters accounting for spatial and spectral scales. Information on vegetation structure was either retrieved from ground-based or LiDAR data. A Random Forest approach was used to disentangle the relationships between SD, TD and vegetation structure, and to identify the best combination of SD computational parameters. At the α-level, we found negative relationship between TD and RD SD indices, which was mainly driven by the presence of gaps within the forest canopy. As regards BD, we found that this algorithm reduced background contribution on SD and was able to differentiate major forest types (broadleaves vs conifers), but derived α-SD indices were marginally correlated with α-TD. At the β-level, we observed a statistically significant positive correlation between BD SD indices and TD (maximum r = 0.24). Finally, we found stronger correlations and R2 when SD indices were calculated using smaller computation windows and over a larger pixels extraction area. Our findings suggest that vegetation cover and structure play a major role, with respect to inter-species spectral differences, in determining α-SD, and that SD might better capture differences in species composition at the landscape-level rather than the richness of individual communities.

Hyperspectral target detection using self-supervised background learning

Hyperspectral target detection is challenging in scenarios where spectral variability is high due to noise, spectral redundancy, and mixing. In addition, this spectral variability also creates the need for target detection algorithms to be robust against variations in the detection threshold. To overcome these challenges, this paper proposes a novel two-stage process for improved target detection in hyperspectral data. In the first stage, coarse detection is performed using a detector with a high probability of detection to identify background samples. These background samples are then used for background learning using an adversarial autoencoder (AAE) network, having spectral angle mapper (SAM) and Huber loss functions to minimize the impact of target pixels’ contamination. In the second stage, an inference is made using the spectral difference between the hyperspectral data and the output of the learned background model, which helps in reducing the false alarm rate of the first stage. The proposed approach is compared with seven other target detection techniques using multiple datasets and evaluated through several metrics, such as the area under the curve (AUC) and signal-to-noise probability ratio (SNPR). Results reveal that the proposed technique outperforms other detectors in terms of SNPR, indicating improved target detectability, background suppressibility, and more tolerance to variations in the detection threshold.

An assessment of multivariate predictions of various properties of crude oil on two ATR/IR spectrometers

About sixty commercially relevant crude oil samples are acquired on two spectrometers that are linked by a calibration transfer function and analyzed through PLS regression. About twenty multivariate models for distillation fractions are built and tested, as well as the models for API, acids, and sulfur. The parallel acquisition of samples on both spectrometers allows for deriving calibration and predictions errors for the models that use calibration transfer function as well as for the models independently built and tested on each of the used spectrometers. Testing of all available samples reveals that the calibration transfer approach produces notably higher prediction errors in comparison to the testing on the spectrometer on which the models are developed. Splitting the samples so that exact same sets for calibration and testing are used on both spectrometers (no calibration transfer) reveals that one spectrometer performs clearly better than the other although both operate under the same operating procedure and pass the same operational qualification. Comparison of the spectra of crude oils acquired on both spectrometers that are consistently identified as outliers on one of the spectrometers does not indicate any easily identifiable spectral differences. The results of this study indicate that the prediction errors may significantly differ depending on the performance of the spectrometers employed despite them nominally passing the qualification criteria, thus being nearly equivalent.

Distributed ultra large strain measurement range sensing based on spectral shift adjacent point difference method in OFDR

