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Randomised controlled trial (RCT) methodology is viewed as the gold standard in evaluating the impact of interventions. Recruitment problems present one threat to the validity of RCTs, yet the barriers to recruitment are poorly understood. The purpose of this study is to identify obstacles to recruitment in a personality disorder (PD) treatment trial, with a view to suggesting ways of overcoming these obstacles.

A discussion group of 13 staff involved in the trial was held to identify barriers to recruitment. The information was subject to thematic analysis.

Eight themes were identified, and three overarching themes: reluctance to diagnose and preference for treating symptoms; increasing the burden and jeopardising other services; and lack of confidence in treatment and in treatment as usual.

Suggestions for minimising recruitment obstacles include careful site selection and protocol negotiation; education and training about PD; continued promotion of both research in general and the specific trial; and assurances about good research practice.

Recruitment is a common problem in RCTs, and the paper addresses this issue, not only in identifying obstacles to recruitment but also in offering suggestions to other trialists for minimising the obstacles.

Industries and businesses are pursuing Industry 4.0 technologies as well as adopting a circular approach focused on improving manufacturing processes through the reduction of wastes, CO₂ emissions and mineral exploration to mitigate the impact of climate change. In this sense, additive manufacturing (AM), often referred to as 3D printing, can play a key role in the closed-loop of operations. However, academics and practitioners have scarcely discussed the feasibility of implementing AM alongside circular economy (CE) practices, the techniques and methods that this would require, or how AM could benefit sustainability and circularity. To address these gaps, this paper proposes a novel circular sustainable 3D printing model for scrap recycling in the automotive industry.

The methodology uses a literature review-based approach followed by empirical research using metal scraps as the raw material for fabricating a powder to input a metal 3D printer for generating sustainable automotive components. A conceptual sustainable circular model for the automotive industry is proposed. Next, is conducted a focus group comprises AM and automotive industry experts for evaluations.

The results indicate that the proposed model can be used to reintroduce waste back into the manufacturing chain as raw material for the on-demand manufacture and supply of automotive components and that it may also have social and environmental implications.

This paper’s contributions are threefold: it explores the combined use of Industry 4.0 (I4.0), CE and sustainability in the automotive industry, develops a new model to support the circularity and sustainability of the scrap chain and proposes the use of AM as a catalyst of CE practices by reproducing recycled components with a 3D printer for prototypes or fully functioning components.

This paper aims to introduce and illustrate a computational technique capable of determining the geometry and complex permittivity of a supplementary dielectric insert making distributions of microwave-induced dissipated power within the processed material as uniform as possible.

The proposed technique is based on a 3D electromagnetic model of the cavity containing both the processed material and the insert. Optimization problem is formulated for design variables (geometrical and material parameters of the insert) identified from computational tests and an objective function (the relative standard deviation [RSD]) introduced as a metric of the field uniformity. Numerical inversion is performed with the method of sequential quadratic programming.

Functionality of the procedure is illustrated by synthesis of a dielectric insert in an applicator for microwave fixation. Optimization is completed for four design variables (two geometrical parameters, dielectric constant and the loss factor of the insert) with 1,000 points in the database. The best three optimal solutions provide RSD approximately 20 per cent, whereas for the patterns corresponding to all 1,000 non-optimized (randomly chosen) sets of design variables this metric is in the interval from 27 to 136 per cent with the average of 78 per cent.

As microwave thermal processing is intrinsically inhomogeneous and the heating time is not a part of the underlying model, the procedure is able to lead only to a certain degree of closeness to uniformity and is intended for applications with high heating rates. The initial phase of computational identification of design variables and their bounds is therefore very important and may pre-condition the “quality” of the optimal solution. The technique may work more efficiently in combination with advanced optimization techniques dealing with “smart” (rather than random) generation of the data; for the use with more general microwave heating processes characterized by lower heating rates, the technique has to use the metric of non-uniformity involving temperature and heating time.

While the procedure can be used for computer-aided design (CAD) of microwave applicators, a related practical limitation may emerge from the fact that the material with particular complex permittivity (determined in the course of optimization) may not exist. In such cases, the procedure can be rerun for the constant values of material parameters of the available medium mostly close to the optimal ones to tune geometrical parameters of the insert. Special manufacturing techniques capable of producing a material with required complex permittivity also may be a practical option here.

Non-uniformity of microwave heating remains a key challenge in the design of many practical applicators. This paper suggests a concept of a practical CAD and outlines corresponding computational procedure that could be used for designing a range of applied systems with high heating rates.