Relativistic electron in curved magnetic fields

Making use of the perturbation method based on the nonlinear differential equation theory, the author investigates the classical motion of a relativistic electron in a class of curved magnetic fields which may be written as B=B(O,B sub phi, O) in cylindrical coordinates (R. phi, Z). Under general astrophysical conditions the author derives the analytical expressions of the motion orbit, pitch angle, etc., of the electron in their dependence upon parameters characterizing the magnetic field and electron. The effects of non-zero curvature of magnetic field lines on the motion of electrons and applicabilities of these results to astrophysics are also discussed

Strangeness magnetic form factor of the proton in the extended chiral quark model

Background: Unravelling the role played by nonvalence flavors in baryons is crucial in deepening our comprehension of QCD. Strange quark, a component of the higher Fock states in baryons, is an appropriate tool to investigate nonperturbative mechanisms generated by the pure sea quark. Purpose: Study the magnitude and the sign of the strangeness magnetic moment $\mu_s$ and the magnetic form factor ($G_M^s$) of the proton. Methods: Within an extended chiral constituent quark model, we investigate contributions from all possible five-quark components to $\mu_s$ and $G_M^s (Q^2)$ in the four-vector momentum range $Q^2 \leq 1$ (GeV/c)$^2$. Probability of the strangeness component in the proton wave function is calculated employing the $^3 P_0$ model. Results: Predictions are obtained without any adjustable parameters. Observables $\mu_s$ and $G_M^s (Q^2)$ are found to be small and negative, consistent with the lattice-QCD findings as well as with the latest data released by the PVA4 and HAPPEX Collaborations. Conclusions: Due to sizeable cancelations among different configurations contributing to the strangeness magnetic moment of the proton, it is indispensable to (i) take into account all relevant five-quark components and include both diagonal and non-diagonal terms, (ii) handle with care the oscillator harmonic parameter $\omega_5$ and the ${s \bar s}$ component probability.Comment: References added, typos corrected, accepted for publication by Phys. Rev.

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Generalized Jack polynomials and the AGT relations for the SU(3)SU(3) group

We find generalized Jack polynomials for the group $SU(3)$ and verify that their Selberg averages for several first levels are given by Nekrasov functions. To compute the averages we derive recurrence relations for the $sl_3$ Selberg integrals.Comment: 6 pages, refs added, typos corrected, published versio

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

Interaction-induced localization of mobile impurities in ultracold systems

The impurities, introduced intentionally or accidentally into certain materials, can significantly modify their characteristics or reveal their intrinsic physical properties, and thus play an important role in solid-state physics. Different from those static impurities in a solid, the impurities realized in cold atomic systems are naturally mobile. Here we propose an effective theory for treating some unique behaviors exhibited by ultracold mobile impurities. Our theory reveals the interaction-induced transition between the extended and localized impurity states, and also explains the essential features obtained from several previous models in a unified way. Based on our theory, we predict many intriguing phenomena in ultracold systems associated with the extended and localized impurities, including the formation of the impurity-molecules and impurity-lattices. We hope this investigation can open up a new avenue for the future studies on ultracold mobile impurities.Comment: 16 pages, 4 figure

The effects of leaf litter treatments, post-harvest urea and omission of early season fungicide sprays on the overwintering of apple scab on Bramley’s Seedling grown in a maritime environment.

peer-reviewedThe theory that orchards with zero or low levels of apple scab post harvest do not need scab protection at the start of the next growing season was evaluated under Irish conditions. In addition, a range of post-harvest orchard sanitation practices (application of urea to rot overwintering leaves, mowing the orchard or total leaf removal in February) were also evaluated. Due to the high summer rainfall in Ireland (compared to all other European apple growing areas) and the severe susceptibility of the apple cultivar Bramley’s Seedling to scab (Venturia inaequalis), neither clean orchards in the autumn nor sanitation practices were sufficient to eliminate the requirement for full fungicide protection programmes at the start of the following growing season. Post harvest applications of urea proved difficult due to late harvesting of pollinator fruit for the juice market and wet weather. Total removal of leaf litter from plots prior to the commencement of growth did not significantly reduce disease incidence. Regardless of orchard cleanliness in autumn, missing the first fungicide application in the spring always reduced yield.This work was funded by the Department of Agriculture and Rural Development, Northern Ireland

Two in one? A possible dual radio-emitting nucleus in the quasar SDSS J1425+3231

The radio-emitting quasar SDSS J1425+3231 (z=0.478) was recently found to have double-peaked narrow [O III] optical emission lines. Based on the analysis of the optical spectrum, Peng et al. (2011) suggested that this object harbours a dual active galactic nucleus (AGN) system, with two supermassive black holes (SMBHs) separated on the kpc scale. SMBH pairs should be ubiquitous according to hierarchical galaxy formation scenarios in which the host galaxies and their central black holes grow together via interactions and eventual mergers. Yet the number of presently-confirmed dual SMBHs on kpc or smaller scales remains small. A possible way to obtain direct observational evidence for duality is to conduct high-resolution radio interferometric measurements, provided that both AGN are in an evolutionary phase when some activity is going on in the radio. We used the technique of Very Long Baseline Interferometry (VLBI) to image SDSS J1425+3231. Observations made with the European VLBI Network (EVN) at 1.7 GHz and 5 GHz frequencies in 2011 revealed compact radio emission at sub-mJy flux density levels from two components with a projected linear separation of \sim2.6 kpc. These two components support the possibility of a dual AGN system. The weaker component remained undetected at 5 GHz, due to its steep radio spectrum. Further study will be necessary to securely rule out a jet--shock interpretation of the less dominant compact radio source. Assuming the dual AGN interpretation, we discuss black hole masses, luminosities, and accretion rates of the two components, using available X-ray, optical, and radio data. While high-resolution radio interferometric imaging is not an efficient technique to search blindly for dual AGN, it is an invaluable tool to confirm the existence of selected candidates.Comment: 7 pages, 2 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Societ