Revisit of tensor-meson nonet in resonance chiral theory

We study the properties of the lowest multiplet of light-flavor tensor meson resonances, i.e. $f_2(1270)$, $a_2(1320)$, $K_2^*(1430)$, and $f_2'(1525)$, within the resonance chiral theory approach. The higher-order resonance chiral operators, including the light-quark mass and $1/N_C$ corrections, are simultaneously incorporated in our study. The use of resonance chiral expressions allows us to analyze not only the relevant experimental data but also in the meantime the lattice results at unphysical quark masses, including the masses of the lowest multiplet of tensor resonances and their decay widths into two pseudoscalar mesons. In addition, the radiative decays of the tensor resonances into one photon plus one pseudoscalar meson and two photons are also studied.Comment: 18 pages, 3 tables, 3 figures. To match the published versio

Significant Inhibition of Tumor Growth following Single Dose Nanoparticle-Enhanced Photodynamic Therapy

Photodynamic therapy (PDT) for cancer treatment involves the pathology’s uptake of photosensitizers, which produce cytotoxic reactive oxygen species by photoirradiation. The use of nanoparticles as carriers of photosensitizers is one promising approach to this endeavor, owing to their small size, unique physicochemical properties, and easy/diverse functionalization. In the current work, we report on the in vivo assessment of PDT efficacy of these nanoconstructs in a murine model of human breast cancer, following a single (one-shot) nanoparticle dose and photoirradiation. Palladium-porphyrin (PdTPP) was administered intratumorally via injection of aqueous suspensions of either free PdTPP or MSN-conjugated PdTPP (MSN-PdTPP) at a dose of 50 μg. Mice were then exposed to a single photoirradiation session with total energy of 80 J. One month after one-shot PDT treatment, significantly greater reductions in tumor growth were observed in MSN-Pd treated animals than in PdTPP cohorts. Electron microscopy of tumor specimens harvested at various timepoints revealed excellent MSN-PdTPP uptake by cancer cells while immunohistologic analysis demonstrated marked increases in apoptotic response of MSN-PdTPP treated animals relative to PdTPP controls. Taken together, these findings suggest that considerable improvements in PDT efficacy can readily be achieved via the use of nanoparticle-based photosensitizers

Entanglement Structure: Entanglement Partitioning in Multipartite Systems and Its Experimental Detection Using Optimizable Witnesses

Creating large-scale entanglement lies at the heart of many quantum information processing protocols and the investigation of fundamental physics. For multipartite quantum systems, it is crucial to identify not only the presence of entanglement but also its detailed structure. This is because in a generic experimental situation with sufficiently many subsystems involved, the production of so-called genuine multipartite entanglement remains a formidable challenge. Consequently, focusing exclusively on the identification of this strongest type of entanglement may result in an all or nothing situation where some inherently quantum aspects of the resource are overlooked. On the contrary, even if the system is not genuinely multipartite entangled, there may still be many-body entanglement present in the system. An identification of the entanglement structure may thus provide us with a hint about where imperfections in the setup may occur, as well as where we can identify groups of subsystems that can still exhibit strong quantum-information-processing capabilities. However, there is no known efficient methods to identify the underlying entanglement structure. Here, we propose two complementary families of witnesses for the identification of such structures. They are based on the detection of entanglement intactness and entanglement depth, each requires only the implementation of solely two local measurements. Our method is also robust against noises and other imperfections, as reflected by our experimental implementation of these tools to verify the entanglement structure of five different eight-photon entangled states. We demonstrate how their entanglement structure can be precisely and systematically inferred from the experimental data. In achieving this goal, we also illustrate how the same set of data can be classically postprocessed to learn the most about the measured system.Comment: 21 pages, 13 figure

Temperature Swing Adsorption Process for CO2 Capture Using Polyaniline Solid Sorbent

AbstractTo capture carbon dioxide from power plant flue gas which consists of 15% CO2 and 85% N2, with a temperature swing adsorption (TSA) by using polyaniline solid sorbent as the adsorbent, is explored experimentally and theoretically. First, single component adsorption equilibrium data of carbon dioxide on polyaniline solid sorbent is obtained by using Micro-Balance Thermo D-200. Then isotherm curves and the parameters are obtained by numerical method. The adsorption is expressed by the Langmuir-Freundlich isotherm. After accomplishment of isotherm curves, the breakthrough curve experiment is investigated with single adsorption column. The experiments test the change in adsorbed gas concentration at the outlet by adsorbed gas, CO2, and non-adsorbed gas, helium. Finally, this study accentuates the TSA experiments on CO2 purity and recovery by operation variable discussion which includes feed pressure, adsorption temperature and desorption temperature to find optimal operation condition. The results of optimal operation condition are CO2 purity of 47.65% with a 92.46% recovery

(Z)-2-[(2-Hydr­oxy-1-naphth­yl)methyl­eneamino]benzonitrile

The title compound, C18H12N2O, crystallizes in a phenol–imine tautomeric form with a Z conformation for the imine functionality. The dihedral angle between the aromatic rings is 8.98 (9)°. A strong intra­molecular O—H⋯N hydrogen-bond inter­action between the hydroxyl group and imine N atom occurs

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201