Feasibility of extracting a Σ−\Sigma^- admixture probability in the neutron-rich Λ10^{10}_{\Lambda}Li hypernucleus

We examine theoretically production of the neutron-rich $^{10}_{\Lambda}$Li hypernucleus by a double-charge exchange ($\pi^-$, $K^+$) reaction on a $^{10}$B target with distorted-wave impulse approximation calculations. The result shows that the magnitude and shape of the calculated spectrum at 1.20 GeV/c by a one-step mechanism $\pi^-p \to K^+ \Sigma^-$ via $\Sigma^-$ doorways caused by a $\Sigma^-p \leftrightarrow \Lambda n$ coupling can explain the recent experimental data, and the $\Sigma^-$ admixture probability in $^{10}_{\Lambda}$Li is found to be the order of 10$^{-1}$ %. The ($\pi^-$, $K^+$) reaction provides a capability of extracting properties of wave functions with $\Lambda$-$\Sigma$ coupling effects in neutron-rich nuclei, together with the reaction mechanism.Comment: 13 pages, 3 figure

Intelligent Phishing Detection Scheme Using Deep Learning Algorithms

Purpose: Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames. Design/methodology/approach: Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues. Findings: An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s. Originality/value: The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge

Design and Validation of a Bifunctional Ligand Display System for Receptor Targeting

AbstractHere we developed a bacteriophage display particle designed to serve as a bifunctional entity that can target tumors while delivering an agent. We engineered a chimera phage vector containing a pIII-displayed αv integrins-targeting moiety and a pVIII-displayed streptavidin binding adaptor moiety. By using the chimeric phage particle, targeting of αv integrins on cells in culture and tumor-related blood vessels was shown through different applications, including luminescent quantum dots localization, surface plasmon resonance-based binding detection, and an in vivo tumor model. The strategy validated here will accelerate the discovery and characterization of receptor-ligand binding events in high throughput, and cell-specific delivery of diagnostics or therapeutics to organs of choice without the need for chemical conjugation

Hyperonic mixing in five-baryon double-strangeness hypernuclei in a two-channel treatment

Properties of hypernuclei $_{\Lambda \Lambda}^5$H and $_{\Lambda \Lambda }^5$He are studied in a two-channel approach with explicit treatment of coupling of channels ^3\text{Z}+\Lambda+\Lambda and \alpha+\Xi. Diagonal \Lambda\Lambda and coupling \Lambda\Lambda-\Xi N interactions are derived within G-matrix procedure from Nijmegen meson-exchange models. Bond energy \Delta B_{\Lambda\Lambda} in $_{\Lambda \Lambda}^5$He exceeds significantly that in $_{\Lambda \Lambda}^5$H due to the channel coupling. Diagonal \Xi\alpha attraction amplifies the effect, which is sensitive also to \Lambda-core interaction. The difference of the \Delta B_{\Lambda\Lambda} values can be an unambiguous signature of the \Lambda\Lambda-\Xi N coupling in \Lambda\Lambda hypernuclei. However, improved knowledge of the hyperon-nucleus potentials is needed for quantitative extraction of the coupling strength from future data on the \Lambda\Lambda hypernuclear binding energies.Comment: 11 pages with 3 figures; Phys. Rev. C, accepte

The Λ\Lambda-Σ\Sigma coupling effect in the neutron-rich Λ\Lambda-hypernucleus Λ10_{\Lambda}^{10}Li by microscopic shell model

We investigate the structure of the neutron-rich $\Lambda$-hypernucleus $_{\Lambda}^{10}$Li by using microscopic shell-model calculations considering a $\Lambda$-$\Sigma$ coupling effect. The calculated $\Sigma$-mixing probability in the $_{\Lambda}^{10}$Li ground state is found to be about 0.34 % which is coherently enhanced by the $\Lambda$-$\Sigma$ coupling configurations, leading to the energy shift 0.28 MeV which is about 3 times larger than that in $_{\Lambda}^{7}$Li. The importance of the $\Sigma$ configuration obtained by the $\Sigma N$ interaction and the potentiality of the neutron-rich environment are discussed.Comment: 6 figure

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments

Mean-VaR portfolio optimization: A nonparametric approach

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice