Deexcitation nuclear gamma-ray line emission from low-energy cosmic rays in the inner Galaxy

Recent observations of high ionization rates of molecular hydrogen in diffuse interstellar clouds point to a distinct low-energy cosmic-ray component. Supposing that this component is made of nuclei, two models for the origin of such particles are explored and low-energy cosmic-ray spectra are calculated which, added to the standard cosmic ray spectra, produce the observed ionization rates. The clearest evidence of the presence of such low-energy nuclei between a few MeV per nucleon and several hundred MeV per nucleon in the interstellar medium would be a detection of nuclear \gamma-ray line emission in the range E_ 0.1 - 10 MeV, which is strongly produced in their collisions with the interstellar gas and dust. Using a recent \gamma-ray cross section compilation for nuclear collisions, \gamma-ray line emission spectra are calculated alongside with the high-energy \gamma-ray emission due to {\pi} 0 decay, the latter providing normalization of the absolute fluxes by comparison with Fermi-LAT observations of the diffuse emission above E \gamma = 0.1 GeV. Our predicted fluxes of strong nuclear \gamma-ray lines from the inner Galaxy are well below the detection sensitivies of INTEGRAL, but a detection, especially of the 4.4-MeV line, seems possible with new-generation \gamma-ray telescopes based on available technology. We predict also strong \gamma-ray continuum emission in the 1-8 MeV range, which in a large part of our model space for low-energy cosmic rays exceeds considerably estimated instrument sensitivities of future telescopes.Comment: 22 pages, 7 figures, accepted for publication in ApJ; figures 6 and 7 replace

Spectral Domain Approach Of Multilayered Superconducting Microstrip Line Using a New Set of Edge-Conditioned Basis Functions

A new set of edge-conditioned basis functions is used to determine the dispersion properties of microstrip line. Since An alternative formulation of the spectral domain approach (SDA) method presented for high-temperature superconducting microstrip line Green’s function derivation. The method relies on reflection factor rather than the transverse impedance used in the immittance approach. The inner products is involved in the Galerkin procedure are pole-free. The numerical examples are presented and close agreement is obtained between simulated and published data.A new set of edge-conditioned basis functions is used to determine the dispersion properties of microstrip line. Since An alternative formulation of the spectral domain approach (SDA) method presented for high-temperature superconducting microstrip line Green’s function derivation. The method relies on reflection factor rather than the transverse impedance used in the immittance approach. The inner products is involved in the Galerkin procedure are pole-free. The numerical examples are presented and close agreement is obtained between simulated and published data

SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation

A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models and datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. Experimental results obtained over image recognition datasets of varied natures show the efficiency of these new methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasksComment: The project is available at https://github.com/hammoudiproject/SuperpixelGridMask

A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models

Determining lymphoma subtypes is a crucial step for better patients treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which is based on gene expression technology, is highly expensive and time-consuming making difficult its accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. WSI (Whole Slide Image) analysis by deep learning models showed promising new directions for cancer diagnosis that would be cheaper and faster than existing alternative methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we propose a multi-modal architecture to train a classifier model from various WSI modalities. We then exploit this model through a knowledge distillation mechanism for efficiently driving the learning of a mono-modal classifier. Our experimental study conducted on a dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent methods from the state-of-the-art dedicated for cancer classification. Moreover, the power-law curve, estimated on our experimental data, shows that our classification model requires a reasonable number of additional patients for its training to potentially reach identical diagnosis accuracy as IHC technologies

Learning Boundary Edges for 3D-Mesh Segmentation

International audienceThis paper presents a 3D-mesh segmentation algorithm based on a learning approach. A large database of manually segmented 3D-meshes is used to learn a boundary edge function. The function is learned using a classifier which automatically selects from a pool of geometric features the most relevant ones to detect candidate boundary edges. We propose a processing pipeline that produces smooth closed boundaries using this edge function. This pipeline successively selects a set of candidate boundary contours, closes them and optimizes them using a snake movement. Our algorithm was evaluated quantitatively using two different segmentation benchmarks and was shown to outperform most recent algorithms from the state-of-the-art

