Global Best Research Articles

A Global Safety Analysis and Best Practice for Metro Railways

Fatalities due to railway accidents occur rarely and some railways identify risks from potential accident precursors. By lowering precursor frequency, the probability of more serious incidents and accidents may be reduced, following the idea of a reverse pyramid between precursors, top events, injuries and deaths. This paper analyses precursors, top events, injuries and deaths and their relationships both to each other and to incidents and accidents for a set of 17 major global metro railways. The 27 precursors analysed, for the period 2002-2008, fall into six categories: human performance, technical failures, passenger actions, fires, malicious action and management action. Statistical analysis indicates a positive correlation between injuries and top events as well as between injuries and precursors. Using these results, this paper proposes a number of measures to improve metro railway safety in each precursor category based on the experience of best practice observed among the metros.

A Global Best Path Meteorological Data Gathering Algorithm for Wireless Sensor Networks

Sink mobility has been viewed as an important technique to improve network performance for wireless sensor networks (WSNs) such as energy consumption and balancing, network lifetime, throughput, end-to-end delay etc. Also, it can largely mitigate the hot spots near sink node as sink node moves randomly or autonomously. In many applications of WSNs, sensors are deployed in areas accessed by laid roads and sinks can be assembled on mobile devices like bus or handcart. In this paper, we propose a Global Best Path (GBP) data gathering algorithm based on wireless Sensor Networks with single Mobile Sink (GBP-MSSN). It aims at determining the best position for the single mobile sink and further using global sensors information to generate the best scheme to gather data from specified node. Generating of best scheme is conducted by GBP algorithm which can balance energy consumption among whole sensor networks and further prolong the network lifetime. Simulation results show that our GBP-MSSN algorithm outperforms conventional algorithms like LEACH, GAF, etc.

ICT for Fostering Global Best Practices in the Business Environment

It is common knowledge that the use of appropriate Information and Communication Technologies (ICTs) has come to revolutionize the 21st century business environment, hence becoming a most pertinent metric of global best practice assessment in recent times. This paper traces the origins of ICTs in business practices along a developmental timeline that highlights specifically, the needs for, sources, and status of technologies that have significantly revolutionized business best practices through the years. Evaluations from surveys of industry response and perception are also reviewed as determinants of future direction for best practices in the business environment with a focus on developed and developing economies. The authors are able to further substantiate evidences of the fact that ICT could be a very pertinent benchmark for best practices adoption by various firms in the 21st Century business environment, as well as a key component for economic growth of the nations.

Uncle Tom's Cabin: Global Best Seller, Anti-slave Narrative, Imperialist Agenda

Uncle Tom's Cabin: Global Best Seller, Anti-slave Narrative, Imperialist Agenda

The Importance of Regional, System-Wide and Local Spatial Scales in Structuring Temperate Estuarine Fish Communities

An extensive literature base worldwide demonstrates how spatial differences in estuarine fish assemblages are related to those in the environment at (bio)regional, estuary-wide or local (within-estuary) scales. Few studies, however, have examined all three scales, and those including more than one have often focused at the level of individual environmental variables rather than scales as a whole. This study has identified those spatial scales of environmental differences, across regional, estuary-wide and local levels, that are most important in structuring ichthyofaunal composition throughout south-western Australian estuaries. It is the first to adopt this approach for temperate microtidal waters. To achieve this, we have employed a novel approach to the BIOENV routine in PRIMER v6 and a modified global BEST test in an alpha version of PRIMER v7. A combination of all three scales best matched the pattern of ichthyofaunal differences across the study area (ρ = 0.59; P = 0.001), with estuary-wide and regional scales accounting for about twice the variability of local scales. A shade plot analysis showed these broader-scale ichthyofaunal differences were driven by a greater diversity of marine and estuarine species in the permanently-open west coast estuaries and higher numbers of several small estuarine species in the periodically-open south coast estuaries. When interaction effects were explored, strong but contrasting influences of local environmental scales were revealed within each region and estuary type. A quantitative decision tree for predicting the fish fauna at any nearshore estuarine site in south-western Australia has also been produced. The estuarine management implications of the above findings are highlighted.

Multi-objective Particle Swarm Optimization with an Adaptive Global Best Selecting Strategy

In the presented article, a new multi-objective PSO algorithm with a novel adaptive global best selecting strategy (AD-MOPSO) has been proposed. The strategy which compromises global and local searching ability selects gBest based on particles’ sigma values and density values. A particle’s density value is evaluated using the crowding angle of each particle in polar coordinates. An adaptive tuning coefficient is designed to maintain a good trade-off between convergence and diversity performance of the algorithm. AD-MOPSO is compared with other two newly developed MOPSO algorithms on a set of well-designed benchmark functions. Results indicate that the new algorithm shows better convergence and diversity performance than other two algorithms.

Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds

This paper presents an unsupervised planar segmentation algorithm of unorganized point clouds based on multidimensional (MD) particle swarm optimization (PSO). A robust objective function of the unsupervised planar segmentation is established according to clustering distances of PSO clustering algorithm and inliers of random sample consensus (RANSAC) method. After that, MD PSO algorithm is adopted to optimize the objective function, where the optimal number and positions of the segmented planar patches are sought simultaneously. In order not to get trapped in local optima, a modification strategy of the global best (GB) position of swarm in each dimension is added to the MD PSO algorithm. Thus the unsupervised planar segmentation of point clouds is realized. Experimental results demonstrate the high planar segmentation accuracy of the proposed algorithm.

Phytoplankton productivity in interlinked mangroves, seagrass and coral reefs and its ecotones in Gulf of Mannar Biosphere Reserve South east India

The present study analysed spatio-temporal variation in phytoplankton productivity in relation to physico-chemical factors, prevailing in an interlinked system of mangroves, seagrasses and coral reefs in south India. Temperature and salinity exhibited variation between seasons but did not vary between sampling sites. Dissolved oxygen and particulate organic carbon (POC) varied between sampling sites but did not vary between seasons. pH, redox potential, reactive silicate, nitrite, total phosphate and primary productivity varied significantly between seasons as well as between sampling sites (P<0.05). In the sediment, chlorophyll a, tannin and total organic carbon (TOC) varied significantly between seasons as well as between sampling sites; total amino acids and sugars did not vary significantly between seasons or between sampling sites (P<0.05). The phytoplankton counts showed significant variation between the sampling sites as well as the seasons (P<0.05). The primary productivity was higher in the mangrove region and lower in the coral reef. The seagrass region exhibited an intermediate value. A similar trend was observed in phytoplankton counts and nutrient loads. The PRIMER analysis revealed higher diversity and species richness of phytoplankton in the mangrove region in accordance with k-dominance curve. The cluster analysis grouped mangroves and the adjacent sites in Manoli Island as a separate group. Principal component ordination, BIOENVI and Global BEST match tests revealed TOC and POC to be the most influential parameters to primary productivity. Thus the present study ascertained the importance of mangrove-derived organic matter in the primary productivity of adjacent ecosystems.

Training neural networks with harmony search algorithms for classification problems

Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy.

Self-adaptive global best harmony search algorithm for training neural networks

Abstract This paper addresses the application of Self-adaptive Global Best Harmony Search (SGHS) algorithm for the supervised training of feed-forward neural networks (NNs). A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on two classification benchmarking problems. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm is compared with other harmony search algorithms and the standard back-propagation algorithm. The experiments presented that the proposed algorithm lends itself very well to training of NNs and it is also highly competitive with the compared methods.

WNA'S WORLDWIDE OVERVIEW ON FRONT-END NUCLEAR FUEL CYCLE GROWTH AND HEALTH, SAFETY AND ENVIRONMENTAL ISSUES

This paper presents the WNA's worldwide nuclear industry overview on the anticipated growth of the front-end nuclear fuel cycle from uranium mining to conversion and enrichment, and on the related key health, safety, and environmental (HSE) issues and challenges. It also puts an emphasis on uranium mining in new producing countries with insufficiently developed regulatory regimes that pose greater HSE concerns. It introduces the new WNA policy on uranium mining: Sustaining Global Best Practices in Uranium Mining and Processing-Principles for Managing Radiation, Health and Safety and the Environment, which is an outgrowth of an International Atomic Energy Agency (IAEA) cooperation project that closely involved industry and governmental experts in uranium mining from around the world.

Video Compression by Memetic Algorithm

Memetic Algorithm by hybridization of Standard Particle Swarm Optimization and Global Local Best Particle Swarm Optimization is proposed in this paper. This technique is used to reduce number of computations of video compression by maintaining same or better quality of video. In the proposed technique, the position equation of Standard Particle Swarm Optimization is modified and used as step size equation to find best matching block in current frame. To achieve adaptive step size, time varying inertia weight is used instead of constant inertia weight for getting true motion vector dynamically. The time varying inertia weight is based up on previous motion vectors. The step size equation is used to predict best matching macro block in the reference frame with respect to macro block in the current frame for which motion vector is found. The result of proposed technique is compared with existing block matching algorithms. The performance of Memetic Algorithm is good as compared to existing algorithms in terms number of computations and accuracy.

Multi-dimensional particle swarm optimization in dynamic environments

Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle ( aGB) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.

