Assessment of Chemical Inhibitor Addition to Improve the Gas Production from Biowaste

The coexistence of sulphate-reducing bacteria and methanogenic archaea in the reactors during the anaerobic digestion from sulphate-containing waste could favor the accumulation of sulfide on the biogas, and therefore reduce its quality. In this study, the effect of sulphate-reducing bacteria inhibitor (MoO−2 4 ) addition in a two phase system from sulphate-containing municipal solid waste to improve the quality of the biogas has been investigated. The results showed that although SRB and sulphide production decreased, the use of inhibitor was not effective to improve the anaerobic digestion in a two phase system from sulphate-containing waste, since a significant decrease on biogas and organic matter removal were observed. Before MoO−2 4 addition the average values of volatile solid were around 12 g/kg, after 5 days of inhibitor use, those values did exceed to 28 g/kg. Molybdate caused acidification in the reactor and it was according to decrease in the pH values. In relation to microbial consortia, the effect of inhibitor was a decrease in Bacteria (44%; 60% in sulphate-reducing bacteria) and Archaea (38%) population

On domain walls in a Ginzburg-Landau non-linear S^2-sigma model

The domain wall solutions of a Ginzburg-Landau non-linear $S^2$-sigma hybrid model are unveiled. There are three types of basic topological walls and two types of degenerate families of composite - one topological, the other non-topological- walls. The domain wall solutions are identified as the finite action trajectories (in infinite time) of a related mechanical system that is Hamilton-Jacobi separable in sphero-conical coordinates. The physical and mathematical features of these domain walls are thoroughly discussed.Comment: 26 pages, 18 figure

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware

Separating planetary reflex Doppler shifts from stellar variability in the wavelength domain

Stellar magnetic activity produces time-varying distortions in the photospheric line profiles of solar-type stars. These lead to systematic errors in high-precision radial-velocity measurements, which limit efforts to discover and measure the masses of low-mass exoplanets with orbital periods of more than a few tens of days. We present a new data-driven method for separating Doppler shifts of dynamical origin from apparent velocity variations arising from variability-induced changes in the stellar spectrum. We show that the autocorrelation function (ACF) of the cross-correlation function used to measure radial velocities is effectively invariant to translation. By projecting the radial velocities on to a subspace labelled by the observation identifiers and spanned by the amplitude coefficients of the ACF's principal components, we can isolate and subtract velocity perturbations caused by stellar magnetic activity. We test the method on a 5-year time sequence of 853 daily 15-minute observations of the solar spectrum from the HARPS-N instrument and solar-telescope feed on the 3.58-m Telescopio Nazionale Galileo. After removal of the activity signals, the heliocentric solar velocity residuals are found to be Gaussian and nearly uncorrelated. We inject synthetic low-mass planet signals with amplitude $K=40$ cm s$^{-1}$ into the solar observations at a wide range of orbital periods. Projection into the orthogonal complement of the ACF subspace isolates these signals effectively from solar activity signals. Their semi-amplitudes are recovered with a precision of $\sim~6.6$ cm s$^{-1}$, opening the door to Doppler detection and characterization of terrestrial-mass planets around well-observed, bright main-sequence stars across a wide range of orbital periods

Abundance, movements and biodiversity of flying predatory insects in crop and non-crop agroecosystems

