The course is aimed to provide the student with the basic elements of some physics topics and to describe the methods of physics applied to biological data. At the end of the course, the student will be able to schematize a phenomenon in terms of physical quantities. The student will apply the scientific method to the study of natural phenomena and will be able to critically evaluate similarities and differences between physical systems.
Knowledge and understanding: the student will acquire knowledge of the laws governing kinematics, dynamics, fluid mechanics, thermology and electromagnetism.
Ability to apply knowledge and understanding: the student will be able to evaluate and describe quantitatively the physical phenomena in the areas covered by the study program.
Ability of making judgements: the student will develop autonomy of judgement and critical sense for the correct interpretation of physical phenomena.
Communication skills: the student will acquire the ability to describe physical phenomena with property of language, and to provide an adequate interpretation of them.
Learning skills: The student will acquire adequate cognitive tools for the continuous updating of knowledge and will be able to access the literature specialized in the field of physics applied to biological systems.
The course aims to acquire the main basic concepts of probability and statistics.
General teaching training objectives in terms of learning outcomes:
Knowledge and understanding: The course aims to acquire skills to students about the description of statistical data; Understand the basic terms (population, sample, variable, etc.); Calculation and presentation of frequency distributions; data description with graphical methods; Calculation of central tendency and variability indices; Understand the basis of the assessment of probability of an event and of a random variable; Acquiring concepts related to inferential statistics such as estimates for intervall confidence and hypothesis tests.
Applying knowledge and understanding: identify distributions appropriate to represent the knowledge underlying; solving problems of inferential statistics and probability.
Making judgments : Through concrete examples and case studies, the student will be able to independently develop solutions to specific problems and assess the suitability of a statistical inference problem and solution.
Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical language within the general framework of the analysis of data using statistical methods.
Learning skills: The course aims, as the goal, to provide students with the necessary theoretical and practical methods to address and solve problems independently in the statistical analysis of data.
The teaching method is based on lectures and exercises. Should the circumstances require online or blended teaching, appropriate modifications to what is hereby stated may be introduced, in order to achieve the main objectives of the course.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Learning assessment may also be carried out on line, should the conditions require it.
Introductory part: Physical quantities. SI System of Units. Scalar and vector quantities. Sum and difference between vectors. Scalar product and vector product. Versors. Notes on the theory of errors in experimental measurements. Casual errors and systematic errors.
Elements of Mechanics: Motion in one dimension. Position vector and displacement vector. Uniform rectilinear motion. Average velocity and instantaneous velocity. Average acceleration and instantaneous acceleration. Uniformly accelerated motion. Circular motion. Concept of mass and density. Force concept. Principles of dynamics. Momentum and conservation of momentum. Gravitational Force and Weight Force. Hooke's law. Work done by a force. Power. Definition of Kinetic Energy and Potential Energy. Kinetic energy theorem. Conservative and non-conservative forces. Conservation of mechanical energy. Examples and hints of applications to biomechanics.
Elements of Fluidostatics and Fluid Dynamics: Stationary motion and continuity equation. Stevin's law. Non-viscous fluids: Bernoulli's theorem. Viscous fluids: laminar and turbulent flow. Cohesion forces and surface tension in liquids.
Elements of Thermology: Laws of ideal and real gases. Temperature and heat. Mechanisms of heat transmission: convection, conduction, radiation. Phase transitions.
Elements of Electromagnetism: Properties of electric charges. Insulators and Conductors. Coulomb's law. The Vector Electric Field. Electric potential. Capacity. Electric current. Ohm's law. The magnetic field vector. Magnetic force.
Wave phenomena in biological systems: Notes on the mechanism of hearing. Notes on the mechanism of vision. The spectrum of electromagnetic waves and related biological applications.
Introduction to probability and statistics: • Introduction to probability; • Events; • Definition of probability; • Conditioned events; • Bayes Theorem; • Discrete Random Variables; • Expectation, Variance, Covariance, Standard Deviation; • Bernoulli distribution; Binomial Distribution; Hypergeometric Distirbution; Negative Bionomial Distribution; Geometric Distribution; Poisson Distribution; • Continuos Random Variables; • Uniform distribution; Exponoential Distribution; Gaussian Distribution; • Examples and excercices; • Introduction to Descriptive Statitiscs; • The data, variables, variability, indeces; • Statistical Inference: parameter estimation, statistical tests; •; • Examples and excercies; • Introduction to R.
- Introduzione alla Fisica, MIELE – PISANTI – Edises
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Lantieri PB, Risso D, Ravera G: Statistica medica per le professioni sanitarie, II ed. McGraw-Hill