The imprint of the geographical, evolutionary and ecological context on species-area relationships

Species-area relationships (SAR) are fundamental in the understanding of biodiversity patterns and of critical importance for predicting species extinction risk worldwide. Despite the enormous attention given to SAR in the form of many individual analyses, little attempt has been made to synthesize these studies. We conducted a quantitative meta-analysis of 794 SAR, comprising a wide span of organisms, habitats and locations. We identified factors reflecting both pattern-based and dynamic approaches to SAR and tested whether these factors leave significant imprints on the slope and strength of SAR. Our analysis revealed that SAR are significantly affected by variables characterizing the sampling scheme, the spatial scale, and the types of organisms or habitats involved. We found that steeper SAR are generated at lower latitudes and by larger organisms. SAR varied significantly between nested and independent sampling schemes and between major ecosystem types, but not generally between the terrestrial and the aquatic realm. Both the fit and the slope of the SAR were scale-dependent. We conclude that factors dynamically regulating species richness at different spatial scales strongly affect the shape of SAR. We highlight important consequences of this systematic variation in SAR for ecological theory, conservation management and extinction risk predictions.

Fractal species distributions do not produce power-law species-area relationships

We derive the species-area relationship (SAR) expected from an assemblage of fractally distributed species. If species have truly fractal spatial distributions with different fractal dimensions, we show that the expected SAR is not the classical power-law function, as suggested recently in the literature. This analytically derived SAR has a distinctive shape that is not commonly observed in nature: upward-accelerating richness with increasing area (when plotted on log-log axes). This suggests that, in reality, most species depart from true fractal spatial structure. We demonstrate the fitting of a fractal SAR using two plant assemblages (Alaskan trees and British grasses). We show that in both cases, when modelled as fractal patterns, the modelled SAR departs from the observed SAR in the same way, in accord with the theory developed here. The challenge is to identify how species depart from fractality, either individually or within assemblages, and more importantly to suggest reasons why species distributions are not self-similar and what, if anything, this can tell us about the spatial processes involved in their generation.

RSSI-based environment identification for 2.45 GHz body area networks

A unique property of body area networks (BANs) is the mobility of the network as the user moves freely around. This mobility represents a significant challenge for BANs, since, in order to operate efficiently, they need to be able to adapt to the changing propagation environment. A method is presented that allows BAN nodes to classify the current operating environment in terms of multipath conditions, based on received signal strength indicator values during normal packet transmissions. A controlled set of measurements was carried out to study the effect different environments inflict on on-body link signal strength in a 2.45 GHz BAN. The analysis shows that, by using two statistical parameters, gathered over a period of one second, BAN nodes can successfully classify the operating environment for over 90% of the time.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

The Use of High Surface Area Silica and Supercritical Carbon Dioxide to Enhance Solubility of Poorly Water-Soluble Drugs

Purpose Poor water-solubility of BCS class II drugs can limit their commercialization because of reduced oral bioavailability. It has been reported that loading of drug by adsorption onto porous silica would enhance drug solubility due to the increased surface area available for solvent diffusion. In this work, solid dispersions are formed using supercritical carbon dioxide (scCO2). The aim of this research was to characterise the solid-state properties of scCO2 dispersion and to investigate the impact of altering scCO2 processing conditions on final amorphous product performance that could lead to enhancement of drug dissolution rate for BCS class II drugs. Methods Indomethacin (IND) was purchased from Sigma-Aldrich (Dorset, UK) and was used as a model drug with two grades of high surface area silica (average particle sizes 3&[micro] and 7&[micro]), which were obtained directly from Grace-Davison (Germany). Material crystallinity was evaluated using powder X-ray diffraction (PXRD, Rigaku™, miniflex II, Japan) and high-speed differential scanning calorimetry (Hyper-DSC 8000, Perkin Elmer, USA). Materials were placed in a high-pressure vessel consisting of a CO2 cylinder, a Thar™ Technologies P50 high-pressure pump and a 750 ml high-pressure vessel (Thar, USA). Physical mixtures were exposed to CO2 gas above its critical conditions. SEM imaging and elemental analysis were conducted using a Jeol 6500 FEGSEM (Advanced MicroBeam Inc., Austria). Drug release was examined using USP type II dissolution tester (Caleva™, UK). Results The two grades of silica were found to be amorphous using PXRD and Hyper-DSC. Using PXRD, it was shown that an increase in incubation time and pressure resulted in a decrease in the crystalline content. Drug release profiles from the two different silica formulations prepared under the same conditions are shown in Figure 1. It was found that there was a significant enhancement in drug release, which was influenced, by silica type and other experiment conditions such as temperature, pressure and exposure time. SEM imaging and elemental analysis showed drug deposited inside silica pores as well as on the outer surface. Conclusion This project has shown that silica carrier platforms may be used as an alternative approach to generating polymeric solid dispersions of amorphous drugs exhibiting enhanced solubility.

