Spectral Descriptors Research Articles

Radiated Sound and Transmitted Vibration Following the Ball/Racket Impact of a Tennis Serve

Shock-induced vibrations transmitted from the racket to the tennis player’s upper limb have interested researchers, whether for investigating their effect on injury risk, or for designing new equipment. Measuring these vibrations is, however, very challenging in an ecological playing situation: sensors must be of very high quality in order to precisely measure high-energy and broad-frequency signals, as well as non-invasive in order to allow the players to perform their usual movements. The working hypothesis of this paper is that contactless sound recordings of the ball/racket impact carry the same information as direct vibratory measurements. The present study focuses on the tennis serve, as being tennis’ most energy-demanding stroke, therefore possibly being the most traumatic stroke for the upper limb. This article aims (a) to evaluate the propagation of vibration from the racket to the upper limb; and (b) to identify correlations with acoustic signals collected simultaneously. Eight expert tennis players performed serves with three rackets and two ball spin effects. Accelerometers measured the vibration on the racket and at five locations on the upper limb, and a microphone measured the impact sound. Resulting signals were analyzed in terms of energy and spectral descriptors. Results showed that flat serves produced louder sounds, higher vibration levels, lower acoustic spectral centroids, and higher vibratory spectral centroids than kick serves. The racket only had a marginal influence. Similarities between acoustic and vibratory measurements were found (levels were correlated), but so were differences (spectral centroids tended to be negatively correlated), encouraging further studies on the link between sound and vibration for the in situ measurement of shock-induced vibration.

Predictive potential of distance-related spectral graphical descriptors for structure-property modeling of thermodynamic properties of polycyclic hydrocarbons with applications

A distance-related spectral descriptor is a graphical index with defining structure built on eigenvalues of chemical matrices relying on distances in graphs. This paper explores the predictive ability of both existing and new distance-related spectral descriptors for estimating thermodynamic characteristics of polycyclic hydrocarbons (PHs). As a standard choice, the entropy and heat capacity are selected to represent thermodynamic properties. Furthermore, 30 initial members of PHs are considered as test molecules for this study. Three new molecular matrices have been proposed and our research demonstrates that distance-spectral graphical indices built by these novel matrices surpass in efficiency relative to famous distance-spectral indices. First, a novel computational method is put forwarded to evaluate distance-spectral indices of molecular graphs. The proposed methodology is utilized to compute both pre-existing and novel distance-related spectral descriptors, with an aim to assess their predictive efficacy using experimental data pertaining to two selected thermodynamic properties. Subsequently, we identify the five most promising distance-related spectral descriptors, comprising the degree-distance and Harary energies, the recently introduced second geometric-arithmetic energy along with its associated Estrada invariant, and 2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {nd}$$\\end{document} atom-bond connectivity (ABC) Estrada index. Notably, the 2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {nd}$$\\end{document} ABC Estrada index and Harary energy demonstrate correlation coefficients exceeding 0.95, while certain conventional spectral indices including the distance energy as well as its associated Estrada index, display comparatively lower performance levels. Moreover, we illustrate the practical implications of our findings on specific classes of one-hexagonal nanocones and carbon polyhex nanotubes. These outcomes hold potential for enhancing the theoretical determination of certain thermodynamic attributes of these nanostructures, offering improved accuracy and minimal margin of error.

Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification.

Convolutional neural networks (CNNs) have been widely applied to hyperspectral image classification (HSIC). However, traditional convolutions can not effectively extract features for objects with irregular distributions. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies, but fixed graph structures and local perceptions limit their performances. To tackle these problems, in this article, different from previous approaches, we perform the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes. Besides spatial objects, we also explore the graph relationships between channels by reasonably aggregating channels to generate spectral descriptors. The adjacent matrices in these graph convolutions are obtained by considering the relationships among all descriptors to realize global perceptions. By combining the extracted spatial and spectral graph features, we finally obtain a spectral-spatial graph reasoning network (SSGRN). The spatial and spectral parts of SSGRN are separately called spatial and spectral graph reasoning subnetworks. Comprehensive experiments on four public datasets demonstrate the competitiveness of the proposed methods compared with other state-of-the-art graph convolution-based approaches.

