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

Anti-malarial landscape in Myanmar: results from a nationally representative survey among community health workers and the private sector outlets in 2015/2016

Abstract Background In 2015/2016, an ACTwatch outlet survey was implemented to assess the anti-malarial and malaria testing landscape in Myanmar across four domains (Eastern, Central, Coastal, Western regions). Indicators provide an important benchmark to guide Myanmar’s new National Strategic Plan to eliminate malaria by 2030. Methods This was a cross-sectional survey, which employed stratified cluster-random sampling across four regions in Myanmar. A census of community health workers (CHWs) and private outlets with potential to distribute malaria testing and/or treatment was conducted. An audit was completed for all anti-malarials, malaria rapid diagnostic tests. Results A total of 28,664 outlets were approached and 4416 met the screening criteria. The anti-malarial market composition comprised CHWs (41.5%), general retailers (27.9%), itinerant drug vendors (11.8%), pharmacies (10.9%), and private for-profit facilities (7.9%). Availability of different anti-malarials and diagnostic testing among anti-malarial-stocking CHWs was as follows: artemisinin-based combination therapy (ACT) (81.3%), chloroquine (67.0%), confirmatory malaria test (77.7%). Less than half of the anti-malarial-stocking private sector had first-line treatment in stock: ACT (41.7%) chloroquine (41.8%), and malaria diagnostic testing was rare (15.4%). Oral artemisinin monotherapy (AMT) was available in 27.7% of private sector outlets (Western, 54.1%; Central, 31.4%; Eastern; 25.0%, Coastal; 15.4%). The private-sector anti-malarial market share comprised ACT (44.0%), chloroquine (26.6%), and oral AMT (19.6%). Among CHW the market share was ACT (71.6%), chloroquine (22.3%); oral AMT (3.8%). More than half of CHWs could correctly state the national first-line treatment for uncomplicated falciparum and vivax malaria (59.2 and 56.9%, respectively) compared to the private sector (15.8 and 13.2%, respectively). Indicators on support and engagement were as follows for CHWs: reportedly received training on malaria diagnosis (60.7%) or national malaria treatment guidelines (59.6%), received a supervisory or regulatory visit within 12 months (39.1%), kept records on number of patients tested or treated for malaria (77.3%). These indicators were less than 20% across the private sector. Conclusion CHWs have a strong foundation for achieving malaria goals and their scale-up is merited, however gaps in malaria commodities and supplies must be addressed. Intensified private sector strategies are urgently needed and must be scaled up to improve access and coverage of first-line treatments and malaria diagnosis, and remove oral AMT from the market place. Future policies and interventions on malaria control and elimination in Myanmar should take these findings into consideration across all phases of implementation