Initial insights on the biodiversity potential of biosolar roofs: A London Olympic Park green roof case study

Cities dominated by impervious artificial surfaces can experience myriad negative environmental impacts. Restoration of green infrastructure has been identified as a mechanism for increasing urban resilience, enabling cities to transition towards sustainable futures in the face of climate-driven change. Building rooftops represent a viable space for integrating new green infrastructure into high density urban areas. Urban rooftops also provide prime locations for photovoltaic (PV) systems. There is increasing recognition that these two technologies can be combined to deliver reciprocal benefits in terms of energy efficiency and biodiversity targets. Scarcity of scientific evaluation of the interaction between PVs and green roofs means that the potential benefits are currently poorly understood. This study documents evidence from a biodiversity monitoring study of a substantial biosolar roof installed in the Queen Elizabeth Olympic Park. Vegetation and invertebrate communities were sampled and habitat structure measured in relation to habitat niches on the roof, including PV panels. Ninety-two plant species were recorded on the roof and variation in vegetation structure associated with proximity to PV panels was identified. Almost 50% of target invertebrate species collected were designated of conservation importance. Arthropod distribution varied in relation to habitat niches on the roof. The overall aim of the MPC green roof design was to create a mosaic of habitats to enhance biodiversity, and the results of the study suggest that PV panels can contribute to niche diversity on a green roof. Further detailed study is required to fully characterise the effects of PV panel density on biodiversity.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Most Significant Change in conflict settings: staff development through monitoring and evaluation

Capoeira4Refugees is an NGO that uses the Afro-Brazilian art-form of Capoeira to promote psychosocial well-being in children affected by conflict and occupation. Capoeira4Refugees introduced the Most Significant Change (MSC) methodology to monitor and evaluate project implementation and impact across two locations in the Middle East. Analysis of interviews conducted with five field staff revealed that in line with, and building on previous research, MSC became an empowering tool that led to staff development. The potential for MSC to build staff reflexivity, independence and leadership has implications for other organisations working in conflict areas, particularly in situations of remote management.