BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
This research contributes to understanding and shaping systems for OFMSW separation at urban Small and Medium Enterprises (SMEs, such as offices, shops and service providers). Separating SMEs’ organic fraction of municipal solid waste (OFMSW) is both an opportunity and a serious challenge for the transition towards circular cities. It is an opportunity because OFMSW represents approximately 40% of the total waste mass generated by these companies. It is challenging because post-collection separation is not feasible for OFMSW. Therefore, SMEs disposing of waste should separate their solid waste so that processing the organic fraction for reuse and recycling is practical and attainable. However, these companies do not experience direct advantages from the extra efforts in separating waste, and much of the OFMSW ends up in landfills, often resulting in unnecessary GHG emissions. Therefore, governments and waste processors are looking for ways to improve the OFMSW separation degree by urban companies disposing of waste through policies for behaviour change.There are multiple types of personnel at companies disposing of waste. These co-workers act according to their values, beliefs and norms. They adapt their behaviour continuously, influenced by the physical environment, events over time and self-evaluation of their actions. Therefore, waste separation at companies can be regarded as a Socio-Technical Complex Adaptive System (STCAS). Agent-based modelling and simulation are powerful methods to help understand STCAS. Consequently, we have created an agent-based model representing the evolution of behaviour regarding waste separation at companies in the urban environment. The model aims to show public and private stakeholders involved in solid waste collection, transport and processing to what extent behaviour change policies can shape the system towards desired waste separation degrees.We have co-created the model with participants utilising literature and empirical data from a case study on the transition of the waste collection system of a business park located at a former harbour area in Amsterdam, The Netherlands. First, a conceptual model of the system and the environment was set up through participatory workshops, surveys and interviews with stakeholders, domain experts and relevant actors. Together with our case participants, five policies that affect waste separation behaviour were included in the model. To model the behaviour of each company worker’s values, beliefs and norms during the separation and disposal of OFMSW, we have used the Value-Belief-Norm (VBN) Theory by Stern et al. (1999). We have collected data on waste collection behaviour and separation rates through interviews, workshops and a literature study to operationalise and validate the model.Simulation results show how combinations of behaviour profiles affect waste separation rates. Furthermore, findings show that single waste separation policies are often limitedly capable of changing the behaviour in the system. Rather, a combination of information and communication policies is needed to improve the separation of OFMSW, i.e., dissemination of a newsletter, providing personal feedback to the co-workers disposing of waste, and sharing information on the (improvement of) recycling rates.This study contributes to a better understanding of how policies can support co-workers’ pro-environmental behaviour for organic waste separation rates at SMEs. Thus, it shows policymakers how to stimulate the circular transition by actively engaging co-workers’ waste separation behaviour at SMEs. Future work will extend the model’s purpose by including households and policies supporting separating multiple waste types aimed at various R-strategies proposed by Potting et al. (2016).
MULTIFILE
01-07-2023