Identifying Spectral Descriptors for Protonation in BaZr0.8Y0.2O3-x with Electron Energy Loss Spectroscopy

Identifying Spectral Descriptors for Protonation in BaZr0.8Y0.2O3-x with Electron Energy Loss Spectroscopy

Cross-modal and multimodal data analysis based on functional mapping of spectral descriptors and manifold regularization

Multimodal manifold modeling methods extend spectral geometry-aware data analysis to incorporate learning from multiple related and complementary modalities. However, most methods assume an equal number of homogeneous data samples in each modality and partial correspondences between modalities as prior knowledge. This work introduces two new multimodal modeling methods. The first method introduces a comprehensive framework for handling multimodal information in heterogeneous data without requiring specific prior knowledge. To achieve this, we begin by extracting local descriptors using spectral graph wavelet signatures (SGWS) to identify manifold localities. Then, we propose a manifold regularization framework that incorporates functional mapping between SGWS descriptors (FMBSD) to determine pointwise correspondences. The second method involves manifold regularized multimodal classification based on pointwise correspondences (M2CPC). This method addresses the challenge of multiclass classification in multimodal heterogeneous data by determining correspondences between modalities using the FMBSD method. Experimental results evaluating the FMBSD method on three widely used cross-modal retrieval datasets and evaluating the M2CPC method on three benchmark multimodal multiclass classification datasets demonstrate their effectiveness and superiority over state-of-the-art methods.

Predictive potential of eigenvalues-based graphical indices for determining thermodynamic properties of polycyclic aromatic hydrocarbons with applications to polyacenes

For the structure–property modeling of thermodynamic properties of polycyclic aromatic hydrocarbons (PAHs), this paper explores the effectiveness of a specific class of graphical indices defined based on eigenvalues, also known as valency-spectral graphical indices. The 30 lower PAHs have been selected as test molecules. The heat capacity (Cp) and entropy (So) are the standard choices for characterizing thermodynamic properties due to their close correlation with the thermodynamic properties of a similar nature. A computer-dependent method is proposed to calculate valency-spectral indices of chemical graphs, and it is employed to compute the indices for our test molecules. The statistical inferences reveal an unexpected finding for the Estrada harmonic energy, a less familiarized spectral descriptor within the research community, outperforming all the existing descriptors, followed by the positive & negative inertia indices. The Estrada harmonic energy achieved the highest mean correlation coefficient of ρ>0.99. On the other hand, well-studied descriptors such as Laplacian energy and adjacency energy still maintained their effectiveness in correlating the thermodynamic properties of PAHs; however, they were significantly outperformed by the Estrada harmonic energy. The three best valency-spectral indices are employed in the structure–property modeling of thermodynamic properties of linear polyacenes.

Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey.

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.

A novel edge-weighted matrix of a graph and its spectral properties with potential applications

<p>Regarding a simple graph $ \Gamma $ possessing $ \nu $ vertices ($ \nu $-vertex graph) and $ m $ edges, the vertex-weight and weight of an edge $ e = uv $ are defined as $ w(v_{i}) = d_{ \Gamma}(v_{i}) $ and $ w(e) = d_{ \Gamma}(u)+d_{ \Gamma}(v)-2 $, where $ d_{ \Gamma}(v) $ is the degree of $ v $. This paper puts forward a novel graphical matrix named the edge-weighted adjacency matrix (adjacency of the vertices) $ A_{w}(\Gamma) $ of a graph $ \Gamma $ and is defined in such a way that, for any $ v_{i} $ that is adjacent to $ v_{j} $, its $ (i, j) $-entry equals $ w(e) = d_{ \Gamma}(v_{i})+d_{ \Gamma}(v_{j})-2 $; otherwise, it equals 0. The eigenvalues $ \lambda_{1}^{w}\ge \lambda_{2}^{w}\ge\ldots\ge \lambda_{\nu}^{w} $ of $ A_w $ are called the edge-weighted eigenvalues of $ \Gamma $. We investigate the mathematical properties of $ A_{w}(\Gamma) $'s spectral radius $ \lambda_{1}^{w} $ and energy $ E_{w}(\Gamma) = \sum_{i = 1}^{\nu}|\lambda_{i}^{w}| $. Sharp lower and upper bounds are obtained for $ \lambda_{1}^{w} $ and $ E_{w}(\Gamma) $, and the respective extremal graphs are characterized. Further, we employ these spectral descriptors in structure-property modeling of the physicochemical properties of polycyclic aromatic hydrocarbons for a set of benzenoid hydrocarbons (BHs). Detailed regression analysis showcases that edge-weighted energy outperforms classical adjacency energy in structure-property modeling of the physicochemical properties of BHs.</p>

Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning

The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of the data at simulation time. In this work, we leverage spectral descriptors to encode local atomic environments and build crystal structure classification models. In addition to the classical way spectral descriptors are computed, i.e. over a fixed radius neighborhood sphere around a central atom, we propose an extension to make them independent from the material’s density. Models are trained on defect-free crystal structures with moderate thermal noise and elastic deformation, using the linear discriminant analysis (LDA) method for dimensionality reduction and logistic regression (LR) for subsequent classification. The proposed classification model is intentionally designed to be simple, incorporating only a limited number of parameters. This deliberate simplicity enables the model to be trained effectively even when working with small databases. Despite the limited training data, the model still demonstrates inherent transferability, making it applicable to a broader range of scenarios and datasets. The accuracy of our models in extreme conditions (high temperature, high density, large deformation) is compared to traditional algorithms from the literature, namely adaptive common neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond structure identification (IDS). Finally, we showcase two applications of our method: tracking a solid–solid BCC-to-HCP phase transformation in Zirconium at high pressure up to high temperature, and visualizing stress-induced dislocation loop expansion in single crystal FCC Aluminum containing a Frank–Read source, at high temperature.

Machine Learning Descriptors for Data-Driven Catalysis Study.

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Machine learning of spectra-property relationship for imperfect and small chemistry data

Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.

An anisotropic Chebyshev descriptor and its optimization for deformable shape correspondence

Shape descriptors have recently gained popularity in shape matching, statistical shape modeling, etc. Their discriminative ability and efficiency play a decisive role in these tasks. In this paper, we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials, called the anisotropic Chebyshev descriptor (ACD); it can effectively capture shape features in multiple directions. The ACD inherits many good characteristics of spectral descriptors, such as being intrinsic, robust to changes in surface discretization, etc. Furthermore, due to the orthogonality of Chebyshev polynomials, the ACD is compact and can disambiguate intrinsic symmetry since several directions are considered. To improve the ACD’s discrimination ability, we construct a Chebyshev spectral manifold convolutional neural network (CSMCNN) that optimizes the ACD and produces a learned ACD. Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors. The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination, efficiency, and robustness to changes in shape resolution and discretization.

Characterization of triglycerides photooxidation under solar radiations: A stepwise Raman study.

Triglycerides (TGs) are one of the main components of the glycerolipid family. Their main task in cells is to store excess fatty acids. TG energy storage is mainly concentrated in adipocytes. TGs and free fatty acids constitute the majority (57.5%) of the skin surface lipids (SSLs). TGs are essential for the formation of the skin water barrier. This work is the second part of a global study that aims to evaluate the effect of solar radiations on SSLs using vibrational spectroscopy. In the first part of this work, a stepwise characterization of free fatty acids was performed, and different spectral descriptors were used to follow the different structural modifications during the photo-oxidation process, that is hydrogen abstraction, formation of hydroperoxides and peroxyl radicals as primary oxidation products and the formation of aldehydes, ketones, alcohol as secondary products. In this second part, the photo-oxidation of TGs was evaluated using Raman spectroscopy. A decrease in the CH2/CH3 stretching bands ratio that confirmed the hydrogen abstraction, an increase in the 1165/1740cm-1 ((δ(OH) and υ(C-O))/ν(C=O) (ester)) ratio indicated the formation of secondary oxidation products such as hydroperoxides. And finally, an increase in the 1725/1740cm-1 (υ(C=O) (ald.)/υ(C=O) (ester)) ratio and the trans ν(C=C)/cis ν(C=C) ratio highlighted the formation of aldehydes, alcohols, ketone, trans secondary products and others.

