Reframing Public Inquiries as 'Procedural Justice' for Victims of Institutional Child Abuse: Towards a Hybrid Model of Justice

As the number of high profile cases of institutional child abuse mounts internationally, and the demands of victims for justice are heard, state responses have ranged from prosecution, apology, and compensation schemes, to truth commissions or public inquiries. Drawing on the examples of Australia and Northern Ireland as two jurisdictions with a recent and ongoing history of statutory inquiries into institutional child abuse, the article utilises the restorative justice paradigm to critically evaluate the strengths and limitations of the inquiry framework in providing ‘justice’ for victims. It critically explores the normative and pragmatic implications of a hybrid model as a more effective route to procedural justice and suggests that an appropriately designed restorative pathway may augment the legitimacy and utility of the public inquiry model for victims chiefly via improving offender accountability and ‘voice’ for victims. The article concludes by offering some thoughts on the broader implications for other jurisdictions in responding to large-scale historical abuses and seeking to come to terms with the legacy of institutional child abuse.

Effect of weather and the hybrid energy storage on the availability of standalone microgrid

This paper presents a model for availability analysis of standalone hybrid microgrid. The microgrid used in the study consists of wind, solar storage and diesel generator. Boolean driven Markov process is used to develop the availability of the system in the proposed method. By modifying the developed model, the relationship between the availability of the system with the fine (normal) weather and disturbed (stormy) weather durations are analyzed. Effects of different converter technologies on the availability of standalone microgrid were investigated and the results have shown that the availability of microgrid increased by 5.80 % when a storage system is added. On the other hand, the availability of standalone microgrid could be overestimated by 3.56 % when weather factor is neglected. In the same way 200, 500 and 1000 hours of disturbed weather durations reduced the availability of the system by 5.36%, 9.73% and 13.05 %, respectively. In addition, the hybrid energy storage cascade topology with a capacitor in the middle maximized the system availability.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Projected equations of motion approach to hybrid quantum/classical dynamics in dielectric-metal composites

We introduce a hybrid method for dielectric-metal composites that describes the dynamics of the metallic system classically whilst retaining a quantum description of the dielectric. The time-dependent dipole moment of the classical system is mimicked by the introduction of projected equations of motion (PEOM) and the coupling between the two systems is achieved through an effective dipole-dipole interaction. To benchmark this method, we model a test system (semiconducting quantum dot-metal nanoparticle hybrid). We begin by examining the energy absorption rate, showing agreement between the PEOM method and the analytical rotating wave approximation (RWA) solution. We then investigate population inversion and show that the PEOM method provides an accurate model for the interaction under ultrashort pulse excitation where the traditional RWA breaks down.

Intelligent DMU creation: Nominal CAD model preparation for automated tolerance allocation

This paper examines the integration of a tolerance design process within the Computer-Aided Design (CAD) environment having identified the potential to create an intelligent Digital Mock-Up [1]. The tolerancing process is complex in nature and as such reliance on Computer-Aided Tolerancing (CAT) software and domain experts can create a disconnect between the design and manufacturing disciplines It is necessary to implement the tolerance design procedure at the earliest opportunity to integrate both disciplines and to reduce workload in tolerance analysis and allocation at critical stages in product development when production is imminent.
The work seeks to develop a methodology that will allow for a preliminary tolerance allocation procedure within CAD. An approach to tolerance allocation based on sensitivity analysis is implemented on a simple assembly to review its contribution to an intelligent DMU. The procedure is developed using Python scripting for CATIA V5, with analysis results aligning with those in literature. A review of its implementation and requirements is presented.

Physical model parameter optimisation for calibrated emulation of the Dallas Rangemaster Treble Booster guitar pedal

In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.