Super suburbia: developing resilience by closing resource cycles in North Belfast

Sustainability can be described as having three interlinked strands, known as the ‘trias energetica’, without which resilience is difficult to achieve. These strands are environmental, social and economic: and if taken as indicators, the suburbs of North Belfast are very poorly performing indeed. Places such as Ligoneal and Glen Cairn have poor housing stock energetically, and also little economic activity. This paper describes propositional work completed by Queens University and Belfast City Council as part of the UK’s Technology Strategy Board’s Future Cities Programme, which aimed to develop new synergies in these neighbourhoods by the insertion of closed cycle economies.

By utilising a research by design methodology, the paper develops a process-based and phased design to develop a new emergent form to these neighbourhoods, one in which new productive systems are embedded into the city, at a small-scales. These include a peak-load hydro-electric project in Ligoneal; a productive landscape in Glen Cairn and a city-wide energy refurbishment utilising neighbourhood waste streams.

These designs allow for a roadmap for development to be created that could change the modus operandi of an area over a relatively short period of time, and show that even modest investments of productive technologies at a local scale could fundamentally change the form and the economic and environmental operation of the city in the future, and create a new resilient city, one that can be less externally dependent and more socially just.

New suburbia: designing in urban resilience by closing resource cycles

By utilising a research by design methodology, the paper develops a process-based and phased design to develop a new emergent form to these neighbourhoods, one in which new productive systems are embedded into the city, at a small-scales. These include a peak-load hydro-electric project in Ligoneal; a productive landscape in Glen Cairn and a city-wide energy refurbishment utilising neighbourhood waste streams.

The three projects illustrate different ways in which place-based solutions can enact urban transformation through a process of rigorous visualisation of process, and its attendant changes in content and form of the neighbourhood, These designs, based around a process-based strategy plan, allow for a roadmap for development to be created that could change the modus operandi of an area over a relatively short period of time,. The paper demonstrates that even modest investments of productive technologies at a local scale can fundamentally change the form and the economic and environmental operation of the city in the future, and create a new resilient city, one that can have resilience built-in. This resilience allows the neighbourhood to be less externally dependent on resources, economically active and more socially just.

Kernel-based local order estimation of nonlinear nonparametric systems

We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NARX), which may have different local dimensions at different points. By minimizing the kernel-based local information criterion introduced in this paper, the strongly consistent estimates for the local orders of the NARX system at points of interest are obtained. The modification of the criterion and a simple procedure of searching the minimum of the criterion, are also discussed. The theoretical results derived here are tested by simulation examples.

Genetic maternity and paternity in a local population of armadillos assessed by microsatellite DNA markers and field data.

Genetic data from polymorphic microsatellite loci were employed to estimate paternity and maternity in a local population of nine-banded armadillos (Dasypus novemcinctus) in northern Florida. The parentage assessments took advantage of maximum likelihood procedures developed expressly for situations when individuals of neither gender can be excluded a priori as candidate parents. The molecular data for 290 individuals, interpreted alone and in conjunction with detailed biological and spatial information for the population, demonstrate high exclusion probabilities and reasonably strong likelihoods of genetic parentage assignment in many cases; low mean probabilities of successful reproductive contribution to the local population by individual armadillo adults in a given year; and statistically significant microspatial associations of parents and their offspring. Results suggest that molecular assays of highly polymorphic genetic systems can add considerable power to assessments of biological parentage in natural populations even when neither parent is otherwise known.

The direction aftereffect is driven by adaptation of local motion detectors

The processing of motion information by the visual system can be decomposed into two general stages; point-by-point local motion extraction, followed by global motion extraction through the pooling of the local motion signals. The direction aftereVect (DAE) is a well known phenomenon in which prior adaptation to a unidirectional moving pattern results in an exaggerated perceived direction diVerence between the adapted direction and a subsequently viewed stimulus moving in a diVerent direction. The experiments in this paper sought to identify where the adaptation underlying the DAE occurs within the motion processing hierarchy. We found that the DAE exhibits interocular transfer, thus demonstrating that the underlying adapted neural mechanisms are binocularly driven and must, therefore, reside in the visual cortex. The remaining experiments measured the speed tuning of the DAE, and used the derived function to test a number of local and global models of the phenomenon. Our data provide compelling evidence that the DAE is driven by the adaptation of motion-sensitive neurons at the local-processing stage of motion encoding. This is in contrast to earlier research showing that direction repulsion, which can be viewed as a simultaneous presentation counterpart to the DAE, is a global motion process. This leads us to conclude that the DAE and direction repulsion reflect interactions between motion-sensitive neural mechanisms at different levels of the motion-processing hierarchy.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.