An improved genetic algorithm and graph theory based method for optimal sectionalizer switch placement in distribution networks with DG

In this paper a new graph-theory and improved genetic algorithm based practical method is employed to solve the optimal sectionalizer switch placement problem. The proposed method determines the best locations of sectionalizer switching devices in distribution networks considering the effects of presence of distributed generation (DG) in fitness functions and other optimization constraints, providing the maximum number of costumers to be supplied by distributed generation sources in islanded distribution systems after possible faults. The proposed method is simulated and tested on several distribution test systems in both cases of with DG and non DG situations. The results of the simulations validate the proposed method for switch placement of the distribution network in the presence of distributed generation.

The effect of various pH, ionic strength and temperature on papain hydrolysis of salivary film

Stimulated human whole saliva (WS) was used to study the dynamics of papain hydrolysis at defined pH, ionic strength and temperature with the view of reducing an acquired pellicle. A quartz crystal microbalance with dissipation (QCM-D) was used to monitor the changes in frequency due to enzyme hydrolysis of WS films and the hydrolytic parameters were calculated using an empirical model. The morphological and conformational changes of the salivary films before and after enzymatic hydrolysis were characterized by atomic force microscopy (AFM) imaging and grazing angle infrared spectroscopy (GA-FTIR) spectra, respectively. The characteristics of papain hydrolysis of WS films were pH-, ionic strength- and temperature-dependent. The WS films were partially removed by the action of enzyme, resulting thinner and smoother surfaces. The IR data suggested that hydrolysis-induced deformation did not occur onto the remnants salivary films. The processes of papain hydrolysis of WS films can be controlled by properly regulating pH, ionic strength and temperature.

Multi-objective design optimization of morphing UAV aerofoil/wing using hybridised MOGA

The paper investigates two advanced Computational Intelligence Systems (CIS) for a morphing Unmanned Aerial Vehicle (UAV) aerofoil/wing shape design optimisation. The first CIS uses Genetic Algorithm (GA) and the second CIS uses Hybridized GA (HGA) with the concept of Nash-Equilibrium to speed up the optimisation process. During the optimisation, Nash-Game will act as a pre-conditioner. Both CISs; GA and HGA, are based on Pareto optimality and they are coupled to Euler based Computational Fluid Dynamic (CFD) analyser and one type of Computer Aided Design (CAD) system during the optimisation.

Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Genetic algorithms to schedule container transfers at multimodal terminals

Optimising the container transfer schedule at the multimodal terminals is known to be NP-hard, which implies that the best solution becomes computationally infeasible as problem sizes increase. Genetic Algorithm (GA) techniques are used to reduce container handling/transfer times and ships' time at the port by speeding up handling operations. The GA is chosen due to the relatively good results that have been reported even with the simplest GA implementations to obtain near-optimal solutions in reasonable time. Also discussed, is the application of the model to assess the consequences of increased scheduled throughput time as well as different strategies such as the alternative plant layouts, storage policies and number of yard machines. A real data set used for the solution and subsequent sensitivity analysis is applied to the alternative plant layouts, storage policies and number of yard machines.

An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

Zircon chronochemistry of high heat-producing granites in Queensland and Europe

High heat-producing granites (HHPGs) are reservoir rocks for enhanced geothermal systems (EGS), yet the origins of their anomalous chemistry remain poorly understood. To gain a better understanding of the characteristic distribution of elemental depletions and enrichments (focussing on U, Th & K) within granite suites of different heritage and tectonic setting, and the processes that lead to these enrichments, we are undertaking a systematic accessory-mineral chronochemical study of two suites of S- and I-type granites in northern Queensland, as well as two archetypal HHPGs in Cornwall, England (S-type) and Soultz-sous- Forêts, France (I-type). Novel zircon LA-ICP-MS chronochemical methods will later be underpinned by a systematic petrographic, scanning electron microscope (SEM), and electron microprobe (EPMA) study of all the REE-Y-Th-U-rich accessory minerals to fully characterise how the composition, textural distributions and associations change with rock chemistry between and among the suites. Preliminary results indicate that zircons with inherited ages do not have anomalously high U (>1000 ppm) & Th (>400 ppm) values (Ahrens, 1965). Instead, enrichment in these HPE is seen in zircons dated to around the time of magmatic emplacement. These results indicate that enrichment arose primarily through fractional crystallisation of the granitic magmas. Our results support the suggestion that a source pre-enriched in the HPEs does not appear to be fundamental for the formation of all HHPGs. Instead fractional crystallisation processes, and the accessory minerals formed in magmas of differing initial compositions, are the key controls on the levels of enrichment observed (e.g. Champion & Chappell, 1992; Chappell & Hine, 2006). One implication is that the most fractionated granites may not be the most enriched in the HPEs and therefore prospective to future EGS development.

