Improving the Sustainability of Dairy Slurry by A Commercial Additive Treatment

Ammonia (NH3), methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions from livestock farms contribute to negative environmental impacts such as acidification and climate change. A significant part of these emissions is produced from the decomposition of slurry in livestock facilities, during storage and treatment phases. This research aimed at evaluating the eectiveness of the additive \u201cSOP LAGOON\u201d (made of agricultural gypsum processed with proprietary technology) on (i) NH3 and Greenhouse Gas (GHG) emissions, (ii) slurry properties and N loss. Moreover, the Life Cycle Assessment (LCA) method was applied to assess the potential environmental impact associated with stored slurry treated with the additive. Six barrels were filled with 65 L of cattle slurry, of which three were used as a control while the additive was used in the other three. The results indicated that the use of the additive led to a reduction of total nitrogen, nitrates, and GHG emissions. LCA confirmed the higher environmental sustainability of the scenario with the additive for some environmental impact categories among which climate change. In conclusion, the additive has beneficial eects on both emissions and the environment, and the nitrogen present in the treated slurry could partially displace a mineral fertilizer, which can be considered an environmental credit

Autocorrelation analysis of GRBM–Beppo-SAX burst data

An autocorrelation function (ACF) analysis was performed on 17 gamma-ray bursts with known redshift, using data from the GRBM on board Beppo-SAX. When corrected from the cosmic time dilation effect, the ACFs show a bimodal distribution at about half-maximum, in agreement with a previous study based on BATSE and Konus burst data. Although the results show more dispersion, the separation between the two classes is highly significant

Time-resolved spectral correlations of long-duration Gamma-Ray Bursts

For a sample of long GRBs with known redshift, we study the distribution of the evolutionary tracks on the rest-frame luminosity-peak energy Liso-Ep' diagram. We are interested in exploring the extension of the `Yonetoku' correlation to any phase of the prompt light curve, and in verifying how the high-signal prompt duration time, Tf, in the rest frame correlates with the residuals of such correlation (Firmani et al. 2006). For our purpose, we analyse separately two samples of time-resolved spectra corresponding to 32 GRBs with peak fluxes >1.8 phot cm^-2 s^-1 from the Swift-BAT detector, and 7 bright GRBs from the CGRO-BATSE detector previously processed by Kaneko et al. (2006). After constructing the Liso-Ep' diagram, we discuss the relevance of selection effects, finding that they could affect significantly the correlation. However, we find that these effects are much less significant in the Liso x Tf-Ep' diagram, where the intrinsic scatter reduces significantly. We apply further corrections for reducing the intrinsic scatter even more. For the sub-samples of GRBs (7 from Swift and 5 from CGRO) with measured jet break time, we analyse the effects of correcting Liso by jet collimation. We find that (i) the scatter around the correlation is reduced, and (ii) this scatter is dominated by the internal scatter of the individual evolutionary tracks. These results suggest that the time, integrated `Amati' and `Ghirlanda' correlations are consequences of the time resolved features, not of selection effects, and therefore call for a physical origin. We finally remark the relevance of looking inside the nature of the evolutionary tracks.Comment: 11 pages, 6 figures, 4 tables. Submitted to MNRAS (Sept 8th), after referee comment

Synthesis of die-castable nano-particle reinforced aluminum matrix composite materials by in-situ gas-liquid reactions

 Nano-particle reinforced aluminum matrix composites areattractive engineering materials for many automotive andaerospace applications because they exhibit numerousdesirable mechanical and thermal properties, such ashigh specific strength, hardness, stiffness, and resistanceto creep and thermal degradation. Unfortunately, makingthese materials is not easy and most of the methodsthat have been developed so far for their synthesis areeither not robust, inefficient, or not cost effective. In thispublication, we report on the synthesis of die-castablealuminum-aluminum nitride nano-composite materialsby the reaction of a nitrogen-containing gas with moltenaluminum-lithium alloy. Specifically, we report on the effectof (1) the lithium content of the alloy, (2) the compositionof the reactive gas, and (3) the reaction time on (a) theamount, (b) the average size, and (c) the average clustersize of the aluminum nitride reinforcing particles; as wellas (d) the hardness and (e) the thermal stability of thecomposite material

On the temporal variability classes found in long gamma-ray bursts with known redshift

Based on the analysis of a small sample of BATSE and Konus gamma-ray bursts (GRBs) with know redshift it has been reported that the width of the autocorrelation function (ACF) shows a remarkable bimodal distribution in the rest-frame of the source. However, the origin of these two well-separated ACF classes remains unexplained.We complement previous ACF analysis studying the corresponding power density spectra (PDS). With the addition of Beppo-SAX data and taken advantage of its broad-band capability, we not only increase the burst sample but we extend the analysis to X-ray energies. The rest-frame PDS analysis at gamma-ray energies shows that the two ACF classes are not simply characterised by a different low frequency cut-off, but they have a distinct variability as a whole in the studied frequency range. Both classes exhibit average PDS with power-law behaviour at high frequencies (f' > 0.1 Hz) but significantly different slopes, with index values close to those of Brownian (-2) and Kolmogorov (-5/3) spectra for the narrow and broad classes respectively. The latter spectrum presents an additional PDS component, a low-frequency noise excess with a sharp cut-off. At X-ray energies we find the power-law index unchanged for the broad class, but a significantly steeper slope in the narrow case (~ -3). We interpret this as an indication that the broad class bursts have weaker spectral evolution than the narrow ones, as suggested also by our analysis of the ACF energy dependence. The low and high frequency PDS components may then arise from two radiating regions involving different emission mechanisms.Comment: 13 pages, 10 figures. Accepted for publication in A&

A New Frequency-Luminosity Relation for Long GRBs?

We have studied power density spectra (PDS) of 206 long Gamma-Ray Bursts (GRBs). We fitted the PDS with a simple power-law and extracted the exponent of the power-law (alpha) and the noise-crossing threshold frequency (f_th). We find that the distribution of the extracted alpha peaks around -1.4 and that of f_th around 1 Hz. In addition, based on a sub-set of 58 bursts with known redshifts, we show that the redshift-corrected threshold frequency is positively correlated with the isotropic peak luminosity. The correlation coefficient is 0.57 +/- 0.03.Comment: 9 pages, 17 figures, 1 table; Accepted for publication in MNRA

Sensitivity of projected long-term CO 2 emissions across the Shared Socioeconomic Pathways

Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO 2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO 2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities