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This chapter considers the place of responsibility in confronting ecological sustainability and social equity problems in the food system. We present two illustrations addressing the following question: In what ways does responsibility present a way to close the metabolic rift in line with the vision of the global food sovereignty movement? First, using the example of Metro-Vancouver in Canada, we consider the ways in which urban people claim responsibility for land protection through the concept of urban agrarianism, defined as an urban ethic of care for foodlands, with an associated responsibility to exercise solidarity with those who cultivate and harvest food. Second, we discuss how deepening relational responsibility in legal and regulatory frameworks might hold the corporate food regime accountable in the Canadian context to address their role in and responsibility for mitigating an increasingly risky world, as evidenced by the COVID-19 pandemic. We argue that the responsibility of urban people to mobilise in solidarity with food movements, and against the corporate food regime in particular, will play a critical role in supporting the transition to sustainable and just food systems. This applies both to finding new ways to claim responsibility for this transition and to hold those actors that have disproportionately benefitted from the corporate food regime responsible. Such a reworking of responsibility is especially necessary as the context for food systems change becomes increasingly urbanised and risky.

The Howard Shuttering Contractors case throws considerable light on the importance which the tribunals attach to warnings before dismissing an employee. In this case the tribunal took great pains to interpret the intention of the parties to the different site agreements, and it came to the conclusion that the agreed procedure was not followed. One other matter, which must be particularly noted by employers, is that where a final warning is required, this final warning must be “a warning”, and not the actual dismissal. So that where, for example, three warnings are to be given, the third must be a “warning”. It is after the employee has misconducted himself thereafter that the employer may dismiss.

‘Soldering and Cleaning in Electronics’ international conference, including an exposition, took place in Brno on 12–13 October 1993. The conference was organised by SMT‐Info, together with the ISHM‐Czech and Slovak Chapter. The purpose of this common action was to bring together the professionals in surface mount technology and thick film technology. In the framework of the conference, in which 130 home and foreign delegates participated, the annual meeting of the ISHM‐Czech and Slovak Chapter took place.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.