Spectral Attributes Research Articles

Surround suppression in mouse auditory cortex underlies auditory edge detection.

Surround suppression (SS) is a fundamental property of sensory processing throughout the brain. In the auditory system, the early processing stream encodes sounds using a one dimensional physical space-frequency. Previous studies in the auditory system have shown SS to manifest as bandwidth tuning around the preferred frequency. We asked whether bandwidth tuning can be found around frequencies away from the preferred frequency. We exploited the simplicity of spectral representation of sounds to study SS by manipulating both sound frequency and bandwidth. We recorded single unit spiking activity from the auditory cortex (ACx) of awake mice in response to an array of broadband stimuli with varying central frequencies and bandwidths. Our recordings revealed that a significant portion of neuronal response profiles had a preferred bandwidth that varied in a regular way with the sound's central frequency. To gain insight into the possible mechanism underlying these responses, we modelled neuronal activity using a variation of the "Mexican hat" function often used to model SS. The model accounted for response properties of single neurons with high accuracy. Our data and model show that these responses in ACx obey simple rules resulting from the presence of lateral inhibitory sidebands, mostly above the excitatory band of the neuron, that result in sensitivity to the location of top frequency edges, invariant to other spectral attributes. Our work offers a simple explanation for auditory edge detection and possibly other computations of spectral content in sounds.

Spectral Attributes in Onshore Fuba Field Niger-Delta, Nigeria, Using 3-D Seismic Time-lapse Data

Spectral attributes in Onshore Fuba Field Niger Delta, Nigeria, are here presented, using 3D seismic time-lapse data. The FUBA Field lies on latitudes 40 50’58’’- 4 0 55’19’’N and longitudes 60 18’41’’- 6 0 26’41’’E with aerial extent of 840km2 . The base (1997) and the monitor (2009) seismic surveys resulted in a 4D response difference. The Base and Monitor data have a root-mean-square repeatability ratio (RRR) of 0.38 implying a very good repeatability when considering the acquisition, processing and environmental noises. Data processing and interpretation were carried out using Petrel software. Reservoir pressure decline rate of 0.062psi/day resulted in production decline rate of 1192.21bbl/day. Structural interpretation of seismic data reveals a highly faulted field. Two distinct horizons were mapped. Fault and horizon interpretation shows closures that are collapsed crestal structures bounded by two major faults. All the interpreted faults are normal synthetic and antithetic faults which are common in the Niger Delta basin. The depth structure maps reveal anticlinal faults. Reservoirs are found at a shallower depth from 6500 to 7500 ft and at a deeper depth ranging from 11500 to 13000 ft. The variance values range from 0.0 to 1.0. The Variance edge analysis was used to delineate the prominent and subtle faults in the area. The results of spectral decomposition at the different frequencies indicates areas of low frequency and high amplitude associated with known hydrocarbon zones, meandering channels, lobes and the presence of small scale faults in the field. The results of the work will help in the recovery of more hydrocarbon as by-passed zones and subtle structures are revealed in the area of study.

Exploration and Evaluation of Pony Fish (Leiognathus sp) Mince as a Source of Surimi and Study its Spectral and Textural Attributes

Background: This is the first study in Indian context where underutilized fish species pony fish (Leiognathus sp.) has been used for the preparation of surimi and surimi gel and its textural and spectral images were analysed. Methods: Textural profile analysis for estimating the hardness, gumminess and chewiness parameters and colour analyser for measuring the difference in whiteness among samples were used. FTIR (Fourier Transform Infrared Spectroscopy) was used to estimate the percentage of proteins folded during heat denaturation process. Result: Textural parameters of CWS (Conventional Washed Surimi) gel showed ideal gel strength of 191.06 g cm, hardness of 46.48 N, gumminess of 30.75 N and chewiness of 28.70 N. Colour analysis of UM (Unwashed Mince), CWS and CWS gel showed whiteness of 38.59, 61.45 and 72.07. FTIR analysis of UM, CWS and CWS gel shows unfolding of protein structures in the wavelength of 1688.07 cm-1, 1665.34 cm-1 and 1653.20 cm-1. This study reveals the potential use of pony fish (Leiognathus sp.) as one of the favorable fish species for surimi and sausage production showing virtuous instrumental quality attributes.

Perception Accuracy of a Multi-Channel Tactile Feedback System for Assistive Technology.

Assistive technology uses multi-modal feedback devices, focusing on the visual, auditory, and haptic modalities. Tactile devices provide additional information via touch sense. Perception accuracy of vibrations depends on the spectral and temporal attributes of the signal, as well as on the body parts they are attached to. The widespread use of AR/VR devices, wearables, and gaming interfaces requires information about the usability of feedback devices. This paper presents results of an experiment using an 8-channel tactile feedback system with vibrators placed on the wrists, arms, ankles, and forehead. Different vibration patterns were designed and presented using sinusoidal frequency bursts on 2, 4, and 8 channels. In total, 27 subjects reported their sensation formally and informally on questionnaires. Results indicate that 2 and 4 channels could be used simultaneously with high accuracy, and the transducers' optimal placement (best sensitivity) is on the wrists, followed by the ankles. Arm and head positions were inferior and generally inadequate for signal presentation. For optimal performance, signal length should exceed 500 ms. Furthermore, the amplitude level and temporal pattern of the presented signals have to be used for carrying information rather than the frequency of the vibration.

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessment, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages. In this study, we utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost, to compensate for the deficiency of forestry inventory using UAS-based LiDAR that requires terrain-following flight for high-resolution data acquisition. Combined with the sparse LiDAR points acquired by using a high-altitude and high-speed UAS for terrain extraction, dense normalized DAP point clouds can be obtained to produce an accurate and high-resolution canopy height model (CHM). Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency. It is proved that sparse LiDAR point cloud could serve as important sources for terrain estimation. The accuracy of the AGB estimates could be improved by 9% and 5% in terms of the RMSE and R2, respectively, by adding spectral metrics to point cloud structural metrics. Compared with results obtained using only spectral metrics, the results obtained using the combination of spectral and point cloud metrics were better by 37% in terms of the RMSE and 55% in R2. Our study supports the development of predictive models for large-scale wall-to-wall AGB mapping by leveraging the complementarity between DAP and LiDAR measurements. This work also reveals the potential of utilizing a UAS-based digital camera and LiDAR synergistically in a plateau mountainous forest area. In this future, we will further investigate different establishing strategies for combining different sensors in a synergistic system.

Hyperspectral Image Classification via Spectral Pooling and Hybrid Transformer

Hyperspectral images (HSIs) contain spatially structured information and pixel-level sequential spectral attributes. The continuous spectral features contain hundreds of wavelength bands and the differences between spectra are essential for achieving fine-grained classification. Due to the limited receptive field of backbone networks, convolutional neural networks (CNNs)-based HSI classification methods show limitations in modeling spectral-wise long-range dependencies with fixed kernel size and a limited number of layers. Recently, the self-attention mechanism of transformer framework is introduced to compensate for the limitations of CNNs and to mine the long-term dependencies of spectral signatures. Therefore, many joint CNN and Transformer architectures for HSI classification have been proposed to obtain the merits of both networks. However, these architectures make it difficult to capture spatial–spectral correlation and CNNs distort the continuous nature of the spectral signature because of the over-focus on spatial information, which means that the transformer can easily encounter bottlenecks in modeling spectral-wise similarity and long-range dependencies. To address this problem, we propose a neighborhood enhancement hybrid transformer (NEHT) network. In particular, a simple 2D convolution module is adopted to achieve dimensionality reduction while minimizing the distortion of the original spectral distribution by stacked CNNs. Then, we extract group-wise spatial–spectral features in a parallel design to enhance the representation capability of each token. Furthermore, a feature fusion strategy is introduced to increase subtle discrepancies of spectra. Finally, the self-attention of transformer is employed to mine the long-term dependencies between the enhanced feature sequences. Extensive experiments are performed on three well-known datasets and the proposed NEHT network shows superiority over state-of-the-art (SOTA) methods. Specifically, our proposed method outperforms the SOTA method by 0.46%, 1.05% and 0.75% on average in overall accuracy, average accuracy and kappa coefficient metrics.

Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas

Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays consistent performance and is the most reliable, followed by QUAC and FLAASH. Pansharpening improved overall accuracy and GS performed better than HCS. The study reports robust segmentation parameters that may be transferable to other VHR-based glacier surface facies mapping applications. The rule sets are adjusted across the processing schemes to adjust to the change in spectral characteristics introduced by the varying routines. The results indicate that GEOBIA for glacier surface facies mapping may be less prone to the differences in spectral signatures introduced by different atmospheric corrections but may respond well to increasing spatial resolution. The study highlighted the role of spatial attributes for mapping fine features, and in combination with appropriate spectral features may enhance thematic classification.

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.

Burn characterization using object-oriented hyperspectral image classification.

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% ± 0.60%), precision (97.30% ± 2.46%), sensitivity (97.23% ± 3.02%) and specificity (98.02% ± 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases

Unmanned aerial vehicle (UAV) remote sensing and machine learning have emerged as a practical approach with ultra-high temporal and spatial resolutions to overcome the limitations of ground-based sampling for continuous crop monitoring. However, little is known on the suitability of distinct sensing indices for different crop management and distinct crop development phases. In this study, we assessed the potential of the UAV-based modeling to monitor field-scale crop growth under different water and nutrient supply considering distinct phenological phases of maize. UAV multispectral observations were deployed over two long-term experimental sites in three growing seasons. Calibration and validation of the random forest model took place at the Nutrient Balance Experimental Site (NBES) and the Water Nitrogen Crop Relation Site (WNCR), respectively. Leaf area index, leaf chlorophyll concentration, and aboveground dry matter were measured at the jointing, heading, and grain filling phases of maize in 2018–2020. Our results revealed that the suitability of sensing indicators differed at distinct maize phenological phases. Overall, red edge, red edge reflectance ratio, and chlorophyll index green are the most appropriate UAV indicators for estimating maize growth variables. The random forest model developed and calibrated at NBES with nutrient supply detected the signal of nitrogen × irrigation interactions at the other experimental site (WNCR) in different development phases and years very well, suggesting that random forest models developed by UAV images of same spatial and spectral attributes could be transferred across sites with the same cultivar while different irrigation and fertilizer management. We conclude that the selected number of UAV detected indicators processed with a random forest model could be used for robustly estimating environment × management (fertilizer and irrigation) interactions on maize growth variables. • Red edge and chlorophyll index green best estimate maize growth across distinct nutrient and water management. • The suitability of UAV-based indicators depends on crop development phase. • Random forest models developed at nutrient experiment can detect distinct water and nutrient effects using similar cultivars.

Preparation and Characterization of Clopidogrel Bisulfate-hydroxypropyl-β-Cyclodextrin Mixed Inclusion Complex for Improved Intestinal Solubility and Anti-Thrombotic Efficacy

The study aimed to increase the intestinal solubility and absorption of orally bioavailable clopidogrel-bisulfate (CPB) by complexing with hydroxypropyl-β-cyclodextrin (HCD) to form a binary inclusion complex that was stabilized by Tween 80 (T80) resulting into mixed inclusion complex. The results of phase solubility studies and molecular docking of CPB with β-cyclodextrin (β-CD) and HCD suggested higher solubility and binding energy of the stable CPB: HCD complex in the presence of T80 as compared to the CPB: β-CD complex. The D-Optimal mixture design was used to optimize the formulation containing the CPB: HCD: T80 mixed inclusion complex. The results suggest the enhanced stability of the CPB: HCD inclusion complex in the presence of T80. The spectral attributes confirmed the inclusion complexation and pointed out the central position of CPB in a hydrophilic cavity of HCD. Further, the prepared soft gelatin capsule successfully confirmed the importance of obliterating the intestinal precipitation associated problem of CPB through an in-vitro release study. The anticoagulant activity in rabbits confirmed that soft gelatin capsules showed 1.2 folds and 1.3 folds increase in clotting time, 1.2 fold and 1.5 folds increase in bleeding time when compared to marketed formulation and pure drug, respectively. Conclusively, soft gelatin capsules exhibit the potential to enhance the oral bioavailability of CPB, leading to reduction of the dose and dose-related side effects.

Perceptual Space of Algorithms for Three-to-One Dimensional Reduction of Realistic Vibrations.

