Bacteriophages as a model for studying carbon regulation in aquatic system

The interconversion of carbon in organic, inorganic and refractory carbon is still beyond the grasp of present environmentalists. The bacteria and their phages, being the most abundant constituents of the aquatic environment, represent an ideal model for studing carbon regulation in the aquatic system. The refractory dissolved organic carbon (DOC), a recently coined terminology from the microbe-driven conversion of bioavailable organic carbon into difficult-to-digest refractory DOC by microbial carbon pump (MCP), is suggested to have the potential to revolutionize our view of carbon sequestration. It is estimated that about 95% of organic carbon is in the form of refractory DOC, which is the largest pool of organic matter in the ocean. The refractory DOC is supposed to be the major factor in the global carbon cycle whose source is not yet well understood. A key element of the carbon cycle is the microbial conversion of dissolved organic carbon into inedible forms. The time studies of phage-host interaction under control conditions reveal their impact on the total carbon content of the source and their interconversion among organic, inorganic and other forms of carbon with respect to control source. The TOC- analysis statistics stipulate an increase in inorganic carbon content by 15-25 percent in the sample with phage as compared to the sample without phage. The results signify a 60-70 fold increase in inorganic carbon content in sample with phage, whereas, 50-55 fold in the case of sample without phages as compared with control. This increase in inorganic carbon content may be due to lysis of the host cell releasing its cellular constituents and utilization of carbon constituent for phage assembly and development. It also proves the role of phages in regulating the carbon flow in aquatic systems like oceans, where their concentration outnumbered other species

Non-classical photon pair generation in atomic vapours

A scheme for the generation of non-classical pairs of photons in atomic vapours is proposed. The scheme exploits the fact that the cross correlation of the emission of photons from the extreme transitions of a four-level cascade system shows anti-bunching which has not been reported earlier and which is unlike the case of the three level cascade emission which shows bunching. The Cauchy-Schwarz inequality which is the ratio of cross-correlation to the auto correlation function in this case is estimated to be $10^3-10^6$ for controllable time delay, and is one to four orders of magnitude larger compared to previous experiments. The choice of Doppler free geometry in addition to the fact that at three photon resonance the excitation/deexcitation processes occur in a very narrow frequency band, ensures cleaner signals.Comment: 18 pages, 7 figure

Thematic Analysis to Assess Indian Consumers Purchase Intention for Organic Apparel

Consumer behavior is dynamic, and there is a beauty in trying to understand consumer’s intention for a product category like organic apparel, especially when it is a unique phenomenon that is scantly explored in an emerging economy like India. This paper is an attempt at understanding the factors that influence Indian consumer’s intention to purchase organic apparel. A purposive sampling procedure was adopted in selecting participants. A focus group discussion was conducted to capture data for the thematic analysis. Theoretical thematic analysis was conducted by relying on the theory of planned behavior model. Inductive thematic analysis gave way for other dimensions like product knowledge and involvement, environmental knowledge, and skepticism that evolved out of the themes. Product knowledge and involvement, subjective norms, perceived behavioral control, and attitude had an influence on the intention. Environmental knowledge and skepticism indicated a chance to negate the relationship. The textile manufacturers, who are innovating with sustainable fabrics, can look at the dimensions that consumers seek for while making a choice of organic apparel. Domestic and international organic apparel manufacturers can capitalize on the behavioral dimensions of the factors that influence consumer’s intention for organic apparel, thereby facilitating identifying the prospect

Anomaly Detection in Data streams using MOA

Anomaly means anything which deviates from normal. It can be a credit card fraud or sensor alarm or a signal from a condition monitoring device. A problem like anomaly arises when we try to monitor the unusual behaviour of a machine. More number of outliers means the machine needs to be inspected. Anomaly detection in static data can be entirely different from that of streaming data. We have some issues in anomaly detection in streaming data when compared to static data. If any off – line algorithms attempt to find anomalies in streams, it has to store the entire stream for analysis. So, there is a high probability that it will run out of memory space. Also, streams can be infinite and evolving over a period of time because of which maintenance of high detection accuracy becomes almost impossible. In this paper we will discuss about anomaly detection in data streams and using MOA (Massive Online Analysis) tool we will analyse which algorithm derives best results

