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Numerous metals can be processed using the additive manufacturing process laser-based powder bed fusion of metals (PBF-LB/M, ISO/ASTM 52900). The main advantages of additive manufacturing technologies are the high degree of design freedom and the cost-effective implementation of lightweight structures. This could be profitable for gears with increased power density, combining reduced mass with considerable material strength. Current research on additively manufactured gears is focused on developing lightweight structures but is seldom accompanied by simulations and even less by mechanical testing. There has been very little research into the mechanical and material properties of additively manufactured gears. The purpose of this study is to investigate the behavior of lightweight structures in additively manufactured gears under static loads.

This research identifies the static load-carrying capacity of helical gears with different lightweight structures produced by PBF-LB/M with the case hardening steel 16MnCr5. A static gear loading test rig with a maximum torque at the pinion of T₁ = 1200 Nm is used. Further focus is set on analyzing material properties such as the relative density, microstructure, hardness depth profile and chemical composition.

All additively manufactured gear variants show no failure or plastic deformation at the maximum test load. The shaft hub connection, the lightweight hub designs and the gearing itself are stable and intact regarding their form and function. The identified material characteristics are comparable to conventionally manufactured gears (wrought and machined), but also some particularities were observed.

This research demonstrates the mechanical strength of lightweight structures in gears. Future research needs to consider the dynamic load-carrying capacity of additively manufactured gears.

The complex and occasionally chaotic nature of health care has been previously described in the literature, as has the broadening recognition that different management approaches are required for different types of problems rather than a “one size fits all” approach. The CYNEFIN framework from Snowden outlines a consistent cognitive approach that offers the leader and leadership team an ability to urgently apply the correct actions to a given situation. This paper proposes a variant CYNEFIN approach for healthcare.

Consistent and accurate decision-making within health care is the hallmark of an effective and pragmatic leader and leadership team. An awareness of how one’s cognitive biases and heuristics may adversely impact on this cognitive process is paramount, as is an understanding of the calibration between fast and slow thinking.

The authors propose a variant CYNEFIN approach for health care of “act-probe-sense-respond” to resolve complex and time-critical emergency scenarios, using the differing contexts of a cardiac arrest and an evolving crisis management problem as examples. The variant serves as a pragmatic sense-making framework for the health-care leader and leadership team that can be adopted for many time-critical crisis situations.

The variant serves as a pragmatic sense-making framework for the health-care leader that can be adopted for many crisis situations.

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.

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.

Conventional time series modeling may not satisfy the model validity for short-period time series data. In this study, we apply the Kernel Variant Multi-Way Principal Component Analysis (KMPCA) to cluster multivariate time series data which havemultiple dimensions with auto- and cross-correlations. We then check whether this method works well in clustering those data by employing simulation for generalization. Two simulation studies with two different mean structures with nine combinations of auto- and cross-correlations were conducted. The results showed that KMPCA cluster two different mean structure groups over 90% success rates with an appropriate kernel function. We also found that when the mean structures are the same, auto-correlation, the number of temporal points, and the kernel function parameter have the statistically significant effects on clustering performance. The second and third order interaction effects with each of those factors also have effects on clustering success rates. Among the effects of the main factors, the kernel function parameter is the most critical factor to consider for obtaining better performance. A similar error structure may obstruct the clustering performance: strong cross-correlation, weak auto-correlation, and a larger number of temporal points. The paper also discussed some limitations of the KMPCA model and suggested directions for future research that could improve the model.

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.

Genetic risk for depressive disorders is poorly understood despite consistent suggestions of a high heritable component. Most genetic studies have focused on risk associated with single variants, a strategy which has so far only yielded small (often non-replicable) risks for depressive disorders. In this paper we argue that more substantial risks are likely to emerge from genetic variants acting in synergy within and across larger neurobiological systems (polygenic risk factors). We show how knowledge of major integrated neurobiological systems provides a robust basis for defining and testing theoretically defensible polygenic risk factors. We do this by describing the architecture of the overall stress response. Maladaptation via impaired stress responsiveness is central to the aetiology of depression and anxiety and provides a framework for a systems biology approach to candidate gene selection. We propose principles for identifying genes and gene networks within the neurosystems involved in the stress response and for defining polygenic risk factors based on the neurobiology of stress-related behaviour. We conclude that knowledge of the neurobiology of the stress response system is likely to play a central role in future efforts to improve genetic prediction of depression and related disorders.

