In the current discourses on sustainable development, one can discern two main intellectual cultures: an analytic one focusing on measuring problems and prioritizing measures, (Life Cycle Analysis (LCA), Mass Flow Analysis (MFA), etc.) and; a policy/management one, focusing on long term change, change incentives, and stakeholder management (Transitions/niches, Environmental economy, Cleaner production). These cultures do not often interact and interactions are often negative. However, both cultures are required to work towards sustainability solutions: problems should be thoroughly identified and quantified, options for large change should be guideposts for action, and incentives should be created, stakeholders should be enabled to participate and their values and interests should be included in the change process. The paper deals especially with engineering education. Successful technological change processes should be supported by engineers who have acquired strategic competences. An important barrier towards training academics with these competences is the strong disciplinarism of higher education. Raising engineering students in strong disciplinary paradigms is probably responsible for their diminishing public engagement over the course of their studies. Strategic competences are crucial to keep students engaged and train them to implement long term sustainable solutions.
Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
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In September 2009 the department of Engineering of Fontys University of Applied Sciences in the Netherlands has started a pilot honours program for excellent engineering students called PRogram OUstanding Development (PROUD). Aim of this program is to give those engineering students, who have the ambition, the opportunity to work on extra profession related challenges in their study. By means of this PROUD program Fontys University of Applied Sciences is responding to the wishes of students for extra curricular activities and increasing need from the industry for excellent professionals with an extra level of theoretical knowledge and practical experience. In this paper the courses offered at the Engineering department of the Fontys University of Applied Sciences are discussed. Different study possibilities/routings for students were developed depending on earlier acquainted competences, adaptation abilities to our system (special possibilities for slow starters) and tracking and tracing by intensive study coaching. This resulted in an improvement of the yield of students to 74% of students started in 2008. After working successfully on reducing the drop out rate of our engineering students the department focused on possibilities for excellent students. The department started the PROUD pilot together with engaged engineering students. In 2008 engineering students have carried out a research among their fellow students, lecturers, other institutes [1] and industry. This resulted in a quite different approach of an honours program for the department of Electronic and Electrical Engineering. In the PROUD program the student is stimulated to personally shape his educational career and to explicitly work on developing his own competences. The PROUD excellent program starts after the first year and extends to at least 3 semesters in the following years. The student, guided by a supervisor and outside the regular study time, is working on building an excellent portfolio at the university as well as in industry. During this period the PROUD student will work in industry one day a week in average. This is on top of his bachelor educational program. The students will receive an excellent honours certificate together with their bachelor's degree at the end of the study to express their honourable work. Each year about 20 students apply for a place in PROUD but thus far only about 3-4 passed the first interview round. It turns out that student, university and industry are eager to participate in this PROUD program.
The SPRONG-collaboration “Collective process development for an innovative chemical industry” (CONNECT) aims to accelerate the chemical industry’s climate/sustainability transition by process development of innovative chemical processes. The CONNECT SPRONG-group integrates the expertise of the research groups “Material Sciences” (Zuyd Hogeschool), “Making Industry Sustainable” (Hogeschool Rotterdam), “Innovative Testing in Life Sciences & Chemistry” and “Circular Water” (both Hogeschool Utrecht) and affiliated knowledge centres (Centres of Expertise CHILL [affiliated to Zuyd] and HRTech, and Utrecht Science Park InnovationLab). The combined CONNECT-expertise generates critical mass to facilitate process development of necessary energy-/material-efficient processes for the 2050 goals of the Knowledge and Innovation Agenda (KIA) Climate and Energy (mission C) using Chemical Key Technologies. CONNECT focuses on process development/chemical engineering. We will collaborate with SPRONG-groups centred on chemistry and other non-SPRONG initiatives. The CONNECT-consortium will generate a Learning Community of the core group (universities of applied science and knowledge centres), companies (high-tech equipment, engineering and chemical end-users), secondary vocational training, universities, sustainability institutes and regional network organizations that will facilitate research, demand articulation and professionalization of students and professionals. In the CONNECT-trajectory, four field labs will be integrated and strengthened with necessary coordination, organisation, expertise and equipment to facilitate chemical innovations to bridge the innovation valley-of-death between feasibility studies and high technology-readiness-level pilot plant infrastructure. The CONNECT-field labs will combine experimental and theoretical approaches to generate high-quality data that can be used for modelling and predict the impact of flow chemical technologies. The CONNECT-trajectory will optimize research quality systems (e.g. PDCA, data management, impact). At the end of the CONNECT-trajectory, the SPRONG-group will have become the process development/chemical engineering SPRONG-group in the Netherlands. We can then meaningfully contribute to further integrate the (inter)national research ecosystem to valorise innovative chemical processes for the KIA Climate and Energy.
CRISPR/Cas genome engineering unleashed a scientific revolution, but entails socio-ethical dilemmas as genetic changes might affect evolution and objections exist against genetically modified organisms. CRISPR-mediated epigenetic editing offers an alternative to reprogram gene functioning long-term, without changing the genetic sequence. Although preclinical studies indicate effective gene expression modulation, long-term effects are unpredictable. This limited understanding of epigenetics and transcription dynamics hampers straightforward applications and prevents full exploitation of epigenetic editing in biotechnological and health/medical applications.Epi-Guide-Edit will analyse existing and newly-generated screening data to predict long-term responsiveness to epigenetic editing (cancer cells, plant protoplasts). Robust rules to achieve long-term epigenetic reprogramming will be distilled based on i) responsiveness to various epigenetic effector domains targeting selected genes, ii) (epi)genetic/chromatin composition before/after editing, and iii) transcription dynamics. Sustained reprogramming will be examined in complex systems (2/3D fibroblast/immune/cancer co-cultures; tomato plants), providing insights for improving tumor/immune responses, skin care or crop breeding. The iterative optimisations of Epi-Guide-Edit rules to non-genetically reprogram eventually any gene of interest will enable exploitation of gene regulation in diverse biological models addressing major societal challenges.The optimally balanced consortium of (applied) universities, ethical and industrial experts facilitates timely socioeconomic impact. Specifically, the developed knowledge/tools will be shared with a wide-spectrum of students/teachers ensuring training of next-generation professionals. Epi-Guide-Edit will thus result in widely applicable effective epigenetic editing tools, whilst training next-generation scientists, and guiding public acceptance.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
