The detection of automated perimetric stimuli in ocular disease

Automated perimetry has made viable a rapid threshold examination of the visual field and has reinforced the role of perimetry in the diagnostic procedure. The aim of this study was twofold: to isolate the influence of certain extraneous factors on the sensitivity gradient, since these might limit the early detection and accurate monitoring of visual field loss and to investigate the efficacy of certain novel combinations of stimulus parameters in the detection of early visual field loss. The work was carried out with particular reference to glaucoma and to ocular hypertension. The effects of media opacities on the visual field were assessed by forward intraocular light scatter (n= 15) and were found to mask diffuse glaucomatous visual field loss and underestimate focal loss. Correction of the visual field indices for the effects of forward intraocular light scatter (n= 26) showed the focal losses to be, in reality, unaffected. Measurements of back scatter underestimated forward intraocular light scatter (n= 60) and the resultant depression of the visual field. Perimetric sensitivity improved with patient learning (n= 25) and exhibited eccentricity- and depth-dependency effects whereby improvements in sensitivity were greatest for peripheral areas of the field and for those areas which initially demonstrated the lowest sensitivity. The effects of practice were retained over several months (n= 16). Perimetric sensitivity decreased during prolonged examination due to fatigue effects (n&61 19); these demonstrated a similar eccentricity-dependency, being greatest for eccentricities beyond 30o. Mean sensitivities over the range of adaptation levels employed obeyed the Weber-Fechner law (n= 10) and, as would be expected, were independent of pupil size. No relationship was found between short-term fluctuation and adaptation level. Detection of diffuse glaucomatous visual field loss was facilitated using a size III stimulus of duration 200msec at an adaptation level of 31.5asb, compared with a size III stimulus of duration 100msec at an adaptation level of 4asb (n= 20). In a pilot study (n= 10), temporal summation was found to be higher in glaucomatous patients compared with age-matched controls, although the difference was not statistically significant.

Adaptive sending rate over wireless mesh networks using SNR

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking. Instead of being another type of ad-hoc networking, WMNs diversify the capabilities of ad-hoc networks. Several protocols that work over WMNs include IEEE 802.11a/b/g, 802.15, 802.16 and LTE-Advanced. To bring about a high throughput under varying conditions, these protocols have to adapt their transmission rate. In this paper, we have proposed a scheme to improve channel conditions by performing rate adaptation along with multiple packet transmission using packet loss and physical layer condition. Dynamic monitoring, multiple packet transmission and adaptation to changes in channel quality by adjusting the packet transmission rates according to certain optimization criteria provided greater throughput. The key feature of the proposed method is the combination of the following two factors: 1) detection of intrinsic channel conditions by measuring the fluctuation of noise to signal ratio via the standard deviation, and 2) the detection of packet loss induced through congestion. We have shown that the use of such techniques in a WMN can significantly improve performance in terms of the packet sending rate. The effectiveness of the proposed method was demonstrated in a simulated wireless network testbed via packet-level simulation.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Market-awareness in service-based systems

Service-based systems are applications built by composing pre-existing services. During design time and according to the specifications, a set of services is selected. Both, service providers and consumers exist in a service market that is constantly changing. Service providers continuously change their quality of services (QoS), and service consumers can update their specifications according to what the market is offering. Therefore, during runtime, the services are periodically and manually checked to verify if they still satisfy the specifications. Unfortunately, humans are overwhelmed with the degree of changes exhibited by the service market. Consequently, verification of the compliance specification and execution of the corresponding adaptations when deviations are detected cannot be carried out in a manual fashion. In this work, we propose a framework to enable online awareness of changes in the service market in both consumers and providers by representing them as active software agents. At runtime, consumer agents concretize QoS specifications according to the available market knowledge. Services agents are collectively aware of themselves and of the consumers' requests. Moreover, they can create and maintain virtual organizations to react actively to demands that come from the market. In this paper we show preliminary results that allow us to conclude that the creation and adaptation of service-based systems can be carried out by a self-organized service market system. © 2012 IEEE.

