Strong Λπ\Lambda \pi Phase Shifts for CP Violation in Weak Ξ→Λπ\Xi \rightarrow \Lambda \pi Decay

Strong interaction $\Lambda\pi$ phase shifts relevant for the weak nonleptonic decay $\Xi \rightarrow \Lambda \pi$ are calculated using baryon chiral perturbation theory. We find in leading order that the S-wave phase shift vanishes and the $J={1 \over 2}$ P-wave phase shift is $-1.7 ^{\rm o}$. The small phase shifts imply that CP violation in this decay will be difficult to observe. Our results follow from chiral $SU(2)_L\times SU(2)_R$ symmetry.Comment: 8 pages, uses phyzzx, 2 figures included as uuencoded file, CALT-68-1940 and CMU-HEP94-2

S-wave phase shift in Lambda-pi scattering

We calculate the s-wave strong interaction $\Lambda$$\pi$ phase shift at the $\Xi$ mass taking into account contributions from the ${{\frac{1}{2}}}^{-}$ and ${{\frac{3}{2}}}^{-}$ $\Sigma$ resonances. We find the S-wave phase shift to be small, of the order of 0.3 degrees and bounded by 0.5 degrees.Comment: 4 page

Chiral Perturbation Theory for |Delta I|=3/2 Hyperon Decays

We study the |Delta I|=3/2 amplitudes of hyperon non-leptonic decays of the form B->B'+pi in the context of chiral perturbation theory. The lowest-order predictions are determined in terms of only one unknown parameter and are consistent within errors with current data. We investigate the theoretical uncertainty of these predictions by calculating the leading non-analytic corrections. We also present an estimate for the size of the S-wave Lambda and Cascade decays which vanish at leading order. We find that the corrections to the lowest-order predictions are within the expectations of naive power counting and, therefore, that this picture can be tested more accurately with improved measurements.Comment: 22 pages, latex, 4 figures, uses axodraw.sty, few typos correcte

Prediction of earnings per share for industry

Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance

Hyperon Nonleptonic Decays in Chiral Perturbation Theory Reexamined

We recalculate the leading nonanalytic contributions to the amplitudes for hyperon nonleptonic decays in chiral perturbation theory. Our results partially disagree with those calculated before, and include new terms previously omitted in the P-wave amplitudes. Although these modifications are numerically significant, they do not change the well-known fact that good agreement with experiment cannot be simultaneously achieved using one-loop S- and P-wave amplitudes.Comment: 14 pages, latex, 3 figures, uses axodraw.sty, minor additions, to appear in Phys. Rev.

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Delta I=3/2 and Delta S =2 Hyperon Decays in Chiral Perturbation Theory

We study the Delta I =3/2 and Delta S =2 amplitudes for hyperon decays of the form (B to B(prime) pion) at lowest order in chiral perturbation theory. At this order, the Delta I=3/2 amplitudes depend on only one constant. We extract the value of this constant from experiment and find a reasonable description of these processes within experimental errors. The same constant determines the Delta S =2 transitions which, in the standard model, are too small to be observed. We find that new physics with parity odd Delta S=2 interactions can produce observable rates in hyperon decays while evading the bounds from K0 - K0(bar) mixing.Comment: 7 pages, Late

Impacts of wildfire and landscape factors on organic soil properties in Arctic tussock tundra

Tundra ecosystems contain some of the largest stores of soil organic carbon among all biomes worldwide. Wildfire, the primary disturbance agent in Arctic tundra, is likely to impact soil properties in ways that enable carbon release and modify ecosystem functioning more broadly through impacts on organic soils, based on evidence from a recent extreme Anaktuvuk River Fire (ARF). However, comparatively little is known about the long-term impacts of typical tundra fires that are short-lived and transient. Here we quantitatively investigated how these transient tundra fires and other landscape factors affected organic soil properties, including soil organic layer (SOL) thickness, soil temperature, and soil moisture, in the tussock tundra. We examined extensive field observations collected from nearly 200 plots across a wide range of fire-impacted tundra regions in AK within the scope of NASA\u27s Arctic Boreal Vulnerability Experiment. We found an overall shallower SOL in our field regions (∼15 cm on average) compared to areas with no known fire record or the ARF (∼20 cm or thicker), suggesting that estimations based on evidence from the extreme ARF event could result in gross overestimation of soil organic carbon (SOC) stock and fire impacts across the tundra. Typical tundra fires could be too short-lived to result in substantial SOL consumption and yield less robust results of SOL and carbon storage. Yet, repeated fires may amount to a larger amount of SOC loss than one single severe burning. As expected, our study showed that wildfire could affect soil moisture and temperature in the tussock tundra over decades after the fire, with drier and warmer soils found to be associated with more frequent and severe burnings. Soil temperature was also associated with vegetation cover and air temperature

Hydrodynamics of thermal granular convection

A hydrodynamic theory is formulated for buoyancy-driven ("thermal") granular convection, recently predicted in molecular dynamic simulations and observed in experiment. The limit of a dilute flow is considered. The problem is fully described by three scaled parameters. The convection occurs via a supercritical bifurcation, the inelasticity of the collisions being the control parameter. The theory is expected to be valid for small Knudsen numbers and nearly elastic grain collisions.Comment: 4 pages, 4 EPS figures, some details adde