Quantum trajectory analysis for electrical detection of single-electron spin resonance

A Monte Carlo simulation on the basis of quantum trajectory approach is carried out for the measurement dynamics of a single-electron spin resonance. The measured electron, which is confined in either a quantum dot or a defect trap, is tunnel coupled to a side reservoir and continuously monitored by a mesoscopic detector. The simulation not only recovers the observed telegraphic signal of detector current, but also predicts unique features in the output power spectrum which are associated with electron dynamics in different regimes.

Examining Ambrosia Pollen Episodes at Poznań (Poland) Using Back-trajectory Analysis

The pollen grains of Ambrosia spp. are considered to be important aeroallergens in parts of southern and central Europe. Back-trajectories have been analysed with the aim of finding the likely sources of Ambrosia pollen grains that arrived at Poznań (Poland). Temporal variations in Ambrosia pollen