Compared with the time-domain distributed optical fiber sensing technology, optical frequency domain reflectometry (OFDR) has the advantages of high spatial resolution and low detection optical power requirements. However, in the large strain measurement, OFDR encountered such a problem: the correlation of the Rayleigh scattering spectra between strain signals and reference signals is relatively low if the loading strain is too large, resulting in incorrect demodulation. To address this issue, we propose a new method called the spectral shift adjacent point difference method. In our approach, the cross-correlation operations using two sets of non-strain signals are performed, and the average value of the cross-correlation’s spectral shift is selected as a reference standard. Then the spectral shift of the entire sensing fiber is obtained by re-sweeping frequency when the strain is loaded along the sensing fiber. The data points with a spectral shift adjacent point difference less than the reference standard will be retained, while other data points with spectral shift adjacent point difference greater than the reference standard will be considered as noisy fake spikes and removed. Finally, interpolation fitting is performed on the retained data points along the entire sensing fiber to obtain the demodulated spectral shifts realize large strain measurement. The experimental results show that the maximum measurable strain by the proposed method can reach 10,800 µε with a spatial resolution of 7.84 mm, which is an order of magnitude higher than the conventional method. The experiment demonstrates the high stability and excellent repeatability of the proposed processing method. The proposed method has the advantage of simple algorithm and easy implementation, and making it more competent in the field of large strain measurement.

A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Synovial fluid research based on SERS and SERRS for enhanced detection of biomarkers in staged osteoarthritis.

Surface-enhanced (resonance) Raman scattering (SER(R)S) can extremely enhance Raman intensity of samples, which is helpful for detecting synovial fluid (SF) that does not show Raman activity under normal conditions. In this study, SER(R)S spectra of SF from three different osteoarthritis (OA) stages were collected and analyzed for OA progress, finding that the content of collagen increased throughout the disease, while non-collagen proteins and polysaccharides decreased sharply at advanced OA stage accompanied by the increase of phospholipid. The spectral features and differences were enhanced by salting-out and centrifugation. Much more information on biomolecules at different OA stages was disclosed by using SERRS for the first time, these main trace components (β-carotene, collagen, hyaluronic acid, nucleotide, and phospholipid) can be used as potential biomarkers. It indicates that SERRS has a more comprehensive ability to assist SERS in seeking micro(trace) biomolecules as biomarkers and facilitating accurate and efficient diagnosis and mechanism research of OA.

A correction method for mitigating absorbance discrepancies between near-infrared spectrometers through the incorporation of blended carbon-titanium dioxide powder

In near-infrared spectroscopy analysis, ensuring the accurate transfer of models between different instruments relies on maintaining the accuracy of instrument wavelengths and absorbance. To mitigate absorbance drift at different wavelength points, this paper proposes a near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples. The method establishes a quadratic polynomial relationship model for the absorbance of each wavelength point between the main instrument and slave instruments. The study utilized two S450 grating-based diffuse reflection near-infrared spectroscopy instruments, with one serving as the main instrument and the other as the slave instrument. The point-by-point quadratic polynomial was employed to correct wheat spectra collected by the slave instruments, and a crude protein content prediction model for wheat was established, comparing it with linear regression correction. After correction, the average Euclidean distance of wheat spectra decreased by 66.71%, from 0.0937 to 0.0321, and the average peak-valley Euclidean distance decreased by 72.28%, from 0.0203 to 0.0056. The standard deviation of the predicted results decreased by 90.69%, from 1.4372 to 0.1338. The correction effect of the method combined with traditional preprocessing methods was superior to using preprocessing methods alone. Overall, the near-infrared spectroscopy point-by-point quadratic polynomial correction method based on carbon-titanium dioxide powder samples significantly reduces spectral differences between different instruments, enhances spectral consistency, and diminishes prediction errors, achieving improved model sharing between instruments.

Quality Control of Doppler Spectra from a Vertically Pointing, S-Band Profiling Radar

Abstract This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments. Significance Statement This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