What can the SNO Neutral Current Rate teach us about the Solar Neutrino Anomaly

We investigate how the anticipated neutral current rate from $SNO$ will sharpen our understanding of the solar neutrino anomaly. Quantitative analyses are performed with representative values of this rate in the expected range of $0.8 - 1.2$. This would provide a $5 - 10 \sigma$ signal for $\nu_e$ transition into a state containing an active neutrino component. Assuming this state to be purely active one can estimate both the $^8B$ neutrino flux and the $\nu_e$ survival probability to a much higher precision than currently possible. Finally the measured value of the $NC$ rate will have profound implications for the mass and mixing parameters of the solar neutrino oscillation solution.Comment: Brief discussion on the first NC result from SNO added; final version to be published in the MPL

Spin observables and reconstruction of pi-d elastic-scattering amplitudes in transverse frame

The authors show that the measurement of only eight real parameters consisting of the differential cross section y0, the analysing power T20 and the polarisation transfer observables Cx,x, Cx,y, Cy,y, Cx,xz, Cx,yz and Cy,xz are sufficient for the complete determination of pi -d elastic scattering amplitudes in the transverse frame

Turbulent Supernova Shock Waves and the Sterile Neutrino Signature in Megaton Water Detectors

The signatures of sterile neutrinos in the supernova neutrino signal in megaton water Cerenkov detectors are studied. Time dependent modulation of the neutrino signal emerging from the sharp changes in the oscillation probability due to shock waves is shown to be a smoking gun for the existence of sterile neutrinos. These modulations and indeed the entire neutrino oscillation signal is found to be different for the case with just three active neutrinos and the cases where there are additional sterile species mixed with the active neutrinos. The effect of turbulence is taken into account and it is found that the effect of the shock waves, while modifed, remain significant and measurable. Supernova neutrino signals in water detectors can therefore give unambiguous proof for the existence of sterile neutrinos, the sensitivity extending beyond that for terrestial neutrino experiments. In addition the time dependent modulations in the signal due to shock waves can be used to trace the evolution of the shock wave inside the supernova.Comment: 28 pages, 11 figure

The use of artificial neural networks to diagnose mastitis in dairy cattle

The use of milk sample categorization for diagnosing mastitis using Kohonen's self-organizing feature map (SOFM) is reported. Milk trait data of 14 weeks of milking from commercial dairy cows in New Zealand was used to train and test a SOFM network. The SOFM network was useful in discriminating data patterns into four separate mastitis categories. Several other artificial neural networks were tested to predict the missing data from the recorded milk traits. A multi-layer perceptron (MLP) network proved to be most accurate (R² = 0.84, r = 0.92) when compared to other MLP (R² = 0.83, r = 0.92), Elman (R² = 0.80, r = 0.92), Jordan (R² = 0.81, r = 0.92) or linear regression (R² = 0.72, r = 0.85) methods. It is concluded that the SOFM can be used as a decision tool for the dairy farmer to reduce the incidence of mastitis in the dairy herd

Resilience models for New Zealand's alpine skiers based on people's knowledge and experience: a mixed method and multi-step fuzzy cognitive mapping approach

Artificial Neural Networks (ANN) as a tool offers opportunities for modeling the inherent complexity and uncertainty associated with socio-environmental systems. This study draws on New Zealand ski fields (multiple locations) as socio- environmental systems while considering their perceived resilience to low probability but potential high consequences catastrophic natural events (specifically earthquakes). We gathered data at several ski fields using a mixed methodology including: geomorphic assessment, qualitative interviews, and an adaptation of Ozesmi and Ozesmi’s (2003) multi-step fuzzy cognitive mapping (FCM) approach. The data gathered from FCM are qualitatively condensed, and aggregated to three different participant social groups. The social groups include ski fields users, ski industry workers, and ski field managers. Both quantitative and qualitative indices are used to analyze social cognitive maps to identify critical nodes for ANN simulations. The simulations experiment with auto-associative neural networks for developing adaptive preparation, response and recovery strategies. Moreover, simulations attempt to identify key priorities for preparation, response, and recovery for improving resilience to earthquakes in these complex and dynamic environments. The novel mixed methodology is presented as a means of linking physical and social sciences in high complexity, high uncertainty socio-environmental systems. Simulation results indicate that participants perceived that increases in Social Preparation Action, Social Preparation Resources, Social Response Action and Social Response Resources have a positive benefit in improving the resilience to earthquakes of ski fields’ stakeholders