Probing the coverage of nanoparticles by biomimetic membranes through nanoplasmonics

Although promising for biomedicine, the clinical translation of inorganic nanoparticles (NPs) is limited by low biocompatibility and stability in biological fluids. A common strategy to circumvent this drawback consists in disguising the active inorganic core with a lipid bilayer coating, reminiscent of the structure of the cell membrane to redefine the chemical and biological identity of NPs. While recent reports introduced membrane-coating procedures for NPs, a robust and accessible method to quantify the integrity of the bilayer coverage is not yet available. To fill this gap, we prepared SiO2 nanoparticles (SiO2NPs) with different membrane coverage degrees and monitored their interaction with AuNPs by combining microscopic, scattering, and optical techniques. The membrane-coating on SiO2NPs induces spontaneous clustering of AuNPs, whose extent depends on the coating integrity. Remarkably, we discovered a linear correlation between the membrane coverage and a spectral descriptor for the AuNPs’ plasmonic resonance, spanning a wide range of coating yields. These results provide a fast and cost-effective assay to monitor the compatibilization of NPs with biological environments, essential for bench tests and scale-up. In addition, we introduce a robust and scalable method to prepare SiO2NPs/AuNPs hybrids through spontaneous self-assembly, with a high-fidelity structural control mediated by a lipid bilayer.

Far-Infrared Signatures for a Two-Step Pressure-Driven Metallization in Transition Metal Dichalcogenides

We present a high-pressure investigation of the semiconductor-to-metal transition in MoS2 and WS2 carried out by synchrotron-based far-infrared spectroscopy, to reconcile the controversial estimates of the metallization pressure found in the literature and gain new insight into the mechanisms ruling this electronic transition. Two spectral descriptors are found indicative of the onset of metallicity and of the origin of the free carriers in the metallic state: the absorbance spectral weight, whose abrupt increase defines the metallization pressure threshold, and the asymmetric line shape of the E1u peak, whose pressure evolution, interpreted within the Fano model, suggests the electrons in the metallic state originate from n-type doping levels. Combining our results with those reported in the literature, we hypothesize a two-step mechanism is at work in the metallization process, in which the pressure-induced hybridization between doping and conduction band states drives an early metallic behavior, while the band gap closes at higher pressures.

Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia

Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.

Unsupervised Feature Selection by a Genetic Algorithm for Mid-Infrared Spectral Data.

Dimensional reduction of highly multidimensional datasets such as those acquired by Fourier transform infrared spectroscopy (FTIR) is a critical step in the data analysis workflow. To achieve this goal, numerous feature selection methods have been developed and applied in a supervised context, i.e., using a priori knowledge about data usually in the form of labels for classification or quantitative values for regression. For this, genetic algorithms have been largely exploited due to their flexibility and global optimization principle. However, few applications in an unsupervised context have been reported in infrared spectroscopy. The aim of this article is to propose a new unsupervised feature selection method based on a genetic algorithm using a validity index computed from KMeans partitions as a fitness function. Evaluated on a simulated dataset and validated and tested on three real-world infrared spectroscopic datasets, our developed algorithm is able to find the spectral descriptors improving clustering accuracy and simplifying the spectral interpretation of results.

Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics.

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4-B1 phase transition of GaN under 50GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

Characterization of free fatty acids photo‐oxidation under UV radiations: A stepwise Raman study

AbstractLipid peroxidation (LPO) is an oxidative deterioration when oxidants such as reactive oxygen species attack lipids that contain double bonds especially polyunsaturated fatty acids (PUFAs). The mechanism of LPO is composed of three steps: initiation, propagation, and termination. It can result from several reaction pathways: (1) auto‐oxidation, (2) enzymatic oxidation, or (3) photo‐oxidation. UV solar exposition is a major concern in skin research, dermatology, and cosmetic science. Skin surface lipids (SSLs) and stratum corneum (SC) lipids are among the first skin components exposed to external insults such as UV radiations. Fatty acids represent a major lipid class in SSLs and SC. The aim of this paper is to study the photo‐oxidation of fatty acids under UV solar radiations using Raman spectroscopy. Vibrational spectroscopies are useful techniques to follow the LPO in its different stages of oxidation: (1) the initiation step by following the CH2/CH3 stretching bands ratio that decreases due to the hydrogen abstraction in alpha position; (2) the propagation step where primary oxidation products are formed such as hydroperoxides, which was evaluated by following the 1165/1640 cm−1 ((δ(OH) and ν(CO))/ν(CO)) ratio that increases during oxidation process; and finally, (3) the termination step that is marked by the formation of aldehydes, alcohols, ketone, trans secondary oxidation products, and others, which was monitored by following the 1730/1640 cm−1 (ν(CO) (ald.)/ν(CO) (acid)) ratio and the trans ν(CC)/cis ν(CC) ratio that increase indicating the formation of secondary oxidation products. To our knowledge, this work is the first to put together different spectral descriptors enabling to follow, step by step, the different modifications of free fatty acids structures during the oxidation process.