257 research outputs found
Enhancing properties of iron and manganese ores as oxygen carriers for chemical looping processes by dry impregnation
The use of naturally occurring ores as oxygen carriers in CLC processes is attractive because of their relative abundance and low cost. Unfortunately, they typically exhibit lower reactivity and lack the mechanical robustness required, when compared to synthetically produced carriers. Impregnation is a suitable method for enhancing both the reactivity and durability of natural ores when used as oxygen carriers for CLC systems. This investigation uses impregnation to improve the chemical and mechanical properties of a Brazilian manganese ore and a Canadian iron ore. The manganese ore was impregnated with Fe2O3 and the iron ore was impregnated with Mn2O3 with the goal of forming a combined Fe/Mn oxygen carrier. The impregnated ore’s physical characteristics were assessed by SEM, BET and XRD analysis. Measurements of the attrition resistance and crushing strength were used to investigate the mechanical robustness of the oxygen carriers. The impregnated ore’s mechanical and physical properties were clearly enhanced by the impregnation method, with boosts in crushing strength of 11–26% and attrition resistance of 37–31% for the impregnated iron and manganese ores, respectively. Both the unmodified and impregnated ore’s reactivity, for the conversion of gaseous fuel (CH4 and syngas) and gaseous oxygen release (CLOU potential) were investigated using a bench-scale quartz fluidised-bed reactor. The impregnated iron ore exhibited a greater degree of syngas conversion compared to the other samples examined. Iron ore based oxygen carrier’s syngas conversion increases with the number of oxidation and reduction cycles performed. The impregnated iron ore exhibited gaseous oxygen release over extended periods in an inert atmosphere and remained at a constant 0.2% O2 concentration by volume at the end of this inert period. This oxygen release would help ensure the efficient use of solid fuels. The impregnated iron ore’s reactivity for CH4 conversion was similar to the reactivity of its unmodified counterpart. The unmodified manganese ore converted CH4 to the greatest extent of all the samples tested here, while the impregnated manganese ore exhibited a decrease in reactivity with respect to syngas and CH4 conversion.EPSR
Comet C/2013 A1 (Siding Spring) as seen with the Herschel Space Observatory
The thermal emission of comet C/2013 A1 (Siding Spring) was observed on March
31, 2013, at a heliocentric distance of 6.48 au using the PACS photometer
camera of the Herschel Space Observatory. The comet was clearly active, showing
a coma that could be traced to a distance of 10", i.e. 50000 km. Analysis of
the radial intensity profiles of the coma provided dust mass and dust
production rate; the derived grain size distribution characteristics indicate
an overabundance of large grains in the thermal emission. We estimate that
activity started about 6 months before these observations, at a heliocentric
distance of 8 au.Comment: 4 pages, 3 figures + a one-page Appendix with 2 figures, accepted for
publication in Astronomy & Astrophysics (Letter
Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes
Heterogeneous, multi-component materials such as industrial tailings or by-products, along with naturally occurring materials, such as ores, have been intensively investigated as candidate oxygen carriers for chemical-looping processes. However, these materials have highly variable compositions, and this strongly influences their chemical-looping performance. Here, using machine learning techniques, we estimate the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping. Experimental data for 19 manganese ores chosen as potential chemical-looping oxygen carriers were used to create a so-called training database. This database has been used to train several supervised artificial neural network models (ANN), which were used to predict the reactivity of the oxygen carriers with different fuels and the oxygen transfer capacity with only the knowledge of reactor bed temperature, elemental composition, and mechanical properties of the manganese ores. This novel approach explores ways of dealing with the training dataset, learning algorithms and topology of ANN models to achieve enhanced prediction precision. Stacked neural networks with a bootstrap resampling technique have been applied to achieve high precision and robustness on new input data, and the confidence intervals were used to assess the precision of these predictions. The current results indicate that the best trained ANNs can produce highly accurate predictions for both the training database and the unseen data with the high coefficient of determination (R2 = 0.94) and low mean absolute error (MAE = 0.057). We envision that the application of these ANNs and other machine learning algorithms will accelerate the development of oxygen carrying materials for a range of chemical-looping applications and offer a rapid screening tool for new potential oxygen carriers
Predicting kill sites of an apex predator from GPS data in different multi‐prey systems
Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill-site identification, such as failing to detect GLCs that are kill sites, and misclassifying the generated GLCs (e.g., kill for nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affected the detection of GLC-indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24 h decreased the probability of detecting GLCs connected with kill sites, particularly those of semidomestic or domestic prey, and small prey. Random forest successfully predicted between 73%–90% of ungulate kills, but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm’s overall accuracy. We provide a set of recommendations for studies focusing on kill-site detection that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher
Potential for increased connectivity between differentiated wolverine populations
Information on genetic population structure provides important knowledge for species conservation. Yet, few studies combine extensive genetic data to evaluate the structure and population dynamics of transboundary populations. Here we used single nucleotide polymorphisms (SNPs), microsatellites and mitochondrial haplotypes to analyze the genetic population structure of wolverines (Gulo gulo) across Fennoscandia using a long-term monitoring dataset of 1708 individuals. Clear population subdivision was detected between the Scandinavian and the eastern Finnish population with a steep cline in the contact zone. While the Scandinavian population showed isolation by distance, large swaths of this population were characterized by high connectivity. Areas with high resistance to gene flow are likely explained by a combination of factors, such as historical isolation and founder effects. From a conservation perspective, promoting gene flow from the population in eastern Finland to the northwest of Scandinavia could augment the less variable Scandinavian population, and increase the demographic resilience of all subpopulations. Overall, the large areas of low resistance to gene flow suggest that transboundary cooperation with aligned actions of harvest and conflict mitigation could improve genetic connectivity across Finland, Sweden, and Norway
A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation
Nanoparticles introduced in living cells are capable of strongly promoting
the aggregation of peptides and proteins. We use here molecular dynamics
simulations to characterise in detail the process by which nanoparticle
surfaces catalyse the self- assembly of peptides into fibrillar structures. The
simulation of a system of hundreds of peptides over the millisecond timescale
enables us to show that the mechanism of aggregation involves a first phase in
which small structurally disordered oligomers assemble onto the nanoparticle
and a second phase in which they evolve into highly ordered beta-sheets as
their size increases
Implementation and effects of user participation in playground management: a comparative study of two Swedish municipalities
This paper describes and analyses how customer orientation strategies, with the focus on user
participation, are implemented in playground management and their effects on managers’ attitudes and work with physical playgrounds. A comparative case study was conducted in two Swedish municipalities that involve users in different ways: through a manager-driven
participation process and through informal user-initiated dialogue. The empirical material consisted of qualitative interviews with professionals in the management organisations and studies of local playgrounds. Implementation of strategies for user participation and tactical management activities appeared to be of importance. The manager-driven participation strategy was associated with a particularly positive attitude among managers, but also difficulties such as maintaining continuous dialogue with users. The small differences found in playground provision between the two municipalities give reason to question the physical
effects of participation processes, and show the need for further research
The natural course and outcome of major depressive disorder in primary care: the PREDICT-NL study
Environmental and lifestyle risk factors of breast cancer in Malta-a retrospective case-control study
The funding for this research was obtained as part of IMaGenX – and ItaliaMalta co-financed EU project Operational Programme 2007–2013.AIM AND BACKGROUND:
Environmental exposures are known to play a role in the development of cancer, including breast cancer. There are known associations of breast cancer with environmental factors such as sunlight exposure, diet and exercise and alcohol consumption as well as physiological factors. This study examines the prevalence of risk factors for breast cancer related to dietary intake, environment and lifestyle in the female population of Malta. Malta has had little research in this area, and therefore an exploratory study was carried out.
METHODS:
A retrospective case-control design was applied. Two hundred cases and 403 controls were included. Both cases and controls were subjects without a known family history for breast cancer. Controls were age-matched to cases in an age-decade category roughly at a 2:1 ratio. Interviews were carried out face-to-face using a questionnaire designed by Maltese and Sicilian researchers, encompassing various factors including diet, lifestyle, physiological factors and medical history. Breast cancer risk was then analysed using both univariate and multivariate analyses. For factors having a metric scale, the Mann-Whitney test was used to compare mean scores, while for categorical factors, the chi-square test was used to compare percentages between the case and control groups. Statistical modelling was carried out using binary logistic regression to relate the likelihood of breast cancer to over 50 risk/protective factors analysed collectively.
RESULTS:
Univariate analysis showed around 20 parameters of interest, 14 of which were statistically significant at a 0.05 level of significance. Logistic regression analysis identified 11 predictors of interest that were statistically significant. Tomato, coffee and canned meat consumption were associated with lower likelihood of breast cancer (OR = 0.988, 0.901, 0.892, respectively), whereas beans and cabbage consumption and low sodium salt were positively associated with breast cancer (OR = 1.045, 1.834, 1.028, respectively). Premenopausal status was associated with a lower risk of breast cancer compared to postmenopausal status (OR = 0.067). Not having experienced myocardial infarction was associated with lower odds of breast cancer (OR = 0.331). Increased height was also found to have a strong association with risk of breast cancer, with the odds of having breast cancer increasing for every centimetre increase in height (OR = 1.048). In terms of quantity, odds of having breast cancer were lower in those exposed to sunlight (OR = 0.891). The odds of having breast cancer were also lower in those not using the oral contraceptive pill (OR = 0.454).
CONCLUSIONS:
Various factors in this exploratory study were found to be associated with development of breast cancer. While causal conclusions cannot be made, tomato consumption is of particular interest, as these results corroborate findings found in other studies. A negative association of breast cancer with sunlight exposure and oral contraceptive pill use corroborates findings in other studies. Other associations with dietary intake can be explained by dietary changes. More robust studies in this area, including possible longitudinal studies, are warranted.peer-reviewe
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