Haptics researchers often endeavor to deliver realistic vibrotactile feedback through broad-bandwidth actuators; however, these actuators typically generate only single-axis vibrations, not 3D vibrations like those that occur in natural tool-mediated interactions. Several three-to-one (321) dimensional reduction algorithms have thus been developed to combine 3D vibrations into 1D vibrations. Surprisingly, the perceptual quality of 321-converted vibrations has never been comprehensively compared to rendering of the original 3D signals. In this study, we develop a multi-dimensional vibration rendering system using a magnetic levitation haptic interface. We verify the system's ability to generate realistic 3D vibrations recorded in both tapping and dragging interactions with four surfaces. We then conduct a study with 15 participants to measure the perceived dissimilarities between five 321 algorithms (SAZ, SUM, VM, DFT, PCA) and the original recordings. The resulting perceptual space is investigated with multiple regression and Procrustes analysis to unveil the relationship between the physical and perceptual properties of 321-converted vibrations. Surprisingly, we found that participants perceptually discriminated the original 3D vibrations from all tested 1D versions. Overall, our results indicate that spectral, temporal, and directional attributes may all contribute to the perceived similarities of vibration signals.

Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networks

Lithology prediction from seismic data is of great significance for sweet-spot detection, reservoir structure delineation, and geologic model building, hence it is important in reducing the risk of exploration and development. Traditional lithofacies prediction methods often are limited by the seismic inversion accuracy and reliability of the rock-physics relationships, which are challenging to be applied in complex reservoirs (such as those containing coal-bearing strata or thin layers). Convolutional neural networks (CNNs) can represent the coupling relationship of seismic characteristics in the time domain through multilayer convolution and effectively manipulate multitype and multidimensional seismic data. Under the framework of a supervised CNN, we jointly integrate prestack seismic gathers (Pre-SGs), seismic inversion results (P-impedance and [Formula: see text] ratio), multiseismic attributes (amplitude-variation-with-offset [AVO] intercept, AVO gradient, instantaneous amplitude, instantaneous frequency, and instantaneous phase), and spectral decomposition attributes (SDA) to predict lithofacies in a complex clastic reservoir interbedded with thin-layer coal. We determine that the fusion model with multiseismic information containing different perspectives and complementary information of seismic data is capable of achieving better prediction performance than only using one type of input feature. In particular, using the proposed methodology, the angle-dependent Pre-SG is essential to decipher the rich information of lithologic details. The models using only poststack seismic data or inversion results cannot reliably describe lithologic details (especially the thin-coal layers). In addition, by including the SDA into model inputs, the model’s ability to recognize thin layers has been further improved but lead to the slight sacrifice of overall prediction accuracy.

Optimization of hazelnut husk medium for pullulan production by a domestic A. pullulans strain

Hazelnut husk is one of the most abundant agricultural residue in Turkey. Valorization of this lignocellulosic biomass would provide a promoting alternative for economical production of pullulan. In this study, dried hazelnut husk hydrolysate was used directly as fermentation medium for pullulan production by a domestic strain of Aureobasidium pullulans. The aim of this work was the optimization of some fermentation medium parameters by central composite design using response surface methodology (RSM). The effects of (NH4)2SO4 concentration, the volume of concentrated H2SO4 and the amount of ground hazelnut husk on pullulan production were optimized by RSM. The optimum levels of the fermentation parameters defined as 7.2 gL−1, 2.5 mL and 20 g, respectively. The maximum pullulan and exopolysaccharide concentrations were determined as 74.39 and 75.95 gL−1 , respectively in the optimum conditions. Specific growth rate of the strain was found as 0.097 h−1. FTIR spectral attributes confirmed the structure of pullulan. Thermal decomposition temperature of synthesized pullulan was found as 247.15 °C. This study showed that hazelnut husk was one of the novel substrate for production of the pullulan by A. pullulans AZ-6. No previous work was found to utilize dried hazelnut husk as fermentation medium for pullulan production by A. pullulans.