[EN] Predatory insects are key natural enemies that can highly reduce crops pest damage. However, there is a lack of knowledge about the movements of flying predatory insects in agroecosystems throughout the year. In particular, it is still unclear how these predators move from crop to non-crop habitats, which are the preferred habitats to overwinter and to spread during the spring and if these predators leave or stay after chemical treatments. Here, the Neuroptera, a generalist, highly mobile, flying predator order of insects, was selected as model. We studied the effects of farming management and the efficiency of edge shelterbelts, ground cover vegetation, and fruit trees canopy on holding flying predatory insects in Mediterranean traditional agroecosystems. Seasonal movements and winter effects were also assessed. We evaluated monthly nine fruit agroecosystems, six organic, and three pesticides sprayed, of 0.5-1 ha in eastern Spain during 3 years using two complementary methods, yellow sticky traps and aspirator. Results show surprisingly that the insect abundance was highest in pesticide sprayed systems, with 3.40 insects/sample versus 2.32 insects/sample in organic systems. The biodiversity indices were highest in agroecosystems conducted under organic management, with S of 4.68 and D of 2.34. Shelterbelts showed highest biodiversity indices, S of 3.27 and D of 1.93, among insect habitats. Insect species whose adults were active during the winter preferred fruit trees to spend all year round. However, numerous species moved from fruit trees to shelterbelts to overwinter and dispersed into the orchard during the following spring. The ground cover vegetation showed statistically much lower attractiveness for flying predatory insects than other habitats. Shelterbelts should therefore be the first option in terms of investment in ecological infrastructures enhancing flying predators.Sorribas Mellado, JJ.; González Cavero, S.; Domínguez Gento, A.; Vercher Aznar, R. (2016). Abundance, movements and biodiversity of flying predatory insects in crop and non-crop agroecosystems. Agronomy for Sustainable Development. 36(2). doi:10.1007/s13593-016-0360-3S362Altieri MA, Letourneau DK (1982) Vegetation management and biological control in agroecosystems. Crop Prot 1:405–430. doi: 10.1016/0261-2194(82)90023-0Altieri MA, Schmidt LL (1986) The dynamics of colonizing arthropod communities at the interface of abandoned, organic and commercial apple orchards and adjacent woodland habitats. Agric Ecosyst Environ 16:29–43. doi: 10.1016/0167-8809(86)90073-3Bengtsson J, Ahnström J, Weibull A (2005) The effects of organic agriculture on biodiversity and abundance: a meta-analysis. J App Ecol 42:261–269. doi: 10.1111/j.1365-2664.2005.01005.xBianchi F, Booij CJH, Tscharntke T (2006) Sustainable pest regulation in agricultural landscapes: a review on landscape composition, biodiversity and natural pest control. Proc R Soc B 273:1715–1727. doi: 10.1098/rspb.2006.3530Chaplin-Kramer RM, Rourke E, Blitzer EJ, Kremen C (2011) A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol Lett 14:922–932. doi: 10.1111/j.1461-0248.2011.01642.xCrowder DW, Northfield TD, Strand MR, Snyder WE (2010) Organic agriculture promotes evenness and natural pest control. Nature 466:109–112. doi: 10.1038/nature09183Dogramaci M, DeBano SJ, Kimoto C, Wooster DE (2011) A backpack-mounted suction apparatus for collecting arthropods from various habitats and vegetation. Entomol Exp et Appl 139:86–90. doi: 10.1111/j.1570-7458.2011.01099.xDuelli P, Studer M, Marchland I, Jakob S (1990) Population movements of arthropods between natural and cultivated areas. Biol Conserv 54:193–207. doi: 10.1016/0006-3207(90)90051-PEilenberg J, Hajek A, Lomer C (2001) Suggestions for unifying the terminology in biological control. BioControl 46:387–400. doi: 10.1023/A:1014193329979Forman RTT, Baudry J (1984) Hedgerows and hedgerow networks in landscape ecology. Environ Manage 8:495–510. doi: 10.1007/BF01871575Gurr GM, Wratten SD, Luna JM (2003) Multi-function agricultural biodiversity: pest management and other benefits. Basic Appl Ecol 4:107–116. doi: 10.1078/1439-1791-00122Hole DG, Perkins AJ et al (2005) Does organic farming benefit biodiversity? Biol Conserv 122:113–130. doi: 10.1016/j.biocon.2004.07.018Landis DA, Wratten SD, Gurr GM (2000) Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu Rev Entomol 45:175–201. doi: 10.1146/annurev.ento.45.1.175Long RF, Corbett A, Lamb C, Reberg-Horton C, Chandler J, Stimmann M (1998) Beneficial insects move from flowering plants to nearby crops. Calif Agr 52:23–26. doi: 10.3733/ca.v052n05p23Östman Ö, Ekbom B, Bengtsson J (2001) Landscape heterogeneity and farming practice influence biological control. Basic App Ecol 2:365–371. doi: 10.1078/1439-1791-00072Pantaleoni RA, Ticchiati V (1988) I Neurotteri delle colture agrarie: osservazioni sulle fluttuazioni stagionali di populazione in frutteti. Boll dell’Ist di Entomol 43:43–57Panzer R, Schwartz MW (1998) Effectiveness of a vegetation-based approach to insect conservation. Conserv Biol 12:693–702. doi: 10.1111/j.1523-1739.1998.97051.xParedes D, Cayuela L, Gurr G, Campos M (2013) Effect of non-crop vegetation types on conservation biological control of pests in olive groves. PeerJ 1:1–16. doi: 10.7717/peerj.116Pekar S, Michalko R, Loverre P, Líznarová E, Cernecká L (2015) Biological control in winter: novel evidence for the importance of generalist predators. J Appl Ecol 52:270–279. doi: 10.1111/1365-2664.12363Pollard KA, Holland JM (2006) Arthropods within the woody element of hedgerows and their distribution pattern. Agric Forest Entomol 8:203–211. doi: 10.1111/j.1461-9563.2006.00297.xRand TA, Tylianakis JM, Tscharntke T (2006) Spillover edge effects: the dispersal of agriculturally subsidized insect natural enemies into adjacent natural habitats. Ecol Lett 9:603–614. doi: 10.1111/j.1461-0248.2006.00911.xSilva EB, Franco JC, Vasconcelos T, Branco M (2010) Effect of ground cover vegetation on the abundance and diversity of beneficial arthropods in citrus orchards. Bull Entomol Res 100:489–499. doi: 10.1017/S0007485309990526Smukler SM, Sánchez-Moreno S et al (2010) Biodiversity and multiple ecosystem functions in an organic farmscape. Agric Ecosyst Environ 139:80–97. doi: 10.1016/j.agee.2010.07.004Stelzl M, Devetak D (1999) Neuroptera in agricultural ecosystems. Agric Ecosyst Environ 74:305–321. doi: 10.1016/S0167-8809(99)00040-7Straub CS, Finke DL, Snyder WE (2008) Are the conservation of natural enemy biodiversity and biological control compatible goals? Biol Control 45:225–237. doi: 10.1016/j.biocontrol.2007.05.013Thierry D, Deutsch B, Paulian M, Villenave J, Canard M (2005) Typifying ecosystems by using green lacewing assemblages. Agron Sustain Dev 25:473–479. doi: 10.1051/agro:2005047Thomas CFG, Parkinson L, Griffiths GJK, García AF, Marshall EJP (2001) Aggregation and temporal stability of carabid beetle distributions in field and hedgerow habitats. J App Ecol 38:100–116. doi: 10.1046/j.1365-2664.2001.00574.xVillenave J, Thierry D, Mamun A, Lode T, Rat-Morris E (2005) The pollens consumed by common green lacewings Chrysoperla spp. in cabbage crop environment in western France. Eur J Entomol 02:547–552, 10.14411/eje.2005.078You M, Hou Y, Liu Y, Yang G, Li Z, Cai H (2004) Non-crop habitat manipulation and integrated pest management in agroecosystems. Acta Entomol Sinica 47:260–268 (http://www.insect.org.cn/EN/Y2004/V47/I2/260

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Kidney transplant in diabetic patients: modalities, indications and results

<p>Abstract</p> <p>Background</p> <p>Diabetes is a disease of increasing worldwide prevalence and is the main cause of chronic renal failure. Type 1 diabetic patients with chronic renal failure have the following therapy options: kidney transplant from a living donor, pancreas after kidney transplant, simultaneous pancreas-kidney transplant, or awaiting a deceased donor kidney transplant. For type 2 diabetic patients, only kidney transplant from deceased or living donors are recommended. Patient survival after kidney transplant has been improving for all age ranges in comparison to the dialysis therapy. The main causes of mortality after transplant are cardiovascular and cerebrovascular events, infections and neoplasias. Five-year patient survival for type 2 diabetic patients is lower than the non-diabetics' because they are older and have higher body mass index on the occasion of the transplant and both pre- and posttransplant cardiovascular diseases prevalences. The increased postransplant cardiovascular mortality in these patients is attributed to the presence of well-known risk factors, such as insulin resistance, higher triglycerides values, lower HDL-cholesterol values, abnormalities in fibrinolysis and coagulation and endothelial dysfunction. In type 1 diabetic patients, simultaneous pancreas-kidney transplant is associated with lower prevalence of vascular diseases, including acute myocardial infarction, stroke and amputation in comparison to isolated kidney transplant and dialysis therapy.</p> <p>Conclusion</p> <p>Type 1 and 2 diabetic patients present higher survival rates after transplant in comparison to the dialysis therapy, although the prevalence of cardiovascular events and infectious complications remain higher than in the general population.</p