The complexity of process plants and the growing demand for digitalization require efficient and accurate information retrieval throughout the lifecycle phases of a process plant. This paper discusses the concept of instantiation and introduces a method for identifying and multiplying required information in plant engineering using scalable so-called Instantiation Blocks linked to the Bill of Material. Core functionality, an ontology graph and a user interface based on Python and React are developed to demonstrate the implementation of the framework and validate its effectiveness in practice.

A computer-based model was constructed to assess enrichment materials (EMats) for intensively-farmed weaned, growing and fattening pigs on a scale from 0 to 10. This model, called RICHPIG, was constructed in order to support the further implementation of EC Directive 2001/93/EC, which states that “pigs must have permanent access to a sufficient quantity of material to enable proper investigation and manipulation activities”. This paper describes the underlying conceptual framework for assessing EMats and explains the concepts, procedures and calculation rules used for semantic modelling. A (parsimonious) weighted average calculation rule was used to calculate enrichment scores from assessment criteria scores (which specify welfare relevant material properties of EMats) and weighting factors (WFs, which specify the relative importance of the assessment criteria). In total, 30 assessment criteria were identified and classified as object design criteria (eg novelty and accessibility), behavioural elements (eg nose, root, chew), biological functions (explore and forage), manipulations (ie object-directed behaviours), other (non-manipulative) consequences (eg aggression and stress) and object performance criteria (eg changeability/destructibility and hygiene). WFs were calculated from a systematic analysis of 573 scientific statements collected in the database, using 11 so-called weighting categories (Wcat, ie scientific paradigms to assess welfare such as the study of natural behaviour, consumer demand studies and stress-physiology) to assign Wcat level scores (which indicate the intensity, duration and incidence of a welfare impact) to the assessment criteria. The main advantages of the RICHPIG model are that it is based explicitly on available scientific information, that it has an explicitly formulated conceptual framework, is transparent, disputable, upgradeable, robust and reasonably in accordance with expert opinion. Major scope for improvements exist in the form of the need for further upgrading with new knowledge, empirical validation and (further) implementation in political decision-making processes.

The present study investigated the operational feasibility of the recently developed Salmon Welfare Index Model (SWIM 1.0) designed for Atlantic salmon (Salmo salar L) in production cages. Ten salmon farms containing spring smolts were visited twice, first between May and June the first year in sea cages, and secondly 2-3 months later. On each farm the SWIM 1.0 assessments were carried out for the two cages assumed by the farmer to represent the best and worst welfare status. The applied welfare indicators (WIs) were water temperature, salinity, stocking density, lighting, disturbance, daily mortality rate, appetite, sea lice infestation ratio, condition factor, emaciation state, vertebral deformation, maturation stage, smoltification state, fin condition and skin condition. The effective time to carry out the welfare evaluation was about 1.5 h per farm. The results showed some marked differences between visits; relatively larger proportions of emaciated fish were sampled during the first compared to the second visit, and more homogeneous scores of skin and fin damage were found on the second visit. The overall welfare index scores were generally in accordance with the farmers’ ranking of the ‘best’ and the ‘worst’ sea cage during the first visit. Together, the findings of this study suggest that the SWIM model may be employed for documentation of animal welfare over the salmon marine production cycle. The results call attention towards re-assessment of some of the welfare indicators, improved sampling methods, and a more user-friendly interface. All-in-all, the current SWIM model is regarded as a promising candidate tool towards welfare assessment of farmed salmon.

Animals have various behavioural and physiological needs that are important for welfare. Fulfilment of these needs depends on the quality of housing, management and animal characteristics. The objective of this study was to develop a model to assign welfare scores to husbandry systems for dairy cattle, based on scientific results, and thereby supporting the design of new, welfare-friendly systems. COWEL is a computer-based decision support system that contains attributes regarding housing and management conditions. These attributes are technical specifications that contain various technical units called levels. These levels are ranked from best-to-worst regarding welfare, based on scientific information about animal-based parameters. This information, inserted in the model as statements, was weighted depending on the impact it has on welfare by using weighting categories. Thereafter, a weighting factor was calculated for each attribute which determines how important an attribute is for welfare. The COWEL model contains 2,343 statements on dairy cattle welfare from 476 sources found during a literature survey. The model was applied to four husbandry systems, namely a tie-stall, cubicle housing, a straw yard and a pasture-based system. The welfare scores, calculated by COWEL for these husbandry systems, correspond with the general opinion about these systems. A tie-stall receives a low and a pasture-based system a high welfare score: 211 and 271, respectively. A husbandry system can receive a maximum of 313 on the welfare scale of COWEL. We conclude that COWEL can be used to rank husbandry systems on a welfare scale, and may be a useful tool to develop new, sustainable and welfare-friendly systems for dairy cattle.

Most modern production systems, especially in temperate climates, do not offer wallowing facilities to pigs and, to date, this has neither generated much concern in welfare science nor public debate on pig welfare. Nevertheless, wallowing is a natural behaviour of pigs which may be important to them. This paper systematically examines the overall importance of wallowing for pig welfare using principles developed in semantic modelling. As a first step, relevant citations were collected from the scientific literature. Secondly, since the importance of the attribute (‘wallowing’) is dependent upon the discrepancy between its best and worst levels, these levels were specified in relation to the status quo in pig husbandry, ie no pool (even during periods of overheating) and the ideal mud pool, respectively. Criteria for an ideal mud pool were formulated in terms of pool location and size, substrate, thermal conditions, body care and hygiene. Thirdly, available scientific information about wallowing was systematically described in relation to ten so-called weighting categories identified in semantic modelling (pain and illness, survival/heat stress, fitness, stress, aggression, abnormal behaviour, frustration, natural behaviour, preferences and demand). Fourthly, the welfare importance of wallowing was assessed by tentatively comparing it to several other welfare attributes, such as food, foraging substrate, social contact and non-castration. This leads to the suggestion that wallowing is important for pig welfare because of its multifaceted nature. It may even be very important when other forms of thermoregulation are sub-optimal. This paper, finally, discusses the ‘ethical room for manoeuvre’ concerning the (non-) implementation of mud pools in practice. An integrated approach is suggested to address related scientific, technological and ethical issues, because stakeholders are faced not only with scientific and technological gaps in knowledge but also with economical, ecological, food-safety and psychological barriers. As an important element of natural behaviour and positive welfare, the subject may provide an opportunity for pig farming. This should be recognised more explicitly in transition processes towards fully sustainable pig production systems.

Product Lifecycle Management (PLM) is one of the most business-critical IT backbones of manufacturing companies. It often consists of numerous, rigidly interwoven monolithic applications and is seen as synonymous with costly maintenance, lack of extensibility, and poor scalability. This paper proposes an approach for transforming a monolithic PLM landscape into a federated Domain and Data Mesh. This enhances semantic interoperability and enables data-driven use cases by treating data as first-class citizens. User-centric PLM domains moreover help to increase productivity in the workplace.

The comprehensive implementation of digital technologies in product manufacturing leads to changes in engineering processes and requires new approaches to data management. An important role belongs to the processes of organizing the collection, storage and reuse of research data obtained and used in the process of product, system or technology development, taking into account the FAIR data principles. This article describes a Research Data Management System for the organization of documentation and measurement requests in the research and development of new oxygen-free production technologies.