QGECE research on delineation of Australian geothermal resources

The geology/reservoir program of the Queensland Geothermal Energy Centre of Excellence (QGECE) has the mission to improve the existing knowledge and develop new innovative scientific approaches for the identification of geothermal resources in Australia, with a particular focus on Queensland. Specifically, the QGECE geology/reservoir program is currently (1) producing a comprehensive geochemical dataset for high heat producing rocks, (2) conducting detailed mineralogical and geochronological studies of granites and hydrothermal alteration minerals, and ; (3) investigating the Cooper Basin representing a superb natural laboratory for understanding of radiogenic heat enrichment process and possible involvement of mantle heat flow. Seven research projects have been established, which are being conducted largely as PhD studies. In the preliminary studies, high quality and valuable results were obtained to address the research topics of understanding the causes and timing of heat producing element enrichment.

Optimized strategy for integrated traffic and transit signal control

This paper proposes a unique and innovative approach to integrate transit signal priority control into a traffic adaptive signal control strategy. The proposed strategy was named OSTRAC (Optimized Strategy for integrated TRAffic and TRAnsit signal Control). The cornerstones of OSTRAC include an online microscopic traffic f low prediction model and a Genetic Algorithm (GA) based traffic signal timing module. A sensitivity analysis was conducted to determine the critical GA parameters. The developed traffic f low model demonstrated reliable prediction results through a test. OSTRAC was evaluated by comparing its performance to three other signal control strategies. The evaluation results revealed that OSTRAC efficiently and effectively reduced delay time of general traffic and also transit vehicles.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Repeated intrauterine exposures to inflammatory stimuli attenuated transforming growth factor-β signaling in the ovine fetal lung

Background: Bronchopulmonary dysplasia (BPD) is one of the most common complications after preterm birth and is associated with intrauterine exposure to bacteria. Transforming growth factor-β (TGFβ) is implicated in the development of BPD. Objectives: We hypothesized that different and/or multiple bacterial signals could elicit divergent TGFβ signaling responses in the developing lung. Methods: Time-mated pregnant Merino ewes received an intra-amniotic injection of lipopolysaccharide (LPS) and/or Ureaplasma parvum serovar 3 (UP) at 117 days' and/or 121/122 days' gestational age (GA). Controls received an equivalent injection of saline and or media. Lambs were euthanized at 124 days' GA (term = 150 days' GA). TGFβ1, TGFβ2, TGFβ3, TGFβ receptor (R)1 and TGFβR2 protein levels, Smad2 phosphorylation and elastin deposition were evaluated in lung tissue. Results: Total TGFβ1 and TGFβ2 decreased by 24 and 51% after combined UP+LPS exposure, whereas total TGFβ1 increased by 31% after 7 days' LPS exposure but not after double exposures. Alveolar expression of TGFβR2 decreased 75% after UP, but remained unaltered after double exposures. Decreased focal elastin deposition after single LPS exposure was prevented by double exposures. Conclusions: TGFβ signaling components and elastin responded differently to intrauterine LPS and UP exposure. Multiple bacterial exposures attenuated TGFβ signaling and normalized elastin deposition.

