New Headspace-Mass Spectrometry Method for the Discrimination of Commercial Gasoline Samples with Different Research Octane Numbers

A method for the discrimination of different gasoline samples according to their RON has been developed using an HS-MS system. The working conditions for the HS-MS analytical procedure were optimized by experimental design. The variables optimized were incubation temperature, incubation time, and sample volume. The optimal conditions were as follows: 145 °C incubation temperature, 10 min incubation time, and 80 μL sample volume. The optimized method was applied to a set of 30 gasoline samples with different RON values (95# and 98#). An hierarchical cluster analysis was applied in which the m/z (45−200 m/z) values were used as a variable to form groups. A perfect classification (100%) of the gasoline samples according to their RON was achieved. A linear discriminant analysis was carried out and the resulting linear discriminant function enabled a perfect classification of the gasoline samples according to the RON using only the m/z values of 88, 95, and 112. These results demonstrate the capacity of the new technique for the discrimination of gasoline samples according to their RON and the applicability of this method in this field. For the first time, HS-MS was used for this purpose. The main advantage of HS-MS vs previous methodologies is that no chromatographic separation and no sample manipulation are required. HS-MS is therefore faster than the current techniques used in these kinds of studies; it is also cheaper, ecofriendly, and easy to use for routine analysis

Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability

Image fusion combines data from different heterogeneous sources to obtain more precise information about an underlying scene. Hyperspectral-multispectral (HS-MS) image fusion is currently attracting great interest in remote sensing since it allows the generation of high spatial resolution HS images, circumventing the main limitation of this imaging modality. Existing HS-MS fusion algorithms, however, neglect the spectral variability often existing between images acquired at different time instants. This time difference causes variations in spectral signatures of the underlying constituent materials due to different acquisition and seasonal conditions. This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images. Simulations with synthetic and real data show that the proposed strategy leads to a significant performance improvement under spectral variability and state-of-the-art performance otherwise

Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples

In the quality control of flammable and combustible liquids, such as gasoline, both rapid analysis and automated data processing are of great importance from an economical viewpoint for the petroleum industry. The present work aims to evaluate the chemometric tools to be applied on the Headspace Mass Spectrometry (HS-MS eNose) and Near-Infrared Spectroscopy (NIRS) results to discriminate gasoline according to their Research Octane Number (RON). For this purpose, data from a total of 50 gasoline samples of two types of RON-95 and 98-analyzed by the two above-mentioned techniques were studied. The HS-MS eNose and NIRS data were com-bined with non-supervised exploratory techniques, such as Hierarchical Cluster Analysis (HCA), as well as other supervised classification techniques, namely Support Vector Machine (SVM) and Random Forest (RF). For su-pervised classification, the low-level data fusion was additionally applied to evaluate if the combined use of the data increases the scope of relevant information. The HCA results showed a clear clustering trend of the gasoline samples according to their RON with HS-MS eNose data. SVM in combination with 5-Fold Cross-Validation successfully classified 100% of the samples with the HS-MS eNose data set. The RF algorithm in combination with 5-Fold Cross-Validation achieved the best accuracy rate for the test set with the low-level data fusion system. Furthermore, it allowed us to identify the most important features that could define the differences between RON 95 and RON 98 gasoline. On the other hand, using the HS-MS eNose and NIRS low-level data fusion reached better results than those obtained using NIRS data individually, with accuracy rates of 100% in both SVM and RF performances with the test set. In general, the performance of the SVM and RF algorithms was found to be similar

Application of an HS–MS for the detection of ignitable liquids from fire debris

In arsonattacks,accelerantssuchasignitableliquidsarecommonlyusedtoinitiateoracceleratea fire. The detection of ignitable liquid residues at fire scenes is therefore a key step in fire investigations.The most widely used analytical technique for the analysis of accelerants is GC–MS. However,pre-concentration of the ignitable liquid residues is required prior to the chromatographic analysis.Thestandard method, ASTM E1412, involves passive headspace concentration with activated charcoal strips as a method to isolate the ignitable liquid residues from fire debris and these residues are subsequently desorbed from the carbon strip with solvents such as carbon disulfide. In the work described here, an alternative analytical technique based on an HS–MS (headspace mass spectrometry) has been developed for the thermal desorption of the carbon strips and analysis of different ignitable liquid residues in fire debris.The working conditions for the HS–MS analytical procedure were optimized using different types of fire debris (pine wood burned with gasoline and diesel). The optimized variables were desorption temperature and desorption time.The optimal conditions were 145 °C and 15 min. The optimized method was applied to a set of fire debris samples. In order to simulate post burn samples several accelerants (gasoline, diesel, citronella, kerosene, paraffin, and alcohol) were used to ignite different substrates (wood, cotton, cork, paper, and paperboard). chemometric methods (cluster analysis and discriminant analysis) were applied to the total ion spectrum obtained from the MS (45–200m/z) to discriminate between the burned samples according to the accelerant used. The method was validated by analyzing all samples by GC–MS according to the standard methods ASTM E1412 and ASTM E1618. The results obtained on using the method developed in this study were comparable to those obtained with the reference method. However, the newly developed HS–MS method is faster, safer, and more environmental friendly than the standard method

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution

The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remains challenging for this task. To this end, we propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet for short, to enhance the spatial resolution of HSI by means of higher-spatial-resolution multispectral image (MSI). Inspired by coupled spectral unmixing, a two-stream convolutional autoencoder framework is taken as backbone to jointly decompose MS and HS data into a spectrally meaningful basis and corresponding coefficients. CUCaNet is capable of adaptively learning spectral and spatial response functions from HS-MS correspondences by enforcing reasonable consistency assumptions on the networks. Moreover, a cross-attention module is devised to yield more effective spatial-spectral information transfer in networks. Extensive experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models, demonstrating the superiority of the CUCaNet in the HSI-SR application. Furthermore, the codes and datasets will be available at: https://github.com/danfenghong/ECCV2020_CUCaNet

