Designing a visible city for visually impaired users

This paper reports on an ongoing doctoral research project which aims to identify the main barriers to access within the built environment for persons with a visual impairment. The research seeks to investigate whether these barriers are common for all types of visual impairment and degree of vision loss and if so, what inclusive design solutions can accommodate the needs of the majority of visually impaired users. An access audit has been conducted within Glasgow city centre which sought to quantify the number and type of hazards present within a typical built environment. This was followed up by a questionnaire which asked participants to rate factors which may prevent them from making independent visits to their nearest city centre including psychological factors, physical features and obstructions resulting from the presence of street furniture. Participants also indicated the colours and contrasts which they find easiest to detect within the built environment. These findings will be used to inform the creation of a new set of design guidelines to assist designers, architects and urban planners as to how they can provide more accessible and inclusive environments for the visually impaired population

State-space approach to nonlinear predictive generalized minimum variance control

A Nonlinear Predictive Generalized Minimum Variance (NPGMV) control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process but because of the assumed structure of the system the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well known GPC controller

Life history of the Critically Endangered largetooth sawfish: a compilation of data for population assessment and demographic modelling

© The authors 2021. Open Access under Creative Commons by Attribution Licence. Use, distribution and reproduction are un - restricted. Authors and original publication must be credited. The largetooth sawfish Pristis pristis is a Critically Endangered, once widespread shark-like ray. The species is now extinct or severely depleted in many former parts of its range and is protected in some other range states where populations persist. The likelihood of collecting substantial new biological information is now low. Here, we review all available life history information on size, age and growth, reproductive biology, and demography as a resource for population assessment and demographic modelling. We also revisit a subset of historical data from the 1970s to examine the maternal size−litter size relationship. All available information on life history is derived from the Indo-West Pacific (i.e. northern Australia) and the Western Atlantic (i.e. Lake Nicaragua-Río San Juan system in Central America) subpopulations. P. pristis reaches a maximum size of at least 705 cm total length (TL), size-at-birth is 72−90 cm TL, female size-at-maturity is reached by 300 cm TL, male size-at-maturity is 280−300 cm TL, age-at-maturity is 8−10 yr, longevity is 30−36 yr, litter size range is 1−20 (mean of 7.3 in Lake Nicaragua), and reproductive periodicity is suspected to be biennial in Lake Nicaragua (Western Atlantic) but annual in Australia (Indo-West Pacific). There was a weak relationship between litter size and maternal size in Lake Nicaragua, and lifetime reproductive output for an individual female from Lake Nicaragua was estimated as 73 pups. Future demographic models should aim to capture the variability and uncertainty in life history parameters for P. pristis and we encourage a conservative approach to any application for conservation and management

Efficiency of nitrogen fertilizers for rice

The photosynthetic biomass that develops in the floodwater of wetland rice fields affects nitrogen dynamics in the ecosystem. This review summarizes available data on the nature, productivity, and composition of the photosynthetic aquatic biomass, and its major activities regarding the nitrogen cycle, i.e., nitrogen fixation by free living blue-green algae and #Azolla$, nitrogen trapping, nitrogen accumulation at the soil surface, its effect on nitrogen losses by ammonia volatilization, nitrogen recycling, and the supply of nitrogen to the rice crop. (Résumé d'auteur

Non-Linear Systems Identification Using Neural Networks

Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems. This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer. New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach

Ecosystem CO2 and CH4 exchange in a mixed tundra and a fen within a hydrologically diverse Arctic landscape: 1. Modeling versus measurements

CO2 and CH4 exchange are strongly affected by hydrology in landscapes underlain by permafrost. Hypotheses for these effects in the model ecosys were tested by comparing modeled CO2 and CH4 exchange with CO2 fluxes measured by eddy covariance from 2006 to 2009, and with CH4 fluxes measured with surface chambers in 2008, along a topographic gradient at Daring Lake, NWT. In an upland tundra, rises in net CO2 uptake in warmer years were constrained by declines in CO2 influxes when vapor pressure deficits (D) exceeded 1.5kPa and by rises in CO2 effluxes with greater active layer depth. Consequently, net CO2 uptake rose little with warming. In a lowland fen, CO2 influxes declined less with D and CO2 effluxes rose less with warming, so that rises in net CO2 uptake were greater than those in the tundra. Greater declines in CO2 influxes with warming in the tundra were modeled from greater soil-plant-atmosphere water potential gradients that developed under higher D in drained upland soil, and smaller rises in CO2 effluxes with warming in the fen were modeled from O2 constraints to heterotrophic and belowground autotrophic respiration from a shallow water table in poorly drained lowland soil. CH4 exchange modeled during July and August indicated very small influxes in the tundra and larger effluxes characterized by afternoon emission events caused by degassing of warming soil in the fen. Emissions of CH4 modeled from degassing during soil freezing in October-November contributed about one third of the annual total

Non-Linear Systems Identification Using Radial Basis Functions

This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial-basis-function centres from a large number of possible candidates and this provides, for the first time, a fully automatic selection procedure for identifying parsimonious radial-basis-function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach

Practical Identification of Narmax Models Using Radial Basis Functions.

A wide class of discrete time non-linear systems can be represented by the non-linear autoregressive moving average model with exogenous inputs or NARMAX model. This paper develops a practical algorithm for identifying NARMAX models based on radial basis functions from noise corrupted data. The algorithm consists of an iterative orthogonal-forward-regression routine coupled with model validity tests. The orthogonal-forward-regression routine selects parsimonious radial-basis-function models while the model validity tests measure the quality of fit. The modelling of a liquid level system and an automotive diesel engine are included to demonstrate the effectiveness of the identification procedure