She Is My Post Card Girl

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Caractérisation des sous-produits d'oxydation des boues en conditions sous-critiques et supercritiques

L'élimination de la matière organique et la réduction de volume des boues peuvent être obtenues par incinération, par oxydation sous pression en milieu humide ("wet air oxidation") ou par combustion en eau supercritique ("supercritical water oxidation"). Une étude en autoclave agité a permis de comparer sur une même boue d'épuration les performances des deux techniques d'oxydation voie humide et d'oxydation supercritique, en mettant l'accent sur les sous-produits résiduels en phase liquide et la composition de la phase gaz. Les résultats obtenus montrent que l'élimination de la DCO dépend fortement de la température: l'abattement de la DCO passe de 70 % à 235 °C à 94 % à 430 °C. L'azote organique de la boue est transformé en NH4+ mais seule une élimination limitée de l'azote totale est obtenue à 430 °C. Les sous-produits résiduels dans la phase liquide sont constitués en majorité d'acides gras, d'aldéhydes et de cétones, l'acide acétique étant prédominant. Hormis le CO2, les sous-produits gazeux majeurs formés par des réactions complexes comme la pyrolyse, le réformage et la méthanation sont CO, H2 et CH4. Dans les conditions supercritiques, tous les sous-produits gazeux sont fortement oxydés. L'augmentation de la température de traitement permet d'obtenir un résidu solide de plus en plus inerte, les cendres obtenues en conditions supercritiques contenant moins de 1 % de matière organique. Les performances des deux procédés étudiés laissent envisager leur développement à moyen terme comme voies alternatives d'élimination des boues.ContextAs the number of wastewater treatment plants increases, and the efficiency of treatment improves, the problem of how to dispose of the ever increasing amounts of generated sludge has intensified. For the beginning of the next century 1 million tons of sludge will be produced annually in France; disposal in landfills will be impossible and agricultural use could be limited by tight quality standards. Therefore, the development of effective and acceptable sludge processes is urgently needed.Destruction of organic matter in sludge and large reductions in sludge volume are achieved either by incineration or by wet air oxidation (WAO), which needs no fuel and generates no smoke, fly ash or emissions of NOx and SO2. Supercritical water oxidation (SCWO) offers an attractive alternative. Water, above its vapor-liquid critical point of 374°C and 221 bar, is an excellent solvent for organic compounds and becomes completely miscible with oxygen. Reported results of sewage sludge SCWO demonstrate rapid and effective treatment. The objective of this study was to compare sub- and supercritical water oxidation of sludge in terms of organic matter destruction and formation of by-products in both gas and liquid phases. MethodologyOxidation of sludge was studied in a 0.5 L batch reactor rated for 450°C-300 bar. The raw material was a biological sludge containing 4% solids with a chemical oxygen demand (COD) value of 52 g/L. In the standard experimental procedure, 100 mL sludge were heated up to reaction temperature and oxygen was then introduced in 50% excess with respect to COD. Heating was maintained during 1 hour before slow cooling to room temperature. The overall organic destruction was quantified in terms of total organic carbon (TOC) and COD. Gas and liquid phases sampled at room temperature after reaction were analyzed by gas chromatography (GC). Sulfur and nitrogen species were also analyzed.ResultsWhen the temperature increased from 210 to 383°C, COD destruction increased significantly (Table 1). At 383°C, a COD destruction efficiency of 94.3% was obtained. However, at 430°C, organic matter oxidation was only marginally improved. In WAO tests, considerable acetic acid was produced and remained in the substrate. The produced acetic acid was oxidized rapidly under SCWO conditions. Surprisingly, the concentrations of the other volatile fatty acids (VFA) remained approximately constant between 310 and 430°C (Table 2). In addition to VFA, which represent ca. 50% of the residual COD, oxygenated organic compounds such as aldehydes, ketones and alcohols were produced (Table 3). The data in Table 4 show that decomposition of organic nitrogen compounds into ammonia was completed at 383°C, while nitrates were reduced to N2 by reaction with organic matter and ammonia. NOx were not detected in the gas phase. The low reactivity of ammonia in supercritical water had been previously demonstrated. At 430°C, ammonia removal from sludge was limited to 15%. On the other hand, even in WAO conditions all sulfur species were totally converted to sulfate. Under subcritical conditions, the gas phase contained significant concentrations of hydrogen and carbon monoxide in addition to water, residual oxygen and carbon dioxide. Traces of methane and C2-C3 hydrocarbons were also detected (Figs. 1 and 2). These gases result from a complex chemistry including pyrolysis, steam reforming and water-gas shift. Under supercritical conditions, all these compounds were extensively eliminated by oxidation. Under supercritical conditions the residual solids contained less than 1% organic matter. By X-ray diffraction hydroxyapatite, quartz and kaliophilite were identified in the residual solids.These results confirm that supercritical water oxidation is a new sludge treatment concept of great interest. The degree of conversion of organic carbon is high, while liquid and especially gaseous by- products are produced in minor amounts compared to subcritical conditions. Temperatures higher than 430°C would be needed for substantial nitrogen removal

Multispectral Remote Sensing for Yield Estimation Using High-Resolution Imagery From an Unmanned Aerial Vehicle

Satellites and autonomous unmanned aerial vehicles (UAVs) are two major platforms for acquiring remotely-sensed information of the earth’s surface. Due to the limitations of satellite-based imagery, such as coarse spatial resolution and fixed schedules, applications of UAVs as low-cost remote sensing systems are rapidly expanding in many research areas, particularly precision agriculture. UAVs can provide imagery with high spatial resolution (finer than 1 meter) and acquire information in visible, near infrared, and even thermal bands. In agriculture, vegetation characteristics such as health, water stress, and the amount of biomass, can be estimated using UAV imagery. In this study, three sets of high-resolution aerial imagery have been used for yield estimation based on vegetation indices. These images were captured by the Utah State University AggieAir™ UAV system flown in June 2017, August 2017, and October 2017 over a field experiment pasture site located in northern Utah. The pasture study area is primarily tall fescue. The field experiment includes 20 50 x 20-m plots, with 4 replications of 5 irrigation levels. Approximately 60 yield samples were harvested after each flight. Sample locations were recorded with high-accuracy real-time kinematic (RTK) GPS. In addition, the leaf area index (LAI) for each sample plot was measured using an optical sensor (LAI2200C) before harvesting. The relationship of yield for each sample versus vegetation indices (VIs) was explored. The VIs include the normalized difference vegetation index (NDVI), calculated using AggieAir imagery, and LAI measured using a ground-based sensor. The results of this study reveal the correlation between vegetation indices and the amount of biomass

Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin

Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion)from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin.Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different timescales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM andPRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet andPRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness off it statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands

Estimation of Soil Moisture at Different Soil Levels Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process are not accessible to researchers, each application of these machine learning approaches must repeat these training steps for any new study area. The fact that the results of this machine learning, black box approach cannot be easily transferred to different locations for extraction of soil moisture estimates is frustrating, and it can lead to inaccurate comparisons between methods or model performance. To overcome the Black-box issue, this study employed a powerful technique called genetic programming (GP), which is a combination of an evolutionary algorithm and artificial intelligence, to simulate soil moisture at different levels using high-resolution, multispectral imagery acquired with an unmanned aerial vehicle (UAV). The output of this approach is either a linear or nonlinear empirical equation that can be used by others. The performance of GP was compared with ANN and SVM modeling results. Several sets of high-resolution aerial imagery captured by the Utah State University AggieAir UAV system over two experimental pasture sites located in northern and southern Utah were used for this soil moisture estimation approach. The inputs used to train these models include the reflectance for the visible, near-infrared (NIR), and thermal bands. The results show (1) the performance of GP versus ANN and SVM and (2) the master equation provided by GP, which can be used in other locations and applications

In flight performance and first results of FREGATE

The gamma-ray detector of HETE-2, called FREGATE, has been designed to detect gamma-ray bursts in the energy range [6-400] keV. Its main task is to alert the other instruments of the occurrence of a gamma-ray burst (GRB) and to provide the spectral coverage of the GRB prompt emission in hard X-rays and soft gamma-rays. FREGATE was switched on on October 16, 2000, one week after the successful launch of HETE-2, and has been continuously working since then. We describe here the main characteristics of the instrument, its in-flight performance and we briefly discuss the first GRB observations.Comment: Invited lecture at the Woods Hole 2001 GRB Conference, 8 pages, 15 figure

Polymorphisms in the gene encoding bovine interleukin-10 receptor alpha are associated with Mycobacterium avium ssp. paratuberculosis infection status

<p>Abstract</p> <p>Background</p> <p>Johne's disease is a chronic inflammatory bowel disease (IBD) of ruminants caused by <it>Mycobacterium avium </it>ssp. <it>paratuberculosis </it>(MAP). Since this pathogen has been implicated in the pathogenesis of human IBDs, the goal of this study was to assess whether single nucleotide polymorphism (SNPs) in several well-known candidate genes for human IBD are associated with susceptibility to MAP infection in dairy cattle.</p> <p>Methods</p> <p>The bovine candidate genes, <it>interleukin-10 (IL10), IL10 receptor alpha/beta (IL10RA/B), transforming growth factor beta 1 (TGFB1)</it>, <it>TGFB receptor class I/II (TGFBR1/2)</it>, and <it>natural resistance-associated macrophage protein 1 (SLC11A1) </it>were sequenced for SNP discovery using pooled DNA samples, and the identified SNPs were genotyped in a case-control association study comprised of 242 MAP negative and 204 MAP positive Holstein dairy cattle. Logistic regression was used to determine the association of SNPs and reconstructed haplotypes with MAP infection status.</p> <p>Results</p> <p>A total of 13 SNPs were identified. Four SNPs in <it>IL10RA </it>(984G > A, 1098C > T, 1269T > C, and 1302A > G) were tightly linked, and showed a strong additive and dominance relationship with MAP infection status. Haplotypes AGC and AAT, containing the SNPs <it>IL10RA </it>633C > A, 984G > A and 1185C > T, were associated with an elevated and reduced likelihood of positive diagnosis by serum ELISA, respectively.</p> <p>Conclusions</p> <p>SNPs in <it>IL10RA </it>are associated with MAP infection status in dairy cattle. The functional significance of these SNPs warrants further investigation.</p

Dynamical Ising-like model for the two-step spin-crossover systems

In order to reproduce the two-step relaxation observed experimentally in spin-crossover systems, we investigate analytically the static and the dynamic properties of a two-sublattice Ising-like Hamiltonian. The formalism is based on a stochastic master equation approach. It is solved in the mean-field approximation, and yields two coupled differential equations that correspond to the HS fractions of the sublattices A and [email protected] ; [email protected]

No More Active Galactic Nuclei in Clumpy Disks Than in Smooth Galaxies at z~2 in CANDELS / 3D-HST

We use CANDELS imaging, 3D-HST spectroscopy, and Chandra X-ray data to investigate if active galactic nuclei (AGNs) are preferentially fueled by violent disk instabilities funneling gas into galaxy centers at 1.3<z<2.4. We select galaxies undergoing gravitational instabilities using the number of clumps and degree of patchiness as proxies. The CANDELS visual classification system is used to identify 44 clumpy disk galaxies, along with mass-matched comparison samples of smooth and intermediate morphology galaxies. We note that, despite being being mass-matched and having similar star formation rates, the smoother galaxies tend to be smaller disks with more prominent bulges compared to the clumpy galaxies. The lack of smooth extended disks is probably a general feature of the z~2 galaxy population, and means we cannot directly compare with the clumpy and smooth extended disks observed at lower redshift. We find that z~2 clumpy galaxies have slightly enhanced AGN fractions selected by integrated line ratios (in the mass-excitation method), but the spatially resolved line ratios indicate this is likely due to extended phenomena rather than nuclear AGNs. Meanwhile the X-ray data show that clumpy, smooth, and intermediate galaxies have nearly indistinguishable AGN fractions derived from both individual detections and stacked non-detections. The data demonstrate that AGN fueling modes at z~1.85 - whether violent disk instabilities or secular processes - are as efficient in smooth galaxies as they are in clumpy galaxies.Comment: ApJ accepted. 17 pages, 17 figure