ラット懸垂萎縮筋と懸垂中に伸張を与えたヒラメ筋の微小終板電位（m.e.p.p.）について

後肢懸垂による廃用性筋萎縮モデルラットを用い、(1)後肢懸垂に伴う神経筋伝達機能の変化と、(2)懸垂萎縮筋に対する筋伸張の影響を調べることを目的とした。後肢懸垂群（HS群）、後肢懸垂+筋伸張群（HS+MS群）、対照群（CONT群）の３群に対し、２週間の後肢懸垂後、ヒラメ筋-脛骨神経標本の微小終板電位（m.e.p.p.）を測定した。その結果、HS群とHS+MS群のm.e.p.p.立ち上がり時間は、CONT群に比べて有意に延長した（P<0.05）。また、HS群とHS+MS群のm.e.p.p.発生頻度は、CONT群に比べて有意に低下した（P<0.05）が、HS群とHS+MS群に有意差はなかった。後肢懸垂に伴うm.e.p.p.の変化については、シナプス間隙、acetylcholinesterase活性、神経終板領域との関連から考察した。懸垂萎縮筋に対して筋伸張の影響が見られなかった原因として、(1)筋萎縮防止の程度が不十分だった、(2)筋伸張はm.e.p.p.の維持に関与しない、の二点が可能性として考えられた。The purpose of this study was to investigate in rats whether neurotransmitter release was altered with hindlimb suspension, and to see how the muscle stretching affected disuse atrophy when precipitated by hindlimb suspension. The rats were divided into 3 groups: hindlimb suspension （HS）,hindlimb suspension+muscle stretching （HS+MS）and control（CONT）Miniature end plate potential （m.e.p.p.）was measured after 2 weeks of hindlimb suspension. M.e.p.p. rise time was significantly more prolonged in HS and HS+MS than in CONT （P<0.05）. The frequency of m.e.p.p.was significantly greater in HS and HS+MS than in CONT （P<0.05）, but was not significantly different between HS and HS+MS. The alterations of m.e.p.p. with hindlimb suspension were discussed from the point of view of synaptic gap junctions, activity of acetylcolinesterase, and end plate area. The reason why muscle stretching could not have affected m.e.p.p. was considered to be that muscle disuse atrophy was insufficiently prevented and that muscle stretching did not correlate with sustaining m.e.p.p.

Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning

Extensive attention has been widely paid to enhance the spatial resolution of hyperspectral (HS) images with the aid of multispectral (MS) images in remote sensing. However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images. Alternatively, we super-resolve MS images in the spectral domain by the means of partially overlapped HS images, yielding a novel and promising topic: spectral superresolution (SSR) of MS imagery. This is challenging and less investigated task due to its high ill-posedness in inverse imaging. To this end, we develop a simple but effective method, called joint sparse and low-rank learning (J-SLoL), to spectrally enhance MS images by jointly learning low-rank HS-MS dictionary pairs from overlapped regions. J-SLoL infers and recovers the unknown hyperspectral signals over a larger coverage by sparse coding on the learned dictionary pair. Furthermore, we validate the SSR performance on three HS-MS datasets (two for classification and one for unmixing) in terms of reconstruction, classification, and unmixing by comparing with several existing state-of-the-art baselines, showing the effectiveness and superiority of the proposed J-SLoL algorithm. Furthermore, the codes and datasets will be available at: https://github.com/danfenghong/IEEE\_TGRS\_J-SLoL, contributing to the RS community

Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion

In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques.Comment: Accepted in IEEE Transactions on Computational Imagin

A game-theoretic model of kleptoparasitic behavior in polymorphic populations

Kleptoparasitism, the stealing of food by one animal from another, is a widespread biological phenomenon. In this paper we build upon earlier models to investigate a population of conspecifics involved in foraging and, potentially, kleptoparasitism. We assume that the population is composed of four types of individuals, according to their strategic choices when faced with an opportunity to steal and to resist an attack. The fitness of each type of individual depends upon various natural parameters, for example food density, the handling time of a food item and the probability of mounting a successful attack against resistance, as well as the choices that they make. We find the evolutionarily stable strategies (ESSs) for all parameter combinations and show that there are six possible ESSs, four pure and two mixtures of two strategies, that can occur. We show that there is always at least one ESS, and sometimes two or three. We further investigate the influence of the different parameters on when each type of solution occurs

Long distance expansion for the NJL model with SU(3) and U_A(1) breaking

This work is a follow up of recent investigations, where we study the implications of a generalized heat kernel expansion, constructed to incorporate non-perturbatively the effects of a non-commutative quark mass matrix in a fully covariant way at each order of the expansion. As underlying Lagrangian we use the Nambu -- Jona-Lasinio model of QCD, with $SU_f(3)$ and $U_A(1)$ breaking, the latter generated by the 't Hooft flavour determinant interaction. The associated bosonized Lagrangian is derived in leading stationary phase approximation (SPA) and up to second order in the generalized heat kernel expansion. Its symmetry breaking pattern is shown to have a complex structure, involving all powers of the mesonic fields allowed by symmetry. The considered Lagrangian yields a reliable playground for the study of the implications of symmetry and vacuum structure on the mesonic spectra, which we evaluate for the scalar and pseudoscalar meson nonets and compare with other approaches and experiment.Comment: LaTeX, 30 pages, added discussions and references, title change, version to appear in Nucl. Phys.