Solar illumination effects on the dry-season variability of spectral and spatial attributes calculated from PlanetScope data over tropical forests of the Amazon

ABSTRACT The spectral variability of tropical forests during the Amazonian dry season is not entirely understood because of the divergent responses in Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) measured under-increased water deficit and high insolation. Here, we used a dataset composed of 493 cloud-free PlanetScope (PS) images to investigate possible effects of solar illumination on the dry-season variability of spectral and spatial attributes. The attributes were calculated from June to September over dense tropical forests of the Amazon. The dry-season images were obtained at nadir viewing between 2017 and 2019 over 12 selected sites representing different climatic and environmental conditions. To detect dry-season patterns of vegetation brightness with changes in the geometry of image acquisition, we applied principal component analysis (PCA) over the PS surface reflectance. We plotted the average surface reflectance (2017–2019) for each of the four PS bands and inspected the variability of two VIs with distinct levels of anisotropy to bidirectional effects: the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We also investigated the signal of textural metrics from Grey Level Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR) band of PS. Finally, we generated shade fractions from Spectral Mixture Analysis (SMA), correlated the spectral and spatial attributes of vegetation with solar angles, and observed the dry-season variability in reflectance and VIs over pseudo-invariant soil surfaces. The results showed the existence of solar illumination effects on PS image acquisition during the dry season of the Amazon, which affected differently the NDVI and EVI. From the beginning (June) to the end (September) of the dry season, the solar zenith angle (SZA) decreased and the solar azimuth angle (SAA) increased during the period of acquisition of the PS images. The amplitude of SZA between June and September increased towards south of the Amazon, while the amplitude of SAA increased towards north of this region. Changes in vegetation brightness from June to September were captured by PCA over some sites. Because of the overall increase in both red and NIR band reflectance, solar illumination effects were compensated during the NDVI calculation. In contrast, because the EVI is largely driven by changes in NIR reflectance, these effects contributed to increase the EVI signal at the end of the dry season. For most sites, GLCM texture mean increased towards the end of the dry season, while texture variance decreased in the opposite direction. Shade fractions decreased towards September when reduced amounts of canopy shadows were sensed by PS. EVI was more anisotropic than NDVI and presented higher negative correlations with SZA and shade fractions and higher positive correlations with SAA and texture mean. The dry-season increase in EVI with solar illumination effects was also observed over pseudo-invariant soil surfaces. From this unprecedent scale of observations at high spatial and temporal resolutions, we recommend caution when using anisotropic VIs for large-scale phenological studies over the Amazon because biophysical and non-biophysical signals may be coupled together.

Characterizing the subsea Pleistocene fluvial system of the Sunda shelf, offshore Malaysia, using multiattribute corendering and self-organizing maps

The advancement of multiattribute analysis techniques has significantly improved the ability to capture subtle geologic information that is not easily detected on the original seismic. The use of corendering and automated facies clustering/classification techniques with an appropriate set of seismic attributes increases the reliability and accelerates the interpretation process. We have proved the effectiveness of multiattribute corendering and self-organizing maps (SOMs) in characterizing the late Pleistocene fluvial system of the Sunda shelf that was previously studied using the original seismic only. The work aims to document the attribute expression and capture subtle depositional patterns of the fluvial system. We find that amplitude, texture, and spectral attributes delineate the lithology contrast among different depositional features, whereas geometric attributes reveal channel morphology. The red-green-blue and alpha blends significantly increased the data interpretability by manipulating up to four attributes in the same display. In addition, the SOM clustering aids in distinguishing among different sand-prone depositional elements and muddy facies. We use the cross-cutting relation and superposition rules to understand the stratigraphic evolution of the interval. We observe several fluvial depositional elements through a closely spaced time slicing, including straight channel, meander channel, point bar, crevasse splay, and levee. We recognize that the straight and low-sinuosity channels were developed along preexisting faults and find a deeper incision and thicker sediment fill than the other channel types. The widely variable fluvial style in the middle of the interval is attributed to a rapid change in the accommodation space and climate conditions. The small meanders that find a prominent dendritic pattern indicate a marine influence on the less channelized uppermost part of the interval. The well-imaged and documented shallow fluvial system in the area represents an excellent analogous that helps to understand and accurately predict the reservoir distribution in the deeper productive stratigraphic units.

Tautomers and Rotamers of Curcumin: A Combined UV Spectroscopy, High-Performance Liquid Chromatography, Ion Mobility Mass Spectrometry, and Electronic Structure Theory Study.

