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Zoekresultaten (23)

HBO/WO

MBO

1 oktober 2025

An analysis of the current developments and scenarios for the transition to a fossil-free energy society this century

This century, greenhouse gas emissions such as carbon dioxide, methane, and nitrogen oxides must be significantly reduced. Greenhouse gases absorb and emit infrared radiation that contributes to global warming, which can lead to irreversible negative consequences for humans and the environment. Greenhouse gases are caused by the burning of fossil fuels such as crude oil, coal, and natural gas, but livestock farming and agriculture are also to blame. In addition, deforestation contributes to more greenhouse gases. Of the natural greenhouse gases, water vapor is the main cause of the greenhouse effect, accounting for 90%. The remaining 10% is caused from high to low by carbon dioxide, methane, nitrogen oxides, chlorofluorocarbons, and ozone. In addition, there are industrial greenhouse gases such as fluorinated hydrocarbons, sulphur hexafluoride, and nitrogen trifluoride that contribute to the greenhouse effect too. Greenhouse gases are a major cause of climate change, with far-reaching consequences for the welfare of humans and animals. In some regions, extreme weather events like rainfall are more common, while others are associated with more extreme heat waves and droughts. Sea level rise caused by melting ice and an increase in forest fires are undesirable effects of climate change. Countries in low-lying areas fear that sea level rise will force their populations to move to the higher-lying areas. Climate change is affecting the entire world. An estimated 30-40% o f the carbon dioxide released by the combustion of fossil fuels dissolves into the surface water, resulting in an increased concentration of hydrogen ions. This causes the seawater to become more acidic, resulting in a decreasing of carbonate ions. Carbonate ions are an important building block for forming and maintaining calcium carbonate structures of organisms such as oysters, mussels, sea urchins, shallow water corals, deep sea corals, and calcareous plankton.

Open (Access) Tekst Boek

Folkert Koopman

15 september 2025

FiReGlass - Glass beams with defined fire resistance

In this recorded presentation from glasstec 2024, Maximilian Möckel, from TU Dresden, discusses fire resistent glass beams. In the construction industry, the application of load-bearing laminated safety glass (LSG), such as glass beams or glass columns, is of particular importance in terms of preventive structural fire protection. LSG with a transformation temperature of individual components of up to 550 °C conse-quently loses its load-bearing capacity. In co-operation between Hero Fire GmbH, Taupitz GmbH & Co. KG and the Institute of Building Construction of the TUD Dresden University of Technology, LSG-beams were developed for use as load-bearing elements with defined fire resistance. The newly developed FiReGlass beam compris-es a load-bearing LSG core, which is protected from the fire exposure by two protection systems. The glass surfaces and lower glass edges are protected by an all-round protection system compris-ing fire protection gel and sacrificial panes. This system is integrated into the support structure. These new developments enable the realization of comprehensive fire protection and defined fire resistance. For further details, please refer to the linked publication. Special thanks to Messe Düsseldorf GmbH for providing the footage, and to Maximilian Möckel for contributing to the Open GLASSroom OER collection. The creation of these resources has been (partially) funded by the ERASMUS+ grant program of the European Union under grant no. 2022-1-DE01-KA220-HED-000089750. Neither the European Commission nor the project's national funding agency DAAD are responsible for the content or liable for any losses or damage resulting of the use of these resources.

Video

Maximilian Möckel

6 juni 2025

How To Write A Quantitative Research Paper

So, you need to write a ‘quantitative’ research paper, and you want to know: what is the correct way of writing such a paper? The answer is, unfortunately: it depends … For one, there is no universally accepted, ‘correct’ way of writing any paper in the social sciences in general. Moreover, even just within the so-called ‘quantitative’ research tradition specifically, there exist different viewpoints on what makes for a good research paper. To muddy things up even further, how to write a paper will also depend on the specific research question(s) that it addresses. Hence, even the same scientist might opt for varying approaches in different papers. Having said that, it is possible to identify some common rules and practices that are often followed, as well as a number of practices that should generally be avoided. Based on these common rules and practices, this document is meant to help you on your way with writing a ‘quantitative’ research paper by offering a number of tips, tricks and rules-of-thumb. None of these rules and practices are set in stone, and you (or your teacher/supervisor) may have excellent arguments to deviate from them. However, in general, following them will greatly increase the quality of your paper.

Document

Alex Lehr, Roderick Sluiter

2024

Rubric for assessment of MSc thesis at Wageningen University

This is the rubric to be used for the assessment of MSc thesis at Wageningen University.

Document

Arnold Moene, Mieke Latijnhouwers

2024

Rubric for assessment of MSc research practice at Wageningen University

This rubric is to be used for the assessment of an MSc research practice.

Document

Arnold Moene, Mieke Latijnhouwers

1 september 2023

Practical: Presenting statistical data

Understand and demonstrate the data representation for AI and ML modelling. Apply a range of plotting techniques to visualise data. Be able to check for fairness, handle outliers and missing data.

Document

HCAIM Consortium

23 augustus 2023

Lecture: Understanding data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

Website

HCAIM Consortium

23 augustus 2023

Lecture: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium

23 augustus 2023

Tutorial: Understanding Data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

Website

HCAIM Consortium

23 augustus 2023

Lecture: Trust Models and Trust quantification

None

Website

HCAIM Consortium