306 Intraoral Spectroscopy for the Identification and Study of Molar Hypomineralization

OBJECTIVES/GOALS: Molar hypomineralization (MH) is a highly prevalent dental disease that leads to rapid enamel decay even with preventative measures. While the harm is apparent, the etiology of the condition is not. Further, MH is difficult to study due to limited intact extracted teeth. Therefore, we need a method to study MH non-destructively and intraorally. METHODS/STUDY POPULATION: Recent work has shown excess proteins not commonly found in enamel are present in teeth with MH. This is theorized to be due to disruption and infiltration in the cells that form enamel leading to leftover protein and under mineralized enamel. We hypothesizethese proteins have specific spectroscopic signatures that can be detected using light. We further hypothesize that modern fiber optic probes can provide a method for non-destructive, pain-free, and rapid intraoral examination of MH. Initially, Micro Raman spectroscopy was used to detect specific vibrational bands associated with organics in teeth with MH followed by the collection of spectra of teeth with MH and heathy enamel using a spectrophotometer. These spectra were examined for any obvious differences. RESULTS/ANTICIPATED RESULTS: Currently 12 teeth were collected, and micro-computed tomography reconstructions confirmed location in 3D of MH lesions. Micro Raman of a MH-affected tooth revealed clear organic associated Amide I and III vibrational bands when compared to a synthetic hydroxyapatite (mineral in enamel) powder standard. We determined the wavelength of light that can be used to detect spectral differences between healthy enamel and teeth with MH. The next steps include optimization of the protocol of the intraoral spectrometer with the determined wavelength for implementation in clinic to allow for collection of spectra without the need for tooth extraction. DISCUSSION/SIGNIFICANCE: We hope that this work will lead to advancements in our understanding of the mechanism of MH as well as act as a proof of concept for a MH diagnostic tool. In the long term, the goal is to ideally lead to improvements in dental health care, decrease dental costs, and improve overall quality of life for the children with this condition.

Effects of structural variations to x-ray absorption spectra of g-C3N4: Insight from DFT and TDDFT simulations

X-ray absorption spectroscopy (XAS) is widely employed for structure characterization of graphitic carbon nitride (g-C3N4) and its composites. Nevertheless, even for pure g-C3N4, discrepancies in energy and profile exist across different experiments, which can be attributed to variations in structures arising from diverse synthesis conditions and calibration procedures. Here, we conducted a theoretical investigation on XAS of three representative g-C3N4 structures (planar, corrugated, and micro-corrugated) optimized with different strategies, to understand the structure–spectroscopy relation. Different methods were compared, including density functional theory (DFT) with the full core-hole (FCH) or equivalent core-hole (ECH) approximation as well as the time-dependent DFT (TDDFT). FCH was responsible for getting accurate absolute absorption energy; while ECH and TDDFT aided in interpreting the spectra, through ECH-state canonical molecular orbitals (ECH-CMOs) and natural transition orbitals (NTOs), respectively. With each method, the spectra at the three structures show evident differences, which can be correlated with different individual experiments or in between. Our calculations explained the structural reason behind the spectral discrepancies among different experiments. Moreover, profiles predicted by these methods also displayed consistency, so their differences can be used as a reliable indicator of their accuracy. Both ECH-CMOs and NTO particle orbitals led to similar graphics, validating their applicability in interpreting the transitions. This work provides a comprehensive analysis of the structure-XAS relation for g-C3N4, provides concrete explanations for the spectral differences reported in various experiments, and offers insight for future structure dynamical and transient x-ray spectral analyses.

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying infestation levels was measured using a FieldSpec 4 spectroradiometer during July and September. A detailed assessment was conducted following the guidelines recommended by the European and Mediterranean Plant Protection Organization (EPPO) to quantify the level of infestation; categorising leaves into five distinct grades based on the percentage of leaf surface area affected. Subsequently, spectral data were collected using a contact probe with a tungsten halogen bulb connected to the spectroradiometer, taking three measurements across different areas of each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients of determination R2 = 0.74 and 0.71, and Root Mean Square Errors (RMSEs) of 12.1% and 12.9% for calibration and validation datasets, indicating high accuracy for early disease detection. Significant spectral differences were noted between healthy and infected leaves, especially around 450 nm and 700 nm for visible light, and 1050 nm, 1425 nm, 1650 nm, and 2250 nm for the near-infrared spectrum, likely due to tissue damage, chlorophyll degradation and water loss. Finally, the Powdery Mildew Vegetation Index (PMVI) was introduced, calculated as PMVI = (R755 − R675)/(R755 + R675), where R755 and R675 are the reflectances at 755 nm (NIR) and 675 nm (red), effectively estimating disease severity (R2 = 0.7). The study demonstrates that spectroscopy, combined with PMVI, provides a reliable, non-invasive method for managing powdery mildew and promoting healthier vineyards through precision agriculture practices.

A smartphone-based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions.

Real-time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone-based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short-term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10-fold cross-validation. This exploratory study demonstrates the potential of combining smartphone-based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

Exploring an application-oriented land-based hyperspectral target detection framework based on 3D–2D CNN and transfer learning

Target detection based on hyperspectral images refers to the integrated use of spatial information and spectral information to accomplish the task of localization and identification of targets. There are two main methods for hyperspectral target detection: supervised and unsupervised methods. Supervision method refers to the use of spectral differences between the target to be tested and the surrounding background to identify the target when the target spectrum is known. In ideal situations, supervised object detection algorithms perform better than unsupervised algorithms. However, the current supervised object detection algorithms mainly have two problems: firstly, the impact of uncertainty in the ground object spectrum, and secondly, the universality of the algorithm is poor. A hyperspectral target detection framework based on 3D–2D CNN and transfer learning was proposed to solve the problems of traditional supervised methods. This method first extracts multi-scale spectral information and then preprocesses hyperspectral images using multiple spectral similarity measures. This method not only extracts spectral features in advance, but also eliminates the influence of complex environments to a certain extent. The preprocessed feature maps are used as input for 3D–2D CNN to deeply learn the features of the target, and then, the softmax method is used to output and obtain the detection results. The framework draws on the ideas of integrated learning and transfer learning, solves the spectral uncertainty problem with the combined similarity measure and depth feature extraction network, and solves the problem of poor robustness of traditional algorithms by model migration and parameter sharing. The area under the ROC curve of the proposed method has been increased to over 0.99 in experiments on both publicly available remote sensing hyperspectral images and measured land-based hyperspectral images. The availability and stability of the proposed method have been demonstrated through experiments. A feasible approach has been provided for the development and application of specific target detection technology in hyperspectral images under different backgrounds in the future.

Examining How Interaural Differences Owing to Head Rotation during Walking Improve the Distance of Auditory Obstacle Perceptions among Individuals with Visual Impairment: A Case Study in Small-Scale Blind Group.

The ability of individuals with visual impairment to recognize an obstacle by hearing is called "obstacle sense". This ability is facilitated while they are moving, though the exact reason remains unknown. This study aims to clarify which acoustical factors may contribute to obstacle sense, especially obstacle distance perception. First, we conducted a comparative experiment regarding obstacle distance localization by individuals who are blind (N = 5, five men with blindness aged 22-42 (average: 29.8)) while they were standing and walking. The results indicate that the localized distance was more accurate while walking than while standing. Subsequently, the head rotation angle while walking and acoustic characteristics with respect to obstacle distance and head rotation angle were investigated. The peaks of the absolute head rotation angle during walking ranged from 2.78° to 11.11° (average: 6.55°, S.D.: 2.05°). Regarding acoustic characteristics, acoustic coloration occurred, and spectral interaural differences and interaural intensity differences were observed in the blind participants (N = 4, four men including two blind and two control sighted persons aged 25-38 (average: 30.8)). To determine which acoustic factors contribute, we examined the threshold of changes for interaural differences in time (ITD) and intensity (IID) (N = 11, seven men and four women with blindness aged 21-35 (average: 27.4)), as well as coloration (ICD) (N = 6, seven men and a woman with blindness aged 21-38 (average: 29.9))-depending on the head rotation. Notably, ITD and IID thresholds were 86.2 μs and 1.28 dB; the corresponding head rotation angles were 23.5° and 9.17°, respectively. The angle of the ICD threshold was 6.30° on average. Consequently, IID might be a contributing factor and ICD can be utilized as the cue facilitating the obstacle distance perception while walking.

Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion.

Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples' classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.