Co-morbidity of depression and anxiety in common age-related eye diseases : a population-based study of 662 adults

This study examined the prevalence of co-morbid age-related eye disease and symptoms of depression and anxiety in late life, and the relative roles of visual function and disease in explaining symptoms of depression and anxiety. A community-based sample of 662 individuals aged over 70 years was recruited through the electoral roll. Vision was measured using a battery of tests including high and low contrast visual acuity, contrast sensitivity, motion sensitivity, stereoacuity, Useful Field of View, and visual fields. Depression and anxiety symptoms were measured using the Goldberg scales. The prevalence of self-reported eye disease [cataract, glaucoma, or age-related macular degeneration (AMD)] in the sample was 43.4%, with 7.7% reporting more than one form of ocular pathology. Of those with no eye disease, 3.7% had clinically significant depressive symptoms. This rate was 6.7% among cataract patients, 4.3% among those with glaucoma, and 10.5% for AMD. Generalized linear models adjusting for demographics, general health, treatment, and disability examined self-reported eye disease and visual function as correlates of depression and anxiety. Depressive symptoms were associated with cataract only, AMD, comorbid eye diseases and reduced low contrast visual acuity. Anxiety was significantly associated with self-reported cataract, and reduced low contrast visual acuity, motion sensitivity and contrast sensitivity. We found no evidence for elevated rates of depressive or anxiety symptoms associated with self-reported glaucoma. The results support previous findings of high rates of depression and anxiety in cataract and AMD, and in addition show that mood and anxiety are associated with objective measures of visual function independently of self-reported eye disease. The findings have implications for the assessment and treatment of mental health in the context of late-life visual impairment...

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Heat strain and hydration status of surface mine blast crew workers

Objective Dehydration and symptoms of heat illness are common among the surface mining workforce. This investigation aimed to determine whether heat strain and hydration status exceeded recommended limits. Methods Fifteen blast crew personnel operating in the tropics were monitored across a 12-hour shift. Heart rate, core body temperature, and urine-specific gravity were continuously recorded. Participants self-reported fluid consumption and completed a heat illness symptom inventory. Results Core body temperature averaged 37.46 +/- 0.13[degrees]C, with the group maximum 37.98 +/- 0.19[degrees]C. Mean urine-specific gravity was 1.024 +/- 0.007, with 78.6% of samples 1.020 or more. Seventy-three percent of workers reported at least one symptom of heat illness during the shift. Conclusions Core body temperature remained within the recommended limits; however, more than 80% of workers were dehydrated before commencing the shift, and tended to remain so for the duration.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.

Distribution harmonic state estimation based on a modified PSO considering parameters uncertainty

This paper presents a new algorithm based on a Modified Particle Swarm Optimization (MPSO) to estimate the harmonic state variables in a distribution networks. The proposed algorithm performs the estimation for both amplitude and phase of each injection harmonic currents by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as the uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WTs). The main features of the proposed MPSO algorithm are usage of a primary and secondary PSO loop and applying the mutation function. The simulation results on 34-bus IEEE radial and a 70-bus realistic radial test networks are presented. The results demonstrate that the speed and the accuracy of the proposed Distribution Harmonic State Estimation (DHSE) algorithm are very excellent compared to the algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO, and Honey Bees Mating Optimization (HBMO).

A novel hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration with regard to DGs

This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for distribution feeder reconfiguration (DFR) considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. The approach is tested on a real distribution feeder. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving DFR problem.

Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm

This paper presents an efficient algorithm for multi-objective distribution feeder reconfiguration based on Modified Honey Bee Mating Optimization (MHBMO) approach. The main objective of the Distribution feeder reconfiguration (DFR) is to minimize the real power loss, deviation of the nodes’ voltage. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. So the metahuristic algorithm has been applied to this problem. This paper describes the full algorithm to Objective functions paid, The results of simulations on a 32 bus distribution system is given and shown high accuracy and optimize the proposed algorithm in power loss minimization.

