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Purpose — The purpose of the chapter is to make retrospective data from biographic surveys comparable with traditional cross-section travel surveys, by correcting some biases attached to the biographic collection method. This is applied to a biographic survey passed in France within the 2007–2008 national travel survey.

Methodology/approach — The methodology implemented deals with three specific biases: the general survey sampling and response rate, the survival bias, due to differential surviving rates according to generations, and the geographical bias, as biog‘raphies were not passed in all regions. All biases were corrected by computing specific weightings.

Findings — One main finding is that with these three corrections, biographic data can yield modal shares for commuting trips to work and for commuting trips to education that are similar to those derived from the historical cross-section surveys about regular trips.

Research limitations/implications — Though biographic collection suffers from the memory effect, this effect remains low and does not disturb the modal shares derived from biographies.

The most challenging issue is that of missing generations that contributed to past mobility. But they can be replaced by modeling with an age-period model.

Practical implications — The chapter provides methodology to correct biographic data to reconstitute historical behavior.

Social implications — Exploring the memory of living people is essential to save data about the past, that otherwise could be lost, although they may be useful to understand present behavior and future likely trends.

Originality/value of chapter — Investigating biographic surveys is a new topic in the field of transport survey methods.

Fused filament fabrication (FFF) is one of the most popular additive manufacturing (AM) technologies due to its ability to build thermoplastic parts with complex geometries at low cost. The FFF technique has been mainly used for rapid prototyping owing to the poor mechanical and geometrical properties of pure thermoplastic parts. However, both the development of new fibre-reinforced filaments with improved mechanical properties, and more accurate composite 3D printers have broadened the scope of FFF applications to functional components. FFF is a complex process with a large number of parameters influencing product quality and mechanical properties, and the effects of the combined parameters are usually difficult to evaluate. An array of parameter combinations has been analysed for improving the mechanical performance of thermoplastic parts such as layer thickness, build orientation, raster angle, raster width, air gap, infill density and pattern, fibre volume fraction, fibre layer location, fibre orientation and feed rate. This study aims to assess the effects of nozzle diameter on the mechanical performance and the geometric properties of 3D printed short carbon fibre-reinforced composites processed by the FFF technique.

Tensile and three-point bending tests were performed to characterise the mechanical response of the 3D printed composite samples. The dimensional accuracy, the flatness error and surface roughness of the printed specimens were also evaluated. Moreover, manufacturing costs, which are related to printing time, were evaluated. Finally, scanning electron microscopy images of the printed samples were analysed to estimate the porosity as a function of the nozzle diameter and to justify the effect of nozzle diameter on dimensional accuracy and surface roughness.

The effect of nozzle diameter on the mechanical and geometric quality of 3D printed composite samples was significant. In addition, large nozzle diameters tended to increase mechanical performance and enhance surface roughness, with a reduction in manufacturing costs. In contrast, 3D printed composite samples with small nozzle diameter exhibited higher geometric accuracy. However, the effect of nozzle diameter on the flatness error and surface roughness was of slight significance. Finally, some print guidelines are included.

The effect of nozzle diameter, which is directly related to product quality and manufacturing costs, has not been extensively studied. The presented study provides more information regarding the dependence of the mechanical, microstructural and geometric properties of short carbon fibre-reinforced nylon composite components on nozzle diameter.

This chapter aims to demonstrate that the fundamental human rights principle that no one should be subjected to (grossly) disproportionate punishment should be interpreted to take into account terminal illness of the offender. It should be applied both during imposition of the sentences and also during execution of already imposed sentences.

In order to reveal whether this principle takes into account serious medical conditions, including terminal illness of the offender in the calculus of the proportionality of punishment and whether it is applicable at the execution stage of sentences, this chapter examined the roots of the fundamental human rights principle of proportionality of punishment by briefly surveying the penal theory, jurisprudence, court cases, laws, and legislative history from the U.S. federal and state jurisdictions and from Europe.

There is a consensus among surveyed theories that terminal illness of the offender is an element of the principle of proportionality of punishment. Thus the fundamental human rights principle must be interpreted to take it into account. The principle should be observed not only at the imposition stage, but also at the execution stage of already imposed sentences.

This chapter re-examines the roots of the fundamental human right to not being subjected to (grossly) disproportionate punishment. It does so in order to demonstrate that the right should be interpreted to take into account terminal illness of the offender and that it should be observed not only at the imposition stage, but also at the execution stage of already imposed sentences.

The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines.

Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices.

It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller.

This study aims at using artificial intelligence in gas turbine control systems.

This paper provides a novel methodology for control of gas turbines.