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Care homes for older people may become places of deprivation of liberty due to their residents’ lack of capacity to consent to their stay. The Optional Protocol to the Convention Against Torture requires States Parties to establish National Preventive Mechanisms to undertake visits to all places of deprivation of liberty to prevent torture and other ill treatment. This paper discusses the ways in which National Preventive Mechanisms report on human rights concerns in older people’s care homes across 26 Council of Europe states. A framework established by the European Committee for the Prevention of Torture provides a starting point for this analysis. Common themes around restraint, ill treatment, safeguards for involuntary placements, and resourcing issues emerge. Against a backdrop of concerns heightened by the Covid-19 pandemic, the paper concludes that a narrow focus on individual human rights needs to be supplemented by addressing the structural issues on which the more broadly conceived rights of residents of care homes for older people depend.

This article presents a case study of an innovative course requested, funded and designed by the West London Social Work Teaching Partnership (WLSWTP). Due to difficulties in recruiting social work...

The Infrastructure as a Service (IaaS) platform in cloud computing provides resources as a service from a pool of compute, network, and storage resources. One of the major challenges facing cloud computing is to predict the usage of these resources in real time. By knowing future demands, cloud data centres can dynamically scale resources to decrease energy consumption while maintaining a high quality of service. However cloud resource consumption is ever changing, making it difficult for accurate predictions to be produced. This motivates the research presented in this paper which aims to predict in advance the level of CPU consumption of a host. This research implements evolutionary Neural Networks (NN), a powerful machine learning method, to make these predictions. A number of state of the art swarm and evolutionary optimization algorithms are implemented to train the neural networks to predict host utilization: Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The results of this research demonstrate that CMA-ES converges faster to a better solution on the training data. However when evaluated on the test data, DE performs statistically equal to CMA-ES. The results also demonstrate that the trained networks are still accurate when applied to CPU utilization data from different hosts with no further training needed. When evaluated to predict multiple steps into the future, the accuracy of the network understandably decreases but still performs well on average.

Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions.

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined., 17 pages, 3 figures

Collecting, analyzing and gaining insight from large volumes of data is now the norm in an ever increasing number of industries. Data analytics techniques, such as machine learning, are powerful tools used to analyze these large volumes of data. Synthetic data sets are routinely relied upon to train and develop such data analytics methods for several reasons: to generate larger data sets than are available, to generate diverse data sets, to preserve anonymity in data sets with sensitive information, etc. Processing, transmitting and storing data is a key issue faced when handling large data sets. This paper presents an "On the fly" framework for generating big synthetic data sets, suitable for these data analytics methods, that is both computationally efficient and applicable to a diverse set of problems. An example application of the proposed framework is presented along with a mathematical analysis of its computational efficiency, demonstrating its effectiveness., Comment: 13 pages, 3 figures

Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning (MORL) the reward signal is a vector, where each component represents the performance on a different objective. Reward shaping is a well-established family of techniques that have been successfully used to improve the performance and learning speed of RL agents in single-objective problems. The basic premise of reward shaping is to add an additional shaping reward to the reward naturally received from the environment, to incorporate domain knowledge and guide an agent’s exploration. Potential-Based Reward Shaping (PBRS) is a specific form of reward shaping that offers additional guarantees. In this paper, we extend the theoretical guarantees of PBRS to MORL problems. Specifically, we provide theoretical proof that PBRS does not alter the true Pareto front in both single- and multi-agent MORL. We also contribute the first published empirical studies of the effect of PBRS in single- and multi-agent MORL problems.