Transition probabilities and radiative lifetimes of levels in F I

The electric dipole transition probabilities and the lifetimes of excited levels have been calculated using the weakest bound electron potential model theory (WBEPMT) and the quantum defect orbital theory (QDOT) in atomic fluorine. In the calculations, many of transition arrays included both multiplet and fine-structure transitions are considered. We employed Numerical Coulomb Approximation (NCA) wave functions and numerical non-relativistic Hartree-Fock (NRHF) wave functions for expectation values of radii in determination of parameters. The necessary energy values have been taken from experimental energy data in the literature. The calculated transition probabilities and lifetimes have been compared with available theoretical and experimental results. A good agreement with results in literature has been obtained. Moreover, some transition probability and the lifetime values not existing in the literature for some highly excited levels have been obtained using these methods.     

Oscillator strengths for Be I

The electric dipole oscillator strengths for lines between some singlet and triplet levels have been calculated using the weakest bound electron potential model theory and the quantum defect orbital theory for Be I. In the calculations both multiplet and fine structure transitions are studied. We employed both the numerical Coulomb approximation method and numerical non-relativistic Hartree-Fock wavefunctions for expectation values of radii. The necessary energy values have been taken from experimental energy data in the literature. The calculated oscillator strengths have been compared with available theoretical results. A good agreement with the results in the literature has been obtained.     

Systeme Ga2S3FeS — Diagramme de phase — etude cristallographique

The system was studied by DTA, X-ray diffraction and in particular by the Guinier-Lenné method. Eight intermediate phases are described. 1) Three solid solutions in the Ga₂S₃ region : a) a wurtzite-type solid solution (T > 1000° C ; 0.05 < n < 0.23) (n = Fe $\text{at}\text{Fe}$ at+Ga at), b) a blende-type solid solution (700°C < T < 980°C ; 0.10 < n < 0.20) with many stacking faults, c) a non-stoichiometric hexagonal phase α'Ga₂S₃ type (T < 700°C, 0.07 < n < 0.12). 2) for n = 0.20 and T = 540°C a superstructure of orthorhombic wurtzite-like array FeGa₄S₇. 3) From n = 0.20 through n = 0.27, a non stoichiometric superstructure of tetragonal-like array has the CdGa₂S₄ type (600°C < T < 1030°C). 4) For n = 0.33, the FeGa₂S₄ has two forms depending from the temperature : a) a low temperature form is trigonal ; b) a high temperature form is orthorhombic type ZnAl₂S₄. Transition temperature is 1010°C. 5) For n = 0.50, Fe₂Ga₂S₅ polytypes : a) Fe₂Ga₂S₅α 1T type Mn₂Ga₂S₅ ; b) Fe₂Ga₂S₅ 2H ; c) Fe₂Ga₂S₅ 3R type Mn₂Al₂Se₅. A phase diagram is proposed.     

Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification

Analysis of high dimensional data in modern applications, such as neuroscience, text mining, spectral analysis, chemometrices naturally requires tensor decomposition methods. The Tucker decompositions allow us to extract hidden factors (component matrices) with different dimension in each mode, and investigate interactions among various modalities. The alternating least squares (ALS) algorithms have been confirmed effective and efficient in most of tensor decompositions, especially Tucker with orthogonality constraints. However, for nonnegative Tucker decomposition (NTD), standard ALS algorithms suffer from unstable convergence properties, demand high computational cost for large scale problems due to matrix inverse, and often return suboptimal solutions. Moreover they are quite sensitive with respect to noise, and can be relatively slow in the special case when data are nearly collinear. In this paper, we propose a new algorithm for nonnegative Tucker decomposition based on constrained minimization of a set of local cost functions and hierarchical alternating least squares (HALS). The developed NTD-HALS algorithm sequentially updates components, hence avoids matrix inverse, and is suitable for large-scale problems. The proposed algorithm is also regularized with additional constraint terms such as sparseness, orthogonality, smoothness, and especially discriminant. Extensive experiments confirm the validity and higher performance of the developed algorithm in comparison with other existing algorithms.     

Querying and creating visualizations by analogy

While there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system.     

Ultrawideband circularly polarized antenna with dual band‐notched characteristics

This article presents an ultrawideband (UWB) crossed dipole antenna with circularly polarized (CP) and dual band‐notched characteristics. The proposed design is based on two orthogonal tapered dipoles for UWB CP operation and a square‐shaped cavity for high broadside gain over the entire operating bandwidth (BW). To generate dual band‐notched characteristics, two separated slots are inserted into each dipole's arm. This antenna yields measured impedance BW of 100% (3.2‐9.6 GHz) with dual‐band rejection centered at 5.2 and 5.8 GHz. Correspondingly, dual rejected bands are also observed in the original UWB CP band, which ranges from 3.2 to 8.2 GHz. Additionally, the proposed antenna exhibits high broadside gains better than 6.2 dBic and radiation efficiency greater than 82%.     

Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

Solving distribution problems in content-based recommendation system with gaussian mixture model

Applied Intelligence - Recommendation systems play an important role in boosting purchasing consumption for many manufacturers by helping consumers find the most appropriate items. Furthermore,...