A hybrid genetic algorithm for the minimum interconnection cut problem

In the real world there are many problems in network of networks (NoNs) that can be abstracted to a so-called minimum interconnection cut problem, which is fundamentally different from those classical minimum cut problems in graph theory. Thus, it is desirable to propose an efficient and effective algorithm for the minimum interconnection cut problem. In this paper we formulate the problem in graph theory, transform it into a multi-objective and multi-constraint combinatorial optimization problem, and propose a hybrid genetic algorithm (HGA) for the problem. The HGA is a penalty-based genetic algorithm (GA) that incorporates an effective heuristic procedure to locally optimize the individuals in the population of the GA. The HGA has been implemented and evaluated by experiments. Experimental results have shown that the HGA is effective and efficient.

Dynamic robot path planning using an enhanced simulated annealing approach

Evolutionary computation is an effective tool for solving optimization problems. However, its significant computational demand has limited its real-time and on-line applications, especially in embedded systems with limited computing resources, e.g., mobile robots. Heuristic methods such as the genetic algorithm (GA) based approaches have been investigated for robot path planning in dynamic environments. However, research on the simulated annealing (SA) algorithm, another popular evolutionary computation algorithm, for dynamic path planning is still limited mainly due to its high computational demand. An enhanced SA approach, which integrates two additional mathematical operators and initial path selection heuristics into the standard SA, is developed in this work for robot path planning in dynamic environments with both static and dynamic obstacles. It improves the computing performance of the standard SA significantly while giving an optimal or near-optimal robot path solution, making its real-time and on-line applications possible. Using the classic and deterministic Dijkstra algorithm as a benchmark, comprehensive case studies are carried out to demonstrate the performance of the enhanced SA and other SA algorithms in various dynamic path planning scenarios.

Through-focus performance with multifocal contact lenses : effect of binocularity, pupil diameter and inherent ocular aberrations

Purpose to evaluate the effects of the wearer’s pupil size and spherical aberration on visual performance with centre-near, aspheric multifocal contact lenses (MFCLs). The advantage of binocular over monocular vision was also investigated. Methods Twelve young volunteers, with an average age of 27±5 years, participated in the study. LogMAR Visual Acuity (VA) was measured under cycloplegia for a range of defocus levels (from +3.0 to -3.0D, in 0.5D steps) with no correction and with three aspheric MFCLs (Air Optix Aqua Multifocal, Ciba Vision, Duluth, GA, US) with a centre-near design, providing correction for “Low”, “Med” and “High” near demands. Measurements were performed for all combinations of the following conditions: i) artificial pupils of 6mm and 3mm diameter, ii) binocular and monocular (dominant eye) vision. Depth-of-focus (DOF) was calculated from the VA vs. defocus curves. Ocular aberrations under cycloplegia were measured using iTrace. Results VA at -3.0D defocus (simulating near performance) was statistically higher for the 3mm than for the 6mm pupil (p=0.006), and for binocular rather than for monocular vision (p<0.001). Similarly, DOF was better for the 3mm pupil (p=0.002) and for binocular viewing conditions (p<0.001, ANOVA). Both VA at –3.0D defocus and DOF increased as the “addition” of the MFCL correction increased. Finally, with the centre-near MFCLs a linear correlation was found between VA at –3.0D defocus and the wearer’s ocular spherical aberration (R2=0.20 p<0.001 for 6mm data), with the eyes exhibiting the higher positive spherical aberration experiencing lower VAs. By contrast, no correlation was found between VA and spherical aberration at 0.00D defocus (distance vision). Conclusions Both near VA and depth-of-focus improve with these MFCLs, with the effects being more pronounced for small pupils and binocular than for monocular vision. Coupling of the wearer’s ocular spherical aberration with the aberration profiles provided by MFCLs affects their functionality.

Comparing outcomes of a school based injury prevention intervention for low-medium risk adolescents with high-risk adoelscents

High-risk adolescents are shown to jeopardise their future social and health functioning as well as placing themselves and others at immediate risk of harm. The challenge of “reaching” high-risk adolescents, who are often marginalised, is considerable. There is a positive relationship between age and risk taking behaviors during adolescence. This study examines outcomes (alcohol use, transport risk behaviors, violence) of a school based intervention (SPIY) by comparing low-medium risk adolescents with high-risk adolescents over a six month period.