Modification of the 3D printer for the processing of the high-performance thermoplastic polymers in the production process of transport system components

Nowadays, the ability to process the high-performance thermoplastic polymers has been still the domain of the industrial 3D printers. This article describes the possibilities of the commercial 3D printer modification to achieve the required boundary conditions for the processing of the high-performance thermoplastics with adequate mechanical properties. These materials have a very high melting point, and it is inevitable to ensure this increased temperature in the entire print volume. Therefore, it was necessary to perform the numerical simulations focused mainly on the thermal load of the 3D printer construction. Consequently, the simulation results were experimentally verified, and the optimal material in terms of its density and mechanical properties was selected

Modification of the 3D printer for the processing of the high-performance thermoplastic polymers in the production process of transport system components

Nowadays, the ability to process the high-performance thermoplastic polymers has been still the domain of the industrial 3D printers. This article describes the possibilities of the commercial 3D printer modification to achieve the required boundary conditions for the processing of the high-performance thermoplastics with adequate mechanical properties. These materials have a very high melting point, and it is inevitable to ensure this increased temperature in the entire print volume. Therefore, it was necessary to perform the numerical simulations focused mainly on the thermal load of the 3D printer construction. Consequently, the simulation results were experimentally verified, and the optimal material in terms of its density and mechanical properties was selected

Momentous Choices: Testing nonstandard decision models in health and housing markets

__Abstract__ During more than half a century, several strands of research contributed to the development of decision theory. The standard normative model for choice under uncertainty – expected utility – was given a foundation by von Neumann and Morgenstern (1944) and Savage (1954). It advised – and expected – reasonable actors t

Utility Independence of Multiattribute Utility Theory is Equivalent to Standard Sequence Invariance of Conjoint Measurement

Utility independence is a central condition in multiattribute utility theory, where attributes of outcomes are aggregated in the context of risk. The aggregation of attributes in the absence of risk is studied in conjoint measurement. In conjoint measurement, standard sequences have been widely used to empirically measure and test utility functions, and to theoretically analyze them. This paper shows that utility independence and standard sequences are closely related: utility independence is equivalent to a standard sequence invariance condition when applied to risk. This simple relation between two widely used conditions in adjacent fields of research is surprising and useful. It facilitates the testing of utility independence because standard sequences are flexible and can avoid cancelation biases that affect direct tests of utility independence. Extensions of our results to nonexpected utility models can now be provided easily. We discuss applications to the measurement of quality-adjusted life-years (QALY) in the health domain

Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning

Making case-based decision theory directly observable

Case-based decision theory (CBDT) provided a new way of revealing preferences, with decisions under uncertainty determined by similarities with cases in memory. This paper introduces a method to measure CBDT that requires no commitment to parametric families and that relates directly to decisions. Thus, CBDT becomes directly observable and can be used in prescriptive applications. Two experiments on real estate investments demonstrate the feasibility of our method. Our implementation of real incentives not only avoids the income effect, but also avoids interactions between different memories. We confirm CBDT's predictions except for one violation of separability of cases in memory

Making Case-Based Decision Theory Directly Observable

Case-based decision theory (CBDT) provided a new way of revealing preferences, with decisions under uncertainty determined by similarities with cases in memory. This paper introduces a method to measure CBDT that requires no commitment to parametric families and that relates directly to decisions. Thus, CBDT becomes directly observable and can be used in prescriptive applications. Two experiments on real estate investments demonstrate the feasibility of our method. Our implementation of real incentives not only avoids the income effect, but also avoids interactions between different memories. We confirm CBDT's predictions except for one violation of separability of cases in memory