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Proton beam therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors, while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beam can make this form of therapy more precise and safer for the patient during delivery. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. Unfortunately, some of the Compton camera data is flawed and the reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Previous work used a deep residual fully connected neural network. The use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage. Importantly in a clinical setting, the time to load the model from disk is significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment. 

Quaternary MAX phases, (Ta 1−x Ti x ) 3 AlC 2 ( x = 0.4, 0.62, 0.75, 0.91 or 0.95), have been synthesised via pressureless sintering of TaC, TiC, Ti and Al powders. Via chemical etching of the Al layers, (Ta 0.38 Ti 0.62 ) 3 C 2 T z – a new MXene, has also been synthesised. All materials contain an M-layer solid solution of Ta and Ti, with a variable Ta concentration, paving the way for the synthesis of a range of alloyed (Ta,Ti) 3 C 2 T z MXenes with tuneable compositions for a wide range of potential applications. 

ABSTRACT Social norms – rules governing which behaviours are deemed appropriate or inappropriate within a given community – are typically taken to be uniquely human. Recently, this position has been challenged by a number of philosophers, cognitive scientists, and ethologists, who have suggested that social norms may also be found in certain non‐human animal communities. Such claims have elicited considerable scepticism from norm cognition researchers, who doubt that any non‐human animals possess the psychological capacities necessary for normative cognition. However, there is little agreement among these researchers about what these psychological prerequisites are. This makes empirical study of animal social norms difficult, since it is not clear what we are looking for and thus what should count as behavioural evidence for the presence (or absence) of social norms in animals. To break this impasse, we offer an approach that moves beyond contested psychological criteria for social norms. This approach is inspired by the animal culture research program, which has made a similar shift away from heavily psychological definitions of ‘culture’ to become organised around a cluster of more empirically tractable concepts of culture. Here, we propose an analogous set of constructs built around the core notion of anormative regularity, which we define asa socially maintained pattern of behavioural conformity within a community. We suggest methods for studying potential normative regularities in wild and captive primates. We also discuss the broader scientific and philosophical implications of this research program with respect to questions of human uniqueness, animal welfare and conservation.