Università degli Studi di Roma "Tor Vergata"

LEARNING OUTCOMES
Ability to include the main structural and functional properties of biopolymer.

KNOWLEDGE AND UNDERSTANDING
Understanding of the chemical and physical principles that underlie structural motifs in biopolymers, as well as important techniques for their study.

APPLYING KNOWLEDGE AND UNDERSTANDING
Ability to apply the different knowledge learned during the lessons, as well as ability to discriminate between the best strategy to follow for a study project.

MAKING JUDGEMENTS
Ability to be independent in a scientific project by acquiring information from other related sectors.

COMMUNICATION SKILLS
Ability in the relationship with sectors of genetics, biochemistry and molecular biology to apply for suitable experiments.

LEARNING SKILLS
Ability to autonomously extend one’s own knowledge by using the suitable literature and to know how to move in sectors related to one’s own.

LEARNING OUTCOMES
Ability to understand the relevant scientific literature and to extract information from spectra of Nuclear Magnetic Resonance.

KNOWLEDGE AND UNDERSTANDING
Understanding of the necessary NMR experiments of utility in the field of nanosystems and of the basic theory behind each of them.

APPLYING KNOWLEDGE AND UNDERSTANDING
Ability to apply the different methodologies used during the lesson, as well as the ability to discriminate between the best strategy to follow.

MAKING JUDGEMENTS
Ability to be independent in a scientific project by acquiring information deriving from other related sectors.

COMMUNICATION SKILLS
Ability to relate to other sectors to establish appropriate experiments

LEARNING SKILLS
Ability to extend their own knowledge for the use of other experiments and to know how to move in sectors related to their own.

LEARNING OUTCOMES
The course aims to provide the basic knowledge about the diffusion based phase transformation occurring in the solid state with particular attention to thermodynamics and kinetics. The chemical distribution on nano- and micro-scale and the microstructure of materials will be presented.

KNOWLEDGE AND UNDERSTANDING
The students should understand how the microstructure of metallic materials can be modified through heat treatments which induce the formation of different phases.

APPLYING KNOWLEDGE AND UNDERSTANDING
The content of the course is useful for determining the fundamental process parameters (temperature, time, atmosphere) of heat treatments to induce the suitable microstructural transformations in metal alloys and achieve the desired mechanical properties for a given engineering application.

MAKING JUDGEMENTS
The students will be able to understand how to perform the right heat treatments on metal alloys to get the desired mechanical properties.

COMMUNICATION SKILLS
Description of the microstructure of metallic materials in terms of type and fraction of different phases, and their effect on the mechanical properties.

LEARNING SKILLS
Understanding the relations between microstructural features and mechanical properties of the main families of metal alloys for engineering applications.

LEARNING OUTCOMES
After a careful study during the course the students should be able to:
1. Identify the role of statistics in engineering problems.
2. Discuss the methods used by engineers to collect data.
3. Explain the differences between mechanistic and empirical models.
4. Understand and describe sample spaces and events of random experiments with graphs, tables, lists or tree diagrams.
5. Interpret and use the probability of the results to calculate the probabilities of the events. Calculate the probability of joint events and interpret / calculate the conditional probabilities of events.
6. Apply the Bayes theorem.
7. Understand the meanings of a random variable.
8. Select an appropriate discrete / continuous probability distribution. Determine probability, mean, and variance for the presented discrete / continuous probability distributions.
9. Calculate and interpret mean, variance, standard deviation, median and sample interval.
10. Build and interpret normal probability diagrams.
11. Know the general concepts of estimating the parameters of a population or a probability distribution.
12. Explain the properties of point estimators (bias, variance, mean square error).
13. Construct point estimators with moments method and maximum likelihood method.
14. Calculate and explain the precision of the estimation of a parameter.
15. Understand the central limit theorem.
16. Explain the role of normal distribution as a sampling distribution.
17. Build confidence intervals, forecast intervals, tolerance intervals.
18. Structure engineering decision problems as hypothesis tests.
19. Check the hypotheses on the average of a normal distribution using a Z-test or t-test procedure.
20. Test the hypotheses on variance or standard deviation of a normal distribution. Check the hypotheses on a population.
21. Use the P value approach to make decisions in hypothesis tests.
22. Select a sample size for tests on averages, variances and proportions.
23. Explain and use the relationship between confidence intervals and hypothesis testing.
24. Use the chi-square test to test hypotheses about the distribution.
25. Use simple linear regression to build empirical models of technical and scientific data.
26. Understand the use of the least squares method to estimate parameters in a linear regression model.
27. Analyze the residuals to determine if the regression model fits the data or to see if there are violations of the initial hypotheses.
28. Test the statistical hypotheses and construct confidence intervals on the parameters of the regression model.
29. Use the regression model for the prediction of a future observation and construct an appropriate prediction interval on future observation.
30. Use simple transformations to obtain a linear regression model.
31. Apply the correlation model.
32. Finally, discuss how probabilities and probability models are used in engineering and science in general.

KNOWLEDGE AND UNDERSTANDING
Students acquire understanding and knowledge of: 1) fundamental statistical techniques (summary statistics, normal distribution, interval estimation, regression analysis, modelling) and how they relate to the baseline discipline; 2) software statistical techniques; 3) process monitoring by control charts; 4) process optimization by response surface methodology; 5) determining important factors by hypothesis testing; 6) process modelling by, e.g., regression analysis; 7) design of experiments and laboratory recommendation.
The teaching approach provides the foundation for this understanding, in such a way that at the end of the course students have assimilated a complete knowledge of the basic themes.

APPLYING KNOWLEDGE AND UNDERSTANDING
The goals of the course are to help the students to: i) model and simulate basic engineering problems, ii) collect, analyze and present numerical data in general and simulation results in particular, iii) interpret simulation results by means of statistical methods, iv) use statistical principles and concepts, v) develop software for reporting and for graphical presentation, vi) be familiar with basic probability theory and perform estimation, hypothesis testing, simple correlation-/regression analysis, vii) identify, formulate, and solve engineering problems. Such applications of statistics are widespread in all branches of engineering.

MAKING JUDGEMENTS
The training provided for students of the course is hallmarked by the acquisition of a flexible mentality that helps them to extend the knowledge learned to new concepts, enabling them to introduce elements of innovation. These activities encourage students to develop: critical thinking and problem solving; critical analysis; independence of judgement. At the end of the course, students are therefore able to pose, refine and evaluate scientific questions, this being a fundamental objective both educational and cognitive.

COMMUNICATION SKILLS
Students develop the ability to present clearly what they have learned during the course and, in the same way, the additional knowledge gained from practical exercises, classroom exercises and textbooks. They are expected to present their knowledge effectively. These skills, which concern both oral and written presentations, are based on the ability to analyze and integrate the knowledge areas acquired during the course. Students are also encouraged to develop a positive attitude towards teamwork.
The evaluation of the achievement of written and oral communication skills is verified during classroom exercises, practical exercises, tutoring and through written and oral exams at the end of the course.

LEARNING SKILLS
Students, through the introduction of a range of fundamental statistical techniques, learn how to: analyse data, apply statistics in engineering contexts, use appropriate statistical sofware. Furthermore they acquire: numeracy skills, effective Information retrieval and research skills, computer literacy. On these bases they will be able to connect and relate knowledge across various scales, concepts, and representations “in” and “across” domains.