Low Temperature Behavior of a Two-Dimensional Quantum Antiferromagnet

We analyze the two-dimensional antiferromagnet with a quantum disordered ground state and a gap to bosonic excitations with finite spin. The zero temperature (T = 0) quantum phase transition is studied, and the finite temperature effects are also considered in the low temperature approximation of the Renormalization Group Method. The violation of the universality expected for d = 2 system has been shown to be logarithmic for T = 0 and T 0. We showed that chemical potential and the ground state energy depends on the interaction parameters at T = 0 and T 0. but this dependence is logarithmic.     

Attomole peptide analysis by high-pressure matrix-assisted laser desorption/ionization Fourier transform mass spectrometry

A new high-pressure matrix-assisted laser desorption/ionization (HP-MALDI) source for FTMS has recently been described (O'Connor et al. J. Am. Soc. Mass Spectrom., in press). Improvements to the source design, including the incorporation of a new high-pressure gas channel plate, resulted in ions devoid of metastable fragmentation and also in increased sensitivity compared to the HP-MALDI prototype source design. The focus of this contribution is the evaluation of the current HP-MALDI FTMS configuration. The use of nonconductive sample surfaces, such as Parafilm and Teflon, was explored, and spectra from 30 amol of peptide applied to these surfaces were routinely obtained. In addition, the current limit of detection for this configuration is demonstrated to be 300 zmol for the phosphopeptide RRREEE(pS)EEEAA using multishot accumulation of the ions from 15 laser shots in the hexapole and 1 scan. In addition, the performance of the new HP-MALDI FTMS configuration and its potential application for high-throughput proteomics analyses are discussed.     

A comparative study of stochastic optimization methods in electric motor design

The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs and improving them according to their experience. However, the existence of reliable numerical simulators and powerful stochastic optimization techniques make it possible to automate the design procedure. We present a comparative study of six stochastic optimization algorithms in designing optimal rotor and stator geometries of a universal electric motor where the primary objective is to minimize the motor power losses. We compare three methods from the domain of evolutionary computation, generational evolutionary algorithm, steady-state evolutionary algorithm and differential evolution, two particle-based methods, particle-swarm optimization and electromagnetism-like algorithm, and a recently proposed multilevel ant stigmergy algorithm. By comparing their performance, the most efficient method for solving the problem is identified and an explanation of its success is offered.     

Virus Matryoshka: A Bacteriophage Particle—Guided Molecular Assembly Approach to a Monodisperse Model of the Immature Human Immunodeficiency Virus

Immature human immunodeficiency virus type 1 (HIV‐1) is approximately spherical, but is constructed from a hexagonal lattice of the Gag protein. As a hexagonal lattice is necessarily flat, the local symmetry cannot be maintained throughout the structure. This geometrical frustration presumably results in bending stress. In natural particles, the stress is relieved by incorporation of packing defects, but the magnitude of this stress and its significance for the particles is not known. In order to control this stress, we have now assembled the Gag protein on a quasi‐spherical template derived from bacteriophage P22. This template is monodisperse in size and electron‐transparent, enabling the use of cryo‐electron microscopy in structural studies. These templated assemblies are far less polydisperse than any previously described virus‐like particles (and, while constructed according to the same lattice as natural particles, contain almost no packing defects). This system gives us the ability to study the relationship between packing defects, curvature and elastic energy, and thermodynamic stability. As Gag is bound to the P22 template by single‐stranded DNA, treatment of the particles with DNase enabled us to determine the intrinsic radius of curvature of a Gag lattice, unconstrained by DNA or a template. We found that this intrinsic radius is far larger than that of a virion or P22‐templated particle. We conclude that Gag is under elastic strain in a particle; this has important implications for the kinetics of shell growth, the stability of the shell, and the type of defects it will assume as it grows.     

Long-term high-frequency validation of MODIS LST products in a Mediterranean mountain environment

The accuracy of Moderate-resolution Imaging Spectroradiometer (MODIS) level 3 1 km land surface temperature (LST) products was assessed through long-term validation carried out in a mountainous site in Sierra Nevada, southeast Spain. A total of 1458 day and night thermal images, acquired by Terra and Aqua satellites during 2008, were processed and compared to ground-truth data recorded at the meteorological station of Robledal de Cañar with a frequency of one measurement every 10 min. The purpose of this investigation was to understand whether MODIS LST data can be used as input for climate models to be constructed for mountainous environments. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and the overestimation of night-time values. Although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation coefficients with ground measurements, only night values maintained a relatively high accuracy of approximately 2°C of annual average error. Factors that may cause errors in the MODIS LST data, like acquisition angle, cloud, and snow cover, were analysed without conclusive results. High accuracy levels, i.e. close to 1°C, similar to other validation studies carried out over simpler and much more homogenous land-cover types such as cultivated fields, have been achieved for night images acquired during the summer months, thus making these datasets reliable for their use in climatic models over mountainous regions.     

Robustness and coordination in voltage control of large-scale power systems

In this paper it is shown how the robustness and the coordination of the voltage regulation actions for the transmission grid can be improved. Simpler approaches which ensure higher robustness and performances can be used if the control objectives are pursued at two hierarchical levels of different nature. Also, this is a way to coordinate means of control of different nature with a sufficient time and methodological separation in order to avoid negative mutual influence. At the first level, called the static level, optimal reachable set-points are computed for the second control level, called the dynamic level. The static level can be combined with the shunt reactive power compensation. The system non-linearities are taken into account at the static level while the dynamic level is a linear robust predictive control which takes into account the presence of asynchronous transmission delays. The predictive control strategy is based on the separation property; the output delays are handled using an original steady-state Kalman predictor of order equal to the length of the state of the system without delays. The robustness is improved at the dynamic level against uncertain delays, parametric uncertainties (like, e.g., moderate topological errors and load variations not taken into account in the control model) and unmodelled dynamics. The two-level organisation of the control allows, on one hand, to take into account the important evolutions of the system (like, e.g., large and known topological and load changes) and, on the other hand, a coherent hybrid reactive power control: the switched control of the grid shunt compensation for the reactive power is done at the static level while the reactive power injection provided by the generators is continuously handled at the dynamic level. This is a theoretical analysis of how concepts of automatic control and voltage regulation of power systems can be combined. To be applied as a control scheme, the results presented here should be adapted to a specific context (particularities of the power system and of the organisation of the power industry). They can be used, eventually in conjunction with other improvements, to existing horizontally-organised interconnections (in which all generators of a controlled region can be easily managed since owned by the same utility) or to face specific requirements of moving to the open access in the electric power industry like, e.g., tolerating simplified models in order to cover larger regions, taking into account the interaction between regions, recalibrating set-points, assisting human operator when necessary or facilitating implementation of mechanisms for the management of the reactive power based on price signals.     

Automatic recognition of gait-related health problems in the elderly using machine learning

This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user’s gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson’s disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.