Review of the area planning process (a)

The area planning process continues and formal recommendations arising from it are now being brought forward as development proposals to amalgamate or close schools. At this point in the process the Education Committee requested some comment on five different aspects of the process: the impact of Area Planning to date; the validity of the surplus school vacancy calculation methodology; the Annual Area Profile information; the Needs Model (including as appropriate the implications of the Drumragh judgement); possible enhancements to the consultation and communication process;the facilitation of alternative cross-sectoral or cross-border solutions. This paper offers high level comment on each of these issues. In addition, the Education Committee facilitated a stakeholder consultation meeting in Parliament Buildings on 4 February, 2015. The broad themes that emerged in that discussion will also be mentioned in this paper.

Review of the area planning process (b)

This paper offers a commentary on the area planning reports form primary schools published by each of the Education and Library Boards (ELB) in June 2014. The format of the reports are broadly similar for each ELB, although there are some differences amongst them. All provide an overview on the policy context for the area planning process, a statistical picture of the schools in the ELB and detail on the issues considered for sets of schools within the ELB.

The influence of area level social deprivation on preoperative disease severity and postoperative outcomes following unicompartmental knee joint replacement

BACKGROUND: This study investigated the effect of socioeconomic deprivation on preoperative disease and outcome following unicompartmental knee replacement (UKR).

METHODS: 307 Oxford UKRs implanted between 2008 and 2013 under the care of one surgeon using the same surgical technique were analysed. Deprivation was quantified using the Northern Ireland Multiple Deprivation Measure. Preoperative disease severity and postoperative outcome were measured using the Oxford Knee Score (OKS).

RESULTS: There was no difference in preoperative OKS between deprivation groups. Preoperative knee range of motion (ROM) was significantly reduced in more deprived patients with 10° less ROM than least deprived patients. Postoperatively there was no difference in OKS improvement between deprivation groups (p=0.46), with improvements of 19.5 and 21.0 units in the most and least deprived groups respectively. There was no significant association between deprivation and OKS improvement on unadjusted or adjusted analysis. Preoperative OKS, Short Form 12 mental component score and length of stay were significant independent predictors of OKS improvement. A significantly lower proportion of the most deprived group (15%) reported being able to walk an unlimited distance compared to the least deprived group (41%) one year postoperatively.

CONCLUSION: More deprived patients can achieve similar improvements in OKS to less deprived patients following UKR.

LEVEL OF EVIDENCE: 2b - retrospective cohort study of prognosis.

Joint Optimization of Area Spectral Efficiency and Delay over PPP Interfered Ad-Hoc Networks

To evaluate the performance of the co-channel transmission based communication, we propose a new metric for area spectral efficiency (ASE) of interference limited ad-hoc network by assuming that the nodes are randomly distributed according to a Poisson point processes (PPP). We introduce a utility function, U = ASE/delay and derive the optimal ALOHA transmission probability p and the SIR threshold τ that jointly maximize the ASE and minimize the local delay. Finally, numerical results have been conducted to confirm that the joint optimization based on the U metric achieves a significant performance gain compared to conventional systems.

A Largest Matching Area Approach to Image Denoising

Given the success of patch-based approaches to image denoising,this paper addresses the ill-posed problem of patch size selection.Large patch sizes improve noise robustness in the presence of good matches, but can also lead to artefacts in textured regions due to the rare patch effect; smaller patch sizes reconstruct details more accurately but risk over-fitting to the noise in uniform regions. We propose to jointly optimize each matching patch’s identity and size for gray scale image denoising, and present several implementations.The new approach effectively selects the largest matching areas, subject to the constraints of the available data and noise level, to improve noise robustness. Experiments on standard test images demonstrate our approach’s ability to improve on fixed-size reconstruction, particularly at high noise levels, on smoother image regions.