Phenotypic effect of mutations in evolving populations of RNA molecules

Background. The secondary structure of folded RNA sequences is a good model to map phenotype onto genotype, as represented by the RNA sequence. Computational studies of the evolution of ensembles of RNA molecules towards target secondary structures yield valuable clues to the mechanisms behind adaptation of complex populations. The relationship between the space of sequences and structures, the organization of RNA ensembles at mutation-selection equilibrium, the time of adaptation as a function of the population parameters, the presence of collective effects in quasispecies, or the optimal mutation rates to promote adaptation all are issues that can be explored within this framework. Results. We investigate the effect of microscopic mutations on the phenotype of RNA molecules during their in silico evolution and adaptation. We calculate the distribution of the effects of mutations on fitness, the relative fractions of beneficial and deleterious mutations and the corresponding selection coefficients for populations evolving under different mutation rates. Three different situations are explored: the mutation-selection equilibrium (optimized population) in three different fitness landscapes, the dynamics during adaptation towards a goal structure (adapting population), and the behavior under periodic population bottlenecks (perturbed population). Conclusions. The ratio between the number of beneficial and deleterious mutations experienced by a population of RNA sequences increases with the value of the mutation rate µ at which evolution proceeds. In contrast, the selective value of mutations remains almost constant, independent of µ, indicating that adaptation occurs through an increase in the amount of beneficial mutations, with little variations in the average effect they have on fitness. Statistical analyses of the distribution of fitness effects reveal that small effects, either beneficial or deleterious, are well described by a Pareto distribution. These results are robust under changes in the fitness landscape, remarkably when, in addition to selecting a target secondary structure, specific subsequences or low-energy folds are required. A population perturbed by bottlenecks behaves similarly to an adapting population, struggling to return to the optimized state. Whether it can survive in the long run or whether it goes extinct depends critically on the length of the time interval between bottlenecks. © 2010 Stich et al; licensee BioMed Central Ltd.

Creating resilient SMEs:why one size might not fit all

This article is a first step towards addressing a gap in the field of organisational resilience research by examining how small and medium enterprises (SME) manage the threat and actuality of extreme events. Pilot research found that the managerial framing of extreme events varied by a range of organisational factors. This finding informed further examination of the contextual nature of the resilience concept. To date, large organisations have been the traditional focus of empirical work and theorising in this area; yet the heterogeneous SME sector makes up approximately 99% of UK industry and routinely operates under conditions of uncertainty. In a comparative study examining UK organisational resilience, it emerged that SME participants had both a distinctive perspective and approach to resilience when compared to participants from larger organisations. This article presents a subset of data from 11 SME decision-makers. The relationship between resilience capabilities, such as flexibility and adaptation, is interrogated in relation to organisational size. The data suggest limitations of applying a one-size-fits-all organisation solution (managerial or policy) to creating resilience. This study forms the basis for survey work examining the extent to which resilience is an organisationally contingent concept in practice.

Adaptive sending rate over wireless mesh networks using SNR

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking. Instead of being another type of ad-hoc networking, WMNs diversify the capabilities of ad-hoc networks. Several protocols that work over WMNs include IEEE 802.11a/b/g, 802.15, 802.16 and LTE-Advanced. To bring about a high throughput under varying conditions, these protocols have to adapt their transmission rate. This paper proposes a scheme to improve channel conditions by performing rate adaptation along with multiple packet transmission using packet loss and physical layer condition. Dynamic monitoring, multiple packet transmission and adaptation to changes in channel quality by adjusting the packet transmission rates according to certain optimization criteria provided greater throughput. The key feature of the proposed method is the combination of the following two factors: 1) detection of intrinsic channel conditions by measuring the fluctuation of noise to signal ratio via the standard deviation, and 2) the detection of packet loss induced through congestion. The authors show that the use of such techniques in a WMN can significantly improve performance in terms of the packet sending rate. The effectiveness of the proposed method was demonstrated in a simulated wireless network testbed via packet-level simulation.

Attributions, cognitions, and coping styles:teleworkers' reactions to work-related problems

Based on the attributional reformulation of learned helplessness theory (Abramson, Seligman, & Teasdale, 1978) and Lazarus and Launier's (1978) primary-secondary appraisal theory of stress, the present study sought to examine teleworkers' reactions to their work-related problems. The role of attributions about the sources, and cognitions about the consesquences, of these problems in promoting positive adaptation was addressed. In particular, it was predicted that teleworkers who made optimistic attributions and cognitions would be more likely to employ problem-focused coping strategies and, as a result, report more positive psychological and job-related outcomes. Based on a survey sample of 192 teleworkers, the results indicated that a tendency to engage in self-blame was related to the use of emotion-focused coping strategies. In turn, there was evidence linking emotion-focused coping strategies to negative outcomes and problem-focused coping strategies to positive outcomes. The results are discussed in relation to attributional approaches to stress which highlight the importance of cognitions about the consequences of negative events. Finally, implications for the training of teleworkers are presented.