This study aims to apply new modifications by changing the nonlinear logarithmic calculation steps in the method based on the removal effects of criteria (MEREC) method. Geometric and harmonic mean from multiplicative functions is used for the modifications made while extracting the effects of the criteria on the overall performance one by one. Instead of the nonlinear logarithmic measure used in the MEREC method, it is desired to obtain results that are closer to the mean and have a lower standard deviation.

The MEREC method is based on the removal effects of the criteria on the overall performance. The method uses a logarithmic measure with a nonlinear function. MEREC-G using geometric mean and MEREC-H using harmonic mean are introduced in this study. The authors compared the MEREC method, its modifications and some other objective weight determination methods.

MEREC-G and MEREC-H variants, which are modifications of the MEREC method, are shown to be effective in determining the objective weights of the criteria. Findings of the MEREC-G and MEREC-H variants are more convenient, simpler, more reasonable, closer to the mean and have fewer deviations. It was determined that the MEREC-G variant gave more compatible findings with the entropy method.

Decision-making can occur at any time in any area of life. There are various criteria and alternatives for decision-making. In multi-criteria decision-making (MCDM) models, it is a very important distinction to determine the criteria weights for the selection/ranking of the alternatives. The MEREC method can be used to find more reasonable or average results than other weight determination methods such as entropy. It can be expected that the MEREC method will be more used in daily life problems and various areas.

Objective weight determination methods evaluate the weights of the criteria according to the scores of the determined alternatives. In this study, the MEREC method, which is an objective weight determination method, has been expanded. Although a nonlinear measurement model is used in the literature, the contribution was made in this study by using multiplicative functions. As an important originality, the authors demonstrated the effect of removing criteria in the MEREC method in a sensitivity analysis by actually removing the alternatives one by one from the model.

Drawing on a user-centred design perspective, the purpose of this paper is to (i) provide an overview of three contextual user research methods, namely, spatial walkthroughs, experience curve mapping and card sorting, (ii) exemplify their applications in different case studies and (iii) compare the methods according to their contributions for the study of users’ workplace experiences. Previous workplace studies with qualitative approaches mainly rely on methods such as interviews and observations. Although these methods provide rich data, the understanding of office users, their use situations and finding more fitting workplace designs can benefit from deeper user experience insights.

Three methods and their variants were tested in studies of user experience in flexible offices: spatial walkthroughs, experience curve mapping and card sorting. The methods were tested during workshops and interviews in four case studies with a total of 114 participants.

Spatial walkthroughs were more immersive and provided the most insights on the actual context with respect to spatial design qualities, while experience curve mapping enabled understanding the temporal aspects of the user experience and card sorting enabled exploring user experiences with respect to predetermined spatial qualities and contextual aspects.

Spatial walkthroughs, experience curve mapping and card sorting methods have not previously been applied in workplace studies. They facilitate dialogue, participation and user involvement and provide insights for making evidence-based recommendations for designing or redesigning office environments that fit users’ needs and preferences.

The purpose of the study was to investigate the alignment between current product and manufacturing systems and how it could be achieved.

Case study research method was chosen for the collection and analysis of empirical data. The data was of qualitative nature and was collected using research techniques such as observations through video recordings of processes, documents and open and semi-structured interviews.

The variation of outer side sub-element of the exterior wall element was found to not be aligned with its corresponding assembly. A hybrid assembly of outer side sub-elements characterised by flexibility and reconfigurability can be developed.

The study is limited to the exterior wall element and corresponding manufacturing system.

The presented approach was formulated with the aim to be used both for the analysis of existing products and manufacturing systems as well as for the design of new manufacturing systems.

So far, this is the first study in the context of timber house building where the alignment between product and manufacturing systems was investigated by considering product variety and flexibility of manufacturing systems.