The structures of tautomers and rotameric forms of curcumin, the bioactive compound present in spice plant turmeric, have been investigated using ion mobility mass spectrometry (IMMS) in conjunction with high-performance liquid chromatography (HPLC) and UV-visible spectroscopy. Two tautomeric forms of this β-diketone compound, keto-enol and diketo, have been chromatographically separated, and the electronic absorption spectra for these two tautomeric forms in methanol solution have been recorded separately for the first time. The molecular identity of the HPLC-separated solution fractions is established unambiguously by recording the mass and fragmentation spectra simultaneously. The ion mobility spectrum for the deprotonated curcumin anion, [Cur-H]-, corresponding to the diketo tautomer, displays only one peak (P), and the collision cross-section (CCS) value is measured to be 185.9 Å2. However, the ion mobility spectrum corresponding to the HPLC-separated keto-enol tautomer shows three distinctly separated peaks, P, Q, and R, with CCS values of 185.9, 194.8, and 203.4 Å2, respectively, whereby peak R appears to be the most intense one, followed by peaks P and Q. The theoretically calculated CCS values of different isomers of [Cur-H]-, optimized by electronic structure theory methods, display satisfactory correlation with the experimentally observed values, corroborating our assignments. The spectral attributes also indicate the occurrence of structural rearrangements in the electrospray ionization process. With the aid of the electronic structure calculation, low-energy pathways for the occurrence of the structural isomerization to surpass the energy barrier are suggested, which are consistent with the assignments of the peaks observed in the IM spectra.

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

Unexpected climatic conditions or extreme climatic events in vineyards are a worldwide problem that requires accurate spatial and temporal monitoring. Satellite-based remote sensing is an important source of data to assess this challenge in a climate-change context. This paper provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess heatwave impacts in vineyards at a regional scale. Multi-way partial least squares (N-PLS) regression was used as a supervised technique to predict the intensity of damage caused to vineyards by the heatwave phenomenon that impacted the vineyards in the south of France in 2019. The model was developed based on available ground truth data of yield losses for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The model showed a performance accuracy (R2) of 0.56 in the calibration set and of 0.66 in the validation set, with a standard error of cross-validation in the calibration set of 12.4% and a standard error of the prediction of yield losses in the validation set of 10.7. The model was applied at a regional scale on 4978 vineyard blocks to predict yield losses using spectral and temporal attributes. The prediction of the yield loss due to heat stress at a regional scale was related to the spatial pattern of maximum temperatures recorded during the extreme weather event. This relation was confirmed by a chi-square test (p < 5%). The introduction of N-PLS insights into the analysis enables the characterisation of heat stress responses in vineyards and the identification of spectro-temporal profiles relevant for understanding the effects of heatwaves on vine blocks at a regional scale.

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

Hybrid machine learning classification scheme for speaker identification.

Motivated by the requirement to prepare for the next generation of "Automatic Spokesperson Recognition" (ASR) system, this paper applied the fused spectral features with hybrid machine learning (ML) strategy to the speech communication field. This strategy involved the combined spectral features such as mel-frequency cepstral coefficients (MFCCs), spectral kurtosis, spectral skewness, normalized pitch frequency (NPF), and formants. The characterization of suggested classification method could possibly serve in advanced speaker identification scenarios. Special attention was given to hybrid ML scheme capable of finding unknown speakers equipped with speaker id-detecting classifier technique, known as "Random Forest-Support Vector Machine" (RF-SVM). The extracted speaker precise spectral attributes are applied to the hybrid RF-SVM classifier to identify/verify the particular speaker. This work aims to construct an ensemble decision tree on a bounded area with minimal misclassification error using a hybrid ensemble RF-SVM strategy. A series of standard, real-time speaker databases, and noise conditions are functionally tested to validate its performance with other state-of-the-art mechanisms. The proposed fusion method succeeds in the speaker identification task with a high identification rate (97% avg) and lower equal error rate (EER) (<2%), compared with the individual schemes for the recorded experimental dataset. The robustness of the classifier is validated using the standard ELSDSR, TIMIT, and NIST audio datasets. Experiments on ELSDSR, TIMIT, and NIST datasets show that the hybrid classifier produces 98%, 99%, and 94% accuracy, and EERs were 2%, 1%, and 2% respectively. The findings are then compared with well-known other speaker recognition schemes and found to be superior.