A simultaneous explanation of the large phase in B_s-\bar B_s mixing and B --> \pi\pi / \pi K puzzles in R-parity violating supersymmetry

Recent data on $B$ meson mixings and decays are, in general, in accord with the standard model expectations, except showing a few hiccups: (i) a large phase in $B_s$ mixing, (ii) a significant difference ($> 3.5 \sigma$) between CP-asymmetries in $B^\pm \to \pi^0 K^\pm$ and $B_d \to \pi^\mp K^\pm$ channels, and (iii) a larger than expected branching ratio in $B_d \to \pi^0\pi^0$ channel. We show that selective baryon number violating Yukawa couplings in R-parity violating supersymmetry can reconcile all the measurements.Comment: 8 pages, 4 eps figs; v2: minor errors corrected, fig.2a redrawn with correct y-axis labels, footnote on updated UTfit result on Bs mixing phase added, References and Acknowledgements sections update

Distribution of mosquito larvae in rice field habitats: A spatial scale analysis in semi-field condition

The distribution of the mosquito larvae in the breeding habitats varies at the spatial scale depending on the availability of the resources and the predators. This proposition was assessed through the observation of the spatial distribution of Culex larvae (Culex tritaeniorhynchus) in artificially constructed rice field habitats. Using a binomial generalized linear model with logit link, the disparity in the abundance of the larvae was evaluated to justify the effects of light (light vs shade), vertical (surface vs bottom), and horizontal (wall vs center) distribution as explanatory variables. Under light availability, the spatial occupancy of the mosquito larvae was higher in the center than in the walls of the mesocosms. However, the larval orientation was higher on the surface than at the bottom of the mesocosms in all instances. In comparison to open spaces, the larval aggregation was higher in the presence of the floating vegetations like Azolla and Lemna, indicating that the habitat heterogeneity of the mesocosms influenced the distribution of the mosquito larvae in the available spaces. A reduction in the larval aggregation pattern in the spaces was observed in the presence of the predator (Anisops sp.) reflecting the possible evasion tactics of the mosquito larvae. The observations suggest that the mosquito larvae may utilize the vegetation in the rice field habitats quite effectively and occupy empty spaces of predators. The results may be considered as a prototype of the prospective localization of the mosquito larvae in the rice fields and help to frame the strategies of spraying the biopesticides to achieve optimal efficacy in mosquito regulation

Evaluation of analgesic activity of methanolic extract of bougainvillea spectabilis leaves in experimental animal models

Background: Anti-inflammatory activity of leaves of Bougainvillea spectabilis (family Nyctaginaceae) has already been demonstrated in experimental animals. As pain is one of the important components of inflammation, we had set forward a study this find out possible analgesic activity of the same in animal models Objective: Evaluation of analgesic effects of, Bougainvillea spectabilis in mice models. Methods: 215 gm of fresh dried leaves of Bougainvillea spectabilis (BS) were collected from the local area during the flowering season and air dried. Following Methanol extraction, under reduced pressure solvent was removed on a rotary evaporator. The lyophilized extract was collected and the yield was 8 gm. That was used as an emulsion prepared in propylene glycol and orally administered (20 and 50 mg/kg). Central and peripheral analgesic activities of Bougainvillea spectabilis (BS) were evaluated by tail flick, tail immersion test and writhing test (acetic acid induced) respectively. Study Design: This is an experimental study designed on animal models. Results: Bougainvillea spectabilis (BS) had shown no analgesic action in central anal gesic model at different hours as the reaction time was less than 10 seconds at all time interval. With regard to peripheral analgesic activity, maximal activity was observed at 50 mg/kg b.w. The mean writhes ± standard deviation were 42.7±0.9 and 40±0.5 respectively in BS (20 mg/kg) and BS (50 mg/kg) in comparison to standard drug aspirin (33.3±0.4), control mice being 55.3±0.4. Conclusion: Our data indicates that Bougainvillea spectabilis (50 mg/kg) has a significant peripheral analgesic activity. Without isolating the active principles it's extremely difficult to pinpoint the mechanisms contributing to the observed analgesic activities of Bougainvillea spectabilis and extrapolate that in clinical practice

The Reads-From Equivalence for the TSO and PSO Memory Models

The verification of concurrent programs remains an open challenge due to the non-determinism in inter-process communication. Instead of exploring concrete executions, stateless model-checking (SMC) techniques partition the execution space into equivalence classes, and explore each class as opposed to each execution. For the relaxed memory models of TSO and PSO (total/partial store order), the standard equivalence has been Shasha-Snir traces, seen as an extension of the classic Mazurkiewicz equivalence from SC (sequential consistency) to TSO and PSO. The reads-from (RF) equivalence was recently shown to be coarser than the Mazurkiewicz equivalence, leading to impressive scalability improvements for SMC under SC. The generalization of RF to TSO and PSO requires to overcome two challenges, namely, verifying execution consistency and SMC algorithm. We address these two fundamental problems in this work. Our first set of contributions is on the problem of verifying TSO- and PSO-consistent executions given a reads-from map, VTSO-rf and VPSO-rf, respectively. The problem has been heavily studied under SC due to its numerous applications, but little is known for TSO and PSO. For an execution of $n$ events over $k$ threads and $d$ variables, we establish novel bounds that scale as $n^{k+1}$ for TSO and as $n^{k+1}\cdot \min(n^{k^2}, 2^{k\cdot d})$ for PSO. Our second contribution is an algorithm for SMC under TSO and PSO using the RF equivalence. Our algorithm is exploration-optimal, in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when $k$ is bounded. Our experimental evaluation shows that the RF equivalence is often exponentially coarser than Shasha-Snir traces, and our SMC algorithm scales much better than state-of-the-art tools based on Shasha-Snir traces

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization