Development and assessment of a robust optimization metric for WAMPAC modeling

A PMU based WAMS is to be placed on a weakly coupled section of distribution grid, with high levels of distributed generation. In anticipation of PMU data a Siemens PSS/E model of the electrical environment has been used to return similar data to that expected from the WAMS. This data is then used to create a metric that reflects optimization, control and protection in the region. System states are iterated through with the most desirable one returning the lowest optimization metric, this state is assessed against the one returned by PSS/E under normal circumstances. This paper investigates the circumstances that trigger SPS in the region, through varying generation between 0 and 110% and compromising the network through line loss under summer minimum and winter maximum conditions. It is found that the optimized state can generally tolerate an additional 2 MW of generation (3% of total) before encroaching the same thresholds and in one instance moves the triggering to 100% of generation output.

Cultural and Genetic Influences on the “Biological Standard of Living”

This study suggests an improvement of a popular measure of living standards, namely the biological standard of living. One influence on it is a population's consumption pattern. Since there are different dietary patterns all over the world, researchers estimate the influences of national diets on final average male height. These habits are predominantly related to income, but also to genetics, cultural history, and decisions regarding whether to trade or consume high-quality foodstuffs. Systematic differences are found when analyzing protein-consumption habits among 51 countries between the 1960s and the 1980s. The author calculates metric correction values which can facilitate international comparisons of male average height. While the proposed correction values make a little difference on average, they can be valuable in a comparison of countries with markedly different dietary patterns.

Stochastic Analysis of Saltwater Intrusion in Heterogeneous Aquifers using Local Average Subdivision

This study investigates the effects of ground heterogeneity, considering permeability as a random variable, on an intruding SW wedge using Monte Carlo simulations. Random permeability fields were generated, using the method of Local Average Subdivision (LAS), based on a lognormal probability density function. The LAS method allows the creation of spatially correlated random fields, generated using coefficients of variation (COV) and horizontal and vertical scales of fluctuation (SOF). The numerical modelling code SUTRA was employed to solve the coupled flow and transport problem. The well-defined 2D dispersive Henry problem was used as the test case for the method. The intruding SW wedge is defined by two key parameters, the toe penetration length (TL) and the width of mixing zone (WMZ). These parameters were compared to the results of a homogeneous case simulated using effective permeability values. The simulation results revealed: (1) an increase in COV resulted in a seaward movement of TL; (2) the WMZ extended with increasing COV; (3) a general increase in horizontal and vertical SOF produced a seaward movement of TL, with the WMZ increasing slightly; (4) as the anisotropic ratio increased the TL intruded further inland and the WMZ reduced in size. The results show that for large values of COV, effective permeability parameters are inadequate at reproducing the effects of heterogeneity on SW intrusion.

Dense spectrum of resonances and spin-1/2 particle capture in a near-black-hole metric

We show that a spin-1/2 particle in the gravitational field of a massive body of radius R which slightly exceeds the Schwarzschild radius rs, possesses a dense spectrum of narrow resonances. Their lifetimes and density tend to infinity in the limit R → rs. We determine the cross section of the particle capture into these resonances and show that it is equal to the spin-1/2 absorption cross section for a Schwarzschild black hole. Thus black-hole properties may emerge in a non-singular static metric prior to the formation of a black hole.

Python PSS/E Simulation to Test Efficacy of Proposed PMU Based WAMS and Potential WAMPAC Applications

A PSS/E 32 model of a real section of the Northern Ireland electrical grid was dynamically controlled with Python 2.5. In this manner data from a proposed wide area monitoring system was simulated. The area is of interest as it is a weakly coupled distribution grid with significant distributed generation. The data was used to create an optimization and protection metric that reflected reactive power flow, voltage profile, thermal overload and voltage excursions. Step changes in the metric were introduced upon the operation of special protection systems and voltage excursions. A wide variety of grid conditions were simulated while tap changer positions and switched capacitor banks were iterated through; with the most desirable state returning the lowest optimization and protection metric. The optimized metric was compared against the metric generated from the standard system state returned by PSS/E. Various grid scenarios were explored involving an intact network and compromised networks (line loss) under summer maximum, summer minimum and winter maximum conditions. In each instance the output from the installed distributed generation is varied between 0 MW and 80 MW (120% of installed capacity). It is shown that in grid models the triggering of special protection systems is delayed by between 1 MW and 6 MW (1.5% to 9% of capacity), with 3.5 MW being the average. The optimization and protection metric gives a quantitative value for system health and demonstrates the potential efficacy of wide area monitoring for protection and control.

Complete control of high-harmonic generation for high average power applications

We present a new dual-gas multi-jet HHG source which can be perfectly controlled via phasematching of the long and short trajectory contributions and is applicable for high average power driver laser systems. © 2011 Optical Society of America.

Seasonal variation in month of diagnosis in children with type 1 diabetes registered in 23 European centers during 1989-2008: little short-term influence of sunshine hours or average temperature

BACKGROUND: The month of diagnosis in childhood type 1 diabetes shows seasonal variation.

OBJECTIVE: We describe the pattern and investigate if year-to-year irregularities are associated with meteorological factors using data from 50 000 children diagnosed under the age of 15 yr in 23 population-based European registries during 1989-2008.

METHODS: Tests for seasonal variation in monthly counts aggregated over the 20 yr period were performed. Time series regression was used to investigate if sunshine hour and average temperature data were predictive of the 240 monthly diagnosis counts after taking account of seasonality and long term trends.

RESULTS: Significant sinusoidal pattern was evident in all but two small centers with peaks in November to February and relative amplitudes ranging from ±11 to ±38% (median ±17%). However, most centers showed significant departures from a sinusoidal pattern. Pooling results over centers, there was significant seasonal variation in each age-group at diagnosis, with least seasonal variation in those under 5 yr. Boys showed greater seasonal variation than girls, particularly those aged 10-14 yr. There were no differences in seasonal pattern between four 5-yr sub-periods. Departures from the sinusoidal trend in monthly diagnoses in the period were significantly associated with deviations from the norm in average temperature (0.8% reduction in diagnoses per 1 °C excess) but not with sunshine hours.

CONCLUSIONS: Seasonality was consistently apparent throughout the period in all age-groups and both sexes, but girls and the under 5 s showed less marked variation. Neither sunshine hour nor average temperature data contributed in any substantial way to explaining departures from the sinusoidal pattern.

Impact of average household income and damage exposure on post-earthquake distress and functioning: A community study following the February 2011 Christchurch earthquake.

Post-traumatic stress, depression and anxiety symptoms are common outcomes following earthquakes, and may persist for months and years. This study systematically examined the impact of neighbourhood damage exposure and average household income on psychological distress and functioning in 600 residents of Christchurch, New Zealand, 4–6 months after the fatal February, 2011 earthquake. Participants were from highly affected and relatively unaffected suburbs in low, medium and high average household income areas. The assessment battery included the Acute Stress Disorder Scale, the depression module of the Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder Scale (GAD-7), along with single item measures of substance use, earthquake damage and impact, and disruptions in daily life and relationship functioning. Controlling for age, gender and social isolation, participants from low income areas were more likely to meet diagnostic cut-offs for depression and anxiety, and have more severe anxiety symptoms. Higher probabilities of acute stress, depression and anxiety diagnoses were evident in affected versus unaffected areas, and those in affected areas had more severe acute stress, depression and anxiety symptoms. An interaction between income and earthquake effect was found for depression, with those from the low and medium income affected suburbs more depressed. Those from low income areas were more likely, post-earthquake, to start psychiatric medication and increase smoking. There was a uniform increase in alcohol use across participants. Those from the low income affected suburb had greater general and relationship disruption post-quake. Average household income and damage exposure made unique contributions to earthquake-related distress and dysfunction.

Efficient RkNN Retrieval with Arbitrary Non-Metric Similarity Measures

A RkNN query returns all objects whose nearest k neighbors
contain the query object. In this paper, we consider RkNN
query processing in the case where the distances between
attribute values are not necessarily metric. Dissimilarities
between objects could then be a monotonic aggregate of dissimilarities
between their values, such aggregation functions
being specified at query time. We outline real world cases
that motivate RkNN processing in such scenarios. We consider
the AL-Tree index and its applicability in RkNN query
processing. We develop an approach that exploits the group
level reasoning enabled by the AL-Tree in RkNN processing.
We evaluate our approach against a Naive approach
that performs sequential scans on contiguous data and an
improved block-based approach that we provide. We use
real-world datasets and synthetic data with varying characteristics
for our experiments. This extensive empirical
evaluation shows that our approach is better than existing
methods in terms of computational and disk access costs,
leading to significantly better response times.