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The transition to a circular economy in the built environment is key to achieving a resource-effective society. The built environment can be made more circular by applying circular building components. The purpose of this paper is to present a design tool that can support industry in developing circular building components.

The tool was developed and tested in five steps. In Step 1, the authors analysed existing circular design frameworks to identify gaps and develop requirements for the design tool (Step 2). In Step 3, the authors derived circular design parameters and options from existing frameworks. In Step 4, the authors combined and specified these to develop the “circular building components generator” (CBC-generator). In Step 5, the CBC-generator was applied in the development of an exemplary component: the circular kitchen and tested in a student workshop.

The CBC-generator is a three-tiered design tool, consisting of a technical, industrial and business model generator. These generators are “parameter based”; they consist of a parameter-option matrix and design canvasses. Different variants for circular components can be synthesised by filling the canvasses through systematically “mixing and matching” design options.

The developed tool does not yet support establishing causal links between “parameter-options” and identification of the most circular design variant.

The CBC-generator provides an important step to support the building industry in developing and implementing circular building components in the built environment.

Whilst existing tools and frameworks are not comprehensive, nor specifically developed for designing circular building components, the CBC-generator successfully supports the integral design of circular building components. First, it provides all the design parameters which should be considered; second, it provides extensive design options per parameter; and third, it supports systematic synthesis of design options to a cohesive and comprehensive circular design.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

The purpose of this research is to support the customization ability for industrial house building companies striving to offer individualized products but with a strategy which includes a production facility. This is accomplished by analyzing the as-is state in terms of existing engineering assets and by proposing a to-be state using the design platform and product lifecycle management (PLM) support.

This study is based on design research methodology and collected data are in-depth interviews, document reviews and workshops and method development. The theoretical baseline is product platforms and the design platform.

The analysis showed that despite use of a platform, inherent assets are disorganized. Still, the identified object-based engineering assets were possible to include in a conceptual proposal for better management, both in the process and product view, using an asset relationship matrix and a PLM system.

The results should be applicable for industrial house building and off-site construction companies and offers an approach to identify and manage their assets and platforms which are crucial to stay competitive.

Previous research on design platforms has focused on engineer-to-order companies within the mechanical industry. The contribution of this paper lies in the application and support of the design platform for industrial house building and the introduction of PLM system support.

The study consists of a literature study and a case study. The need for a method via which to handle instruction complexity was identified in both studies. The proposed method was developed based on methods from the literature and experience from the case company.

The purpose of the study presented in this paper is to investigate how linking different maintenance domains in a modular maintenance instruction architecture can help reduce the complexity of maintenance instructions.

The proposed method combines knowledge from the operational and physical domains to reduce the number of instruction task variants. In a case study, the number of instruction task modules was reduced from 224 to 20, covering 83% of the maintenance performed on emergency shutdown valves.

The study showed that the other methods proposed within the body of maintenance literature mainly focus on the development of modular instructions, without the reduction of complexity and non-value-adding variation observed in the product architecture literature.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase.

Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated.

Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value.

A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research.

Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system.

There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential.

To better understand the reality of living with the diseases and conditions that its drugs and therapies are developed to treat, the Novartis leadership determined a need for more meaningful insights into patients’ lives. They sought to develop a systematic, creative methodology – informed by the psychology of insightful rather than analytical thinking – to properly integrate and deploy the research commissioned into its day-to-day business decision-making. For it is well established that better understanding of the patient reality drives both compliance and adherence “beyond the pill”. The purpose of this paper is to bring the novel methodology of creativity to a wider audience and ensure that many others – notably in patient advocacy organizations – can benefit from this approach.

A core team of Insight and Analytics and Patient Engagement leads from various therapeutic area teams worked in partnership with a psychologist and practitioner in the field of insightful thinking, to develop an effective methodology that could reliably surface and articulate genuine patient insights. This methodology – the i4i Insights Discovery™ process – was developed, piloted, refined and codified in 2020 and implemented across the company in 2021–2022. It uses a combination of convergent and divergent thinking techniques – human rather than artificial intelligence, combining diverse research outputs – to understand patients’ lives better. With enhanced understanding, the insights then shape educational and behavioral strategies to drive adherence and compliance.

At a time of tightening budgets and demands to deliver enhanced impact from research budgets, i4i Insights Discovery™ has enabled Novartis teams to turn existing research outputs into profound and useful understandings of what it means to live with specific diseases and develop evidence-based patient engagement strategies; insight-driven decision-making around the lifecycle of any compound. i4i Insights Discovery™ has been applied across Novartis’s diverse areas of expertise, from heart disease to cancer, from organ transplantation to dermatology, from food allergy to ophthalmology.

The i4i Insights Discovery™ process enables Novartis teams to gain deeper understanding of patients’ lives without the need to commission additional research; to do more with less. These insights enable cross-functional Novartis teams to develop better-informed strategies that better address the needs of patients and their care partners, of health-care professionals and health-care systems. The team creating the process is looking to make the i4i Insights Discovery™ approach a gold standard of insight discovery, both for pharma and health care and in other categories, too.

The i4i Insights Discovery™ process is a practical, novel application of well-established principles in the psychology of insightful thinking to address a clear business imperative. By repurposing and reinterpreting existing research outputs using creative verbal and visual exercises, it delivers a more human and empathetic understanding of the patient reality. It moves teams from “So what?” – this is what the data mean – to “Now what?” – this is what we should do as a result.

Decisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.

The nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.

Embodied energy and CO₂ emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.

This research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.

In this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.

Immersive virtual reality (IVR) has been frequently proposed as a promising tool for learning. However, researchers have commonly implemented a plethora of design elements in these IVR systems, which makes the specific aspects of the system that are necessary to achieve beneficial outcomes unclear. Against this background, this study aims to combine the literature on presence with learning theories to propose that the ability of IVR to present 3D objects to users improves the presence of these objects in the virtual environment compared with 2D objects, leading to increased learning performance.

To test this study’s hypotheses, the authors conducted a 2 (training condition: approach vs avoid) x 2 (object presence: high vs low) between-subjects laboratory experiment that used IVR with 83 female participants.

The results support this study’s hypotheses and show that training with high object presence leads to greater reactions to cues (chocolate cravings) and improved health behaviour (chocolate consumption).

This study shows that increased object presence leads to unique experiences for users, which help reinforce training effects. Moreover, this work sheds further light on how immersive computer technologies can affect user attitudes and behaviour. Specifically, this work contributes to IVR research by showing that learning effects can be enhanced through an increased degree of object presence.

The purpose of this paper is to determine best practices of aid agencies for outsourcing logistics to commercial logistics service providers (LSPs) in disaster relief. Moreover, it evaluates the application of the Delphi method for research in humanitarian logistics.

The paper is based on a two-round Delphi study with 31 experts from aid agencies and a complementary full-day focus group with 12 experts from aid agencies and LSPs.

The study revealed 12 best practices for outsourcing logistics in disaster relief and a compilation of more than 100 activities for putting these practices into action. Experts consider a proper balance between efficiency and compliance, a detailed contract and a detailed service request most important. Additionally, the Delphi method was found to be a promising technique for research on humanitarian logistics.

By critically examining the Delphi method, this study establishes the basis for a wider application of the technique in the field of humanitarian logistics. Furthermore, it can help to prioritize future research as the ranking of practices reflects the priorities of practitioners.

The paper provides guidance to practitioners at aid agencies in charge of outsourcing logistics.

This research is one of the first in the field of humanitarian logistics to apply the Delphi method. Moreover, it addresses the lack of literature dealing with approaches for building successful cross-sectoral partnerships.