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This paper deals with the planning and modeling of a split-plot experiment to improve novel gas sensing materials based on Perovskite, a nano-structured, semi-conductor material that is sensitive to changes in the concentration of hazardous gas in the ambient air. The study addresses both applied and theoretical issues. More precisely, it focuses on (i) the detection of harmful gases, e.g., NO 2 and CO, which have a great impact on industrial applications as well as a significantly harmful impact on human health; (ii) the planning and modeling of a split-plot design for the two target gases by applying a dual-response modeling approach in which two models, e.g., location and dispersion models, are estimated; and (iii) a robust process optimization conducted in the final modeling step for each target gas and for each gas sensing material, conditioned to the minimization of the working temperature. The dual-response modeling allows us to achieve satisfactory estimates for the process variables and, at the same time, good diagnostic valuations. Optimal solutions are obtained for each gas sensing material while also improving the results achieved from previous studies.
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Eight different types of nanostructured perovskites based on YCoO 3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO 2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo 0 . 9 Pd 0 . 1 O 3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312 ∘ C for CO (284 ppm, from design) and response equal to -14.17% at 185 ∘ C for NO 2 (16 ppm, from design). Analogously, for NO 2 (16 ppm, from design), the material type YCo 0 . 9 O 2 . 85 + 1 % Pd (Mt8) allows for optimizing the response value at - 15 . 39 % with a working temperature at 181 . 0 ∘ C, whereas for YCo 0 . 95 Pd 0 . 05 O 3 (Mt3), the best response value is achieved at - 15 . 40 % with the temperature equal to 204 ∘ C.
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OBJECTIVES: Sex steroids are important regulators of bone physiology and play an essential role in the maintenance of bone health throughout the life. Hormonal replacement therapy (HRT) is a treatment commonly used to relieve symptoms and some undesirable consequences of menopause such as osteoporosis. Osteoporosis, characterized by the loss of bone mass and deterioration of microarchitecture with a consequent higher risk of fragility fractures, is under genetic influence. A tetranucleotide (TTTA)n microsatellite repeat polymorphism, at intron 4 of the CYP19 (aromatase) gene, has been previously associated with higher lumbar spine bone mineral density (LS-BMD) and lower risk of spine fracture in postmenopausal women. Moreover, the ERα encoded by the ESR1 gene is another important candidate for the regulation of bone mass of menopause. Moreover prospective analysis from >18.000 subjects at the GENOMOS study indicated that XX homozygotes genotype had a reduced risk of fracture independently from BMD. In the present study, we investigated in postmenopausal Italian women, at baseline and after 1 year of HRT, whether ESR1 and CYP19 gene polymorphisms could affect BMD through different statistical models. METHODS: This study has been performed on 100 post-menopausal Italian women, from a larger group of 250. The study group was administred HRT and LS-BMD was measured at baseline and after 1 year of therapy. Genetic analysis evaluating ESR1 and CYP19 gene polymorphisms was performed. RESULTS: Generalized Linear Models (GLMs) test showed that women with normal LS-BMD at the baseline had a major statistically significant BMD increase of 0.1426 gr/cm(2) (p= 0.0001) with respect to the osteoporotic patients. In addition, subjects with genotype 1 and 2 of CYP19 gene had a lower modification in LS-BMD after 1 year of HRT (0.0837 gr/cm(2) and 0,076 g/cm(2); p=0.0470 and 0,0547 respectively) when compared to genotype 3. No influences of the aromatase genotypes were observed in the variable difference using both Anova and GLMs test. Regarding the ESR1 gene polymorphism, the LS-BMD after 1 year of HRT was influenced by the diagnosis at the baseline and height and ERα genotypes were able to influence difference with statistical significant results with both test. CONCLUSIONS: In the present study, we have demonstrated that CYP19 gene polymorphism is able to influence the effect of 1 year HRT on LS-BMD with no influence on pre-/ and post-/HRT LS-BMD differences. Although ESR1 gene polymorphism is not able to influence the LS-BMD after 1 year HRT, it influences the observed modifications during the year of therapy. These data underlie the complexity of the genetics of the bone mass and its importance in influencing the response to HRT.
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Nowadays, computer experiments are used increasingly more to solve complex engineering and technological issues. Computer experiments are analysed through suitable metamodels acting as statistical interpolators of the simulated input-output data: Kriging is the most appropriate and widely used one. We optimise the braking performance of freight trains through computer experiments and Kriging modelling by focussing on the payload distribution along the train, so as to reduce the effects of in-train forces among wagons during a train emergency braking. One contribution of this manuscript is that to improve the freight train efficiency in terms of braking performance, we consider that the train is composed of several train sections with each one characterised by its own overall payload. A suitable Latin hypercube design is planned for the computer experiment that achieves excellent space-filling properties with a relatively low number of experimental runs. Kriging models with anisotropic covariance function are subsequently applied to assess which is the best payload distribution capable of reducting the in-train forces according to the specific train-set arrangement considered. The results are very satisfactory and confirm that our approach represents a valid method to be successfully applied by interested Railway Undertakings.
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The number of spots to monitor to evaluate soil respiration (Rs) is often chosen on an empirical or conventional basis. To obtain an insight into the necessary number of spots to account for Rs variability in a Mediterranean pine-dominated mixed forest, we measured Rs all year long on sixteen dates with a portable gas-analyser in 50 spots per date within an area 1/3 ha wide. Linear mixed-effects models with soil temperature and litter moisture as descriptors, were fitted to the collected data and then evaluated in a Monte Carlo simulation on a progressively decreasing number of spots to identify the minimum number required to estimate Rs with a given confidence interval. We found that monitoring less than 14 spots would have resulted in a 10% probability of not fitting the model, while monitoring 20 spots would have reduced the same probability to about 5% and was the best compromise between field efforts and quality of the results. A simple rainfall index functional to select sampling dates during the summer drought is proposed.