Intensifying chitin hydrolysis by adjunct treatments – an overview

Chitin is, after cellulose, the most abundant organic natural polysaccharide on Earth, being synthesized as a dominant component in the exoskeletons of crustaceans, among other sources. In the processing of seafood for human consumption, between 40 and 50% of the total raw material mass is wasted, causing a significant problem for the environment due to its slow degradation. Efforts to find uses for chitin derivatives, particularly their oligomers, have intensified since these chemicals are highly functional and offer a wide range of applications, especially as antimicrobial agent. As a consequence, some adjunct treatments, either chemical or physical in nature, have been employed to assist acid and enzymatic hydrolysis. This work provides a detailed review of the methods employed to intensify the formation of chitin oligomers, particularly focusing on the adjunct treatments used (microwave, ultrasonication, steam explosion and gamma irradiation), and evaluate the yield and characteristics of the oligomers formed. Adjunct treatments are more suitable for enzymatic hydrolysis since these treatments modify the chitin structure, and enhance the hydrolysis rate and yield of the oligomers, under milder reaction conditions. For future research, it would be worth trying pre-treatments like the application of high-pressure to chitin in order to lower its crystallinity

Analyse et gestion de l’occupation de places de stationnement par vision artificielle

Cet article présente un système de surveillance basé sur la vision pour le développement de services de gestion de places de parking. Le système présenté est un système adaptable pour l'analyse de places de stationnement dans des parkings de différentes configurations. Dans ce but, des expérimentations ont été menées sous différentes prises de vue en utilisant une caméra connectée à une station de travail mobile. Les résultats obtenus montrent la faisabilité du système dans l'analyse et dans la gestion des emplacements de parking avec des véhicules

Gamma ray production cross sections in proton induced reactions on natural Mg, Si and Fe targets over the proton energy range 30 up to 66 MeV

Gamma-ray excitation functions have been measured for 30, 42, 54 and 66 MeV proton beams accelerated onto C + O (Mylar), Mg, Si, and Fe targets of astrophysical interest at the separate-sector cyclotron of iThemba LABS in Somerset West (Cape Town, South Africa). A large solid angle, high energy resolution detection system of the Eurogam type was used to record Gamma-ray energy spectra. Derived preliminary results of Gamma-ray line production cross sections for the Mg, Si and Fe target nuclei are reported and discussed. The current cross section data for known, intense Gamma-ray lines from these nuclei consistently extend to higher proton energies previous experimental data measured up to Ep ~ 25 MeV at the Orsay and Washington tandem accelerators. Data for new Gamma-ray lines observed for the first time in this work are also reported.Comment: 11 pages, 6 figures. IOP Institute of Physics Conference Nuclear Physics in Astrophysics VII, 28th EPF Nuclear Physics Divisional Conference, May 18-22 2015, York, U

Novel Correlations between Spectroscopic and Morphological Properties of Activated Carbons from Waste Coffee Grounds

Massive quantities of spent coffee grounds (SCGs) are generated by users around the world. Different processes have been proposed for SCG valorization, including pyrolytic processes to achieve carbonaceous materials. Here, we report the preparation of activated carbons through pyrolytic processes carried out under different experimental conditions and in the presence of various porosity activators. Textural and chemical characterization of the obtained carbons have been achieved through Brunauer–Emmett–Teller (BET), ESEM, 13C solid state NMR, XPS, XRD, thermogravimetric and spectroscopic determinations. The aim of the paper is to relate these data to the preparation method, evaluating the correlation between the spectroscopic data and the physical and textural properties, also in comparison with the corresponding data obtained for three commercial activated carbons used in industrial adsorption processes. Some correlations have been observed between the Raman and XPS data