A Technique for Combining Global Tropical Cyclone Best Track Data

Abstract Best track data generally consist of the positions and intensities during the life cycle of a tropical cyclone. Despite the widespread interest in the distribution, frequency, and intensity of tropical cyclones worldwide, no publicly available central repository of global best track data from international agencies has been in existence. While there are numerous international centers that forecast tropical cyclones and archive best track data for their defined regions, most researchers traditionally use best track data from a very small subset of centers to construct global datasets and climatologies. This practice results in tropical cyclones that are either missed and/or misrepresented. While the process of combining positions and intensities from disparate data sources can be arduous, it is worthwhile and necessary in light of their importance. The nature of historical best track data is that they are prone to issues with intensity (maximum surface wind and minimum central pressure), especially in the presatellite era. This study is not a reanalysis effort and makes no attempt to correct any longstanding debates about the accuracy of the historical data. Rather, it simply and objectively combines all of the best track data from each of the regional forecast centers that provided best tracks into one single point for distribution, and the methods used to construct the dataset are the focus of this work. Processes are therefore described herein that detail the combining of tropical cyclone best track data with the techniques used to assess the quality of the minimum central pressure and maximum sustained wind speed of each reported tropical cyclone. The result is a comprehensive global best track compilation dataset that contains information on all documented tropical cyclones: the International Best Track Archive for Climate Stewardship (IBTrACS).

Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, so-called Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis-) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG-7 dominant color descriptor show the superiority of the proposed technique.

A Novel Particle Swarm Optimization Model with Adjustment of Search Property According to Update of the Global Best Solution

This paper proposes a novel particle swarm optimization (PSO) model focused on updating the global best solution (gbest). It is fundamentally different from the standard PSO model that gbest subordinates particles and locates them in the proposed one. Particles are generally distributed around gbest so as to search locally; however, when gbest almost stops at some point, they are forcibly moved farther away from it. The above search characteristics of the proposed model is achieved by the introduction of non-uniformly distributed random numbers and the adjustment of the distance how far to spread particles from gbest according to its velocity. It is also of advantage that the functionality of the proposed PSO model is essentially independent of the choice of its parameters. Numerical results for benchmark problems verify that for most cases the proposed model outperforms the standard PSO with the aid of Linearly Decreasing Inertia Weight Approach (LDIWA). Results also show that the proposed model is likely to work better for higher dimensional problems.

Global Best Practices in Insurance Claim Cycle Time &amp; Leakage

Insurers are losing huge amounts of money to fraudulent claims. To cope up with soaring claims, insurers are adopting the best claim practices to transform their operating model. However, they are still reluctant because their investment on technology should be justified with financial prudence. The study covers the best service practices and technologies implemented by various insurers during 2003-07. It reveals that majority of claim technologies have been implemented in the P&amp;C segment. U.S. and UK accounted majority of the claim technology implemented. Insurers have been more focused towards claim administration &amp; Processing globally. Aviva emerged as a leader in the claim technology implementation and deployment of radically innovated technologies.

The Golden Triangle for MNCs:: Standardization Towards Headquarters Practices, Standardization Towards Global Best Practices and Localization

The Golden Triangle for MNCs:: Standardization Towards Headquarters Practices, Standardization Towards Global Best Practices and Localization

Testing of null hypotheses in exploratory community analyses: similarity profiles and biota-environment linkage

Tests for null hypotheses of 'absence of structure' should play an important role in any exploratory study, to guard against interpretation of sample patterns that could have been obtained by chance, and two new tests of this type are described. In the multivariate analyses that arise in community ecology and many other environmental contexts, e.g. in linking assemblage patterns to forcing environmental variables (gradient analysis), the problem of chance associations is exacerbated by the large number of combinations of abiotic variables that can usually be examined. A test which allows for this selection bias is described (the global BEST test), which applies to any dissimilarity measure, utilises only rank dissimilarities, and operates by permutation, assuming no specific distributional form or parametric expression for the biotic to abiotic links. A second permutation procedure, the similarity profile routine (SIMPROF), tests for the presence of sample groups (or more continuous sample patterns) in a priori unstructured sets of samples, for which an a priori structured test (e.g. the widely-used ANOSIM) is invalid. One context is in interpreting dendrograms from hierarchical cluster analyses: a series of SIMPROF tests provides objective stopping rules for ever-finer dissection into subgroups. Connecting these two tests is a third methodological strand, adapting De'ath's multivariate equivalent of univariate CART analysis (Classification And Regression Trees) to a non-parametric context. This produces a divisive, constrained, hierarchical cluster analysis of samples, based on their assemblage data, termed a linkage tree. The constraint is that each binary division of the tree corresponds to a threshold on one of the environmental variables and, consistently with related non-parametric routines, maximises the high-dimensional separation of the two groups, as measured by the ANOSIM R statistic. Such linkage trees therefore provide abiotic 'explanations' for each biotic subdivision of the samples but, as with unconstrained clustering, the LINKTREE routine requires objective stopping rules to avoid over-interpretation, these again being provided by a sequence of SIMPROF tests. The inter-connectedness of these three new developments is illustrated by data from the literature of marine ecology.

The Global Fund's Best Friend?

By Bernard Rivers's own description, he's loving watchdog of the Global Fund to Fight AIDS, Tuberculosis, and Malaria, a dog that spends much more of its time wagging its tail than barking, he adds.