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Hate crime has become an increasingly pernicious problem in many parts of the world, with numbers of incidents rising to record levels and causing devastating emotional and physical damage to victims, their families and wider communities. Within the UK last June’s EU referendum result was the catalyst for a surge in reports of targeted violence, while similar spikes have been seen within the US since the election of President Trump after a prolonged campaign of heated rhetoric and a slow disavowal of white supremacy. Equally alarming levels of hate crime have been documented across Europe with populist political parties in countries such as France, Denmark, Germany, Hungary and the Netherlands exploiting anti-immigrant sentiment, fuelling the scapegoating of particular minority groups and feeding off widely-held anxieties. Within this context the need for fresh responses to hate crime has become all the more pressing. Despite progress within the domains of scholarship and policy, these escalating levels of hate crime – and the associated rise in tensions, demonisation and hostility towards ‘difference’ that accompanies such spikes – casts doubt over the effectiveness of existing measures and their capacity to protect victims of hate crime. As such, this session draws from extensive fieldwork conducted by the panel members to examine the nature, impact and implications of hate crime. In addition to identifying the different forms that hate crime can take and their associated harms, the panel consider ways in which existing faultlines within criminal justice responses compound the sense of distress and alienation felt by victims from a diverse range of communities. They also explore ways in which criminological debate can reach beyond its own echo chamber to connect with ‘real-world’ hate crime responses and experiences, and call for urgent action to plug the ever-widening chasm between state-level narratives and victims’ lived realities.

This work demonstrates a novel approach to design and optimization of rare-earth free magnetic materials for targeted properties by effectively using various computational and statistical tools. From the open literature, we defined the alloying elements and bounds of their concentrations to develop a new system of Alnico alloys. Initial compositions of candidate alloys were generated using a quasi-random sequence generation algorithm. Response surface methodology approach was used to develop surrogate models to efficiently link alloy chemistry with desired macroscopic properties for these multi-component systems. The most accurate meta-models were used for multi-objective optimization of desired properties by utilizing various evolutionary approaches. Various statistical tools and pattern recognition techniques were used to determine patterns and correlations within the created dataset. Pareto-optimized candidate alloys were experimentally validated and used to improve the accuracy of the response surface generation used by the multi-objective optimizer to find the next generation of Pareto-optimal alloys. Results over the cycles show significant experimentally verified improvement in the properties of these alloys.

Empirical potentials based upon two and three body interactions were applied to the Li+ -C system, assuming the Li+ ions to be distributed inside high-symmetry, single walled carbon nanotubes of different chirality. Structural optimizations for various assemblages were conducted using evolutionary and genetic algorithms, where differential evolution and particle swarm optimization techniques worked satisfactorily. The results were compared with the outcome of some rigorous molecular dynamics simulations. The potential for using the carbon nanotubes in the negative electrode of lithium ion batteries was also critically examined.