A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration

This paper deals with an efficient hybrid evolutionary optimization algorithm in accordance with combining the ant colony optimization (ACO) and the simulated annealing (SA), so called ACO-SA. The distribution feeder reconfiguration (DFR) is known as one of the most important control schemes in the distribution networks, which can be affected by distributed generations (DGs) for the multi-objective DFR. In such a case, DGs is used to minimize the real power loss, the deviation of nodes voltage and the number of switching operations. The approach is carried out on a real distribution feeder, where the simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving the DFR problem.

A new human identification protocol and Coppersmith's baby-step giant-step algorithm

We propose a new protocol providing cryptographically secure authentication to unaided humans against passive adversaries. We also propose a new generic passive attack on human identification protocols. The attack is an application of Coppersmith’s baby-step giant-step algorithm on human identification protcols. Under this attack, the achievable security of some of the best candidates for human identification protocols in the literature is further reduced. We show that our protocol preserves similar usability while achieves better security than these protocols. A comprehensive security analysis is provided which suggests parameters guaranteeing desired levels of security.

Cents and Sustainability : Securing Our Common Future by Decoupling Economic Growth from Environmental Pressures

The focus of Cents and Sustainability is to respond to the call by Dr Gro Brundtland in the seminal book Our Common Future to achieve, 'a new era of economic growth - growth that is forceful and at the same time socially and environmentally sustainable'. With the 20th anniversary of Our Common Future in 2007, it is clearly time to re-examine this important work in a modern global context. Using the framework of ‘Decoupling Economic Growth from Environmental Pressures’, Cents and Sustainability investigates a range of new evidence and research in order to develop a deeper understanding of how, and under what conditions, this 'forceful sustainable growth' is possible. With an introduction by Dr Jim MacNeill (former Secretary General to the Brundtland Commission, and former Director, OECD Environment Directorate 1978 -1984), the book will carry forewords from Dr Gro Brundtland (former Chair of the World Commission on Environment and Development), Dr Rajendra Pachauri (Chief, Intergovernmental Panel on Climate Change (IPCC), and joint recipient of the 2007 Nobel Peace Prize on behalf of the IPCC), and Dr Kenneth Ruffing (former Deputy Director and Chief Economist of the OECD Environment Directorate 2000 - 2005). Beginning with a detailed explanation of decoupling theory, along with investigation into a range of issues and barriers to its achievement, the book then focuses on informing national strategies for decoupling. Then putting this into action the book focuses on five key areas of decoupling, namely greenhouse gas emissions, biodiversity, freshwater extraction, waste production, and air pollution, and in each case showing compelling evidence for significant cost effective reductions in environmental pressures. The book concludes with a detailed case study of the groundbreaking application of public interest litigation to combat air pollution in Delhi, India.

An efficient scheme of common secure indices for conjunctive keyword-based retrieval on encrypted data

We consider the following problem: members in a dynamic group retrieve their encrypted data from an untrusted server based on keywords and without any loss of data confidentiality and member’s privacy. In this paper, we investigate common secure indices for conjunctive keyword-based retrieval over encrypted data, and construct an efficient scheme from Wang et al. dynamic accumulator, Nyberg combinatorial accumulator and Kiayias et al. public-key encryption system. The proposed scheme is trapdoorless and keyword-field free. The security is proved under the random oracle, decisional composite residuosity and extended strong RSA assumptions.

Hybrid genetic algorithm for elimination of severe stress concentration in railhead ends

This paper presents a computational method for eliminating severe stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, hybrid genetic algorithm method coupled with parametric finite element has been developed and compared with the traditional genetic algorithm (GA). The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity.

A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing

The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generalization of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics. Also, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement. The comparison results show that the computation using our mapper/reducer placement is much cheaper while still satisfying the computation deadline.

A new grouping genetic algorithm for the MapReduce placement problem in cloud computing

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NPcomplete. Thus, in this paper we propose a new grouping genetic algorithm for the mappers/reducers placement problem in cloud computing. Compared with the original one, our grouping genetic algorithm uses an innovative coding scheme and also eliminates the inversion operator which is an essential operator in the original grouping genetic algorithm. The new grouping genetic algorithm is evaluated by experiments and the experimental results show that it is much more efficient than four popular algorithms for the problem, including the original grouping genetic algorithm.