Introducing education for sustainable development in the undergraduate laboratory: quantitative analysis of bioethanol fuel and its blends with gasoline by using solvatochromic dyes

The concept of Education for Sustainable Development, ESD, has been introduced in a period where chemistry education is undergoing a major change, both in emphasis and methods of teaching. Studying an everyday problem, with an important socio-economic impact in the laboratory is a part of this approach. Presently, the students in many countries go to school in vehicles that run, at least partially, on biofuels; it is high time to let them test these fuels. The use of renewable fuels is not new: since 1931 the gasoline sold in Brazil contains 20 to 25 vol-% of bioethanol; this composition is being continually monitored. With ESD in mind, we have employed a constructivist approach in an undergraduate course, where UV-vis spectroscopy has been employed for the determination of the composition of two fuel blends, namely, bioethanol/water, and bioethanol/gasoline. The activities started by giving a three-part quiz. The first and second ones introduced the students to historical and practical aspects of the theme (biofuels). In the third part, we asked them to develop a UV-vis experiment for the determination of the composition of fuel blends. They have tested two approaches: (i) use of a solvatochromic dye, followed by determination of fuel composition from plots of the empirical fuel polarity versus its composition; (ii) use of an ethanol-soluble dye, followed by determination of the blend composition from a Beer's law plot; the former proved to be much more convenient. Their evaluation of the experiment was highly positive, because of the relevance of the problem; the (constructivist) approach employed, and the bright colors that the solvatochromic dye acquire in these fuel blends. Thus ESD can be fruitfully employed in order to motivate the students; make the laboratory "fun", and teach them theory (solvation). The experiments reported here can also be given to undergraduate students whose major is not chemistry (engineering, pharmacy, biology, etc.). They are low-cost and safe to be introduced at high-school level.

Cost-effectiveness of conventional and modified supervised toothbrushing in preventing caries in permanent molars among 5-year-old children

The cost-effectiveness of a modified supervised toothbrushing program was compared to a conventional program. A total of 284 five-year-old children presenting at least one permanent molar with emerged/sound occlusal surface participated. In the control group, oral health education and dental plaque dying followed by toothbrushing with fluoride dentifrice was carried outfour times per year. With the test group, children also underwent professional cross-brushing on surfaces of first permanent molar rendered by a dental assistant five times per year. Enamel/dentin caries were recorded on buccal, occlusal and lingual surfaces of permanent molars for a period of 18 months. The incidence density (ID) ratio was estimated using Poisson's regression model. The ID was 50% lower among boys in the test group (p = 0.016). The cost of the modified program was US$ 1.79 per capita. The marginal cost-effectiveness ratio among boys was US$ 6.30 per avoided carie. The modified supervised toothbrushing program was shown to be cost-effective in the case of boys.

A semi-supervised classification technique based on interacting forces

Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed technique achieves good classification results on artificial data sets and is comparable to well-known semi-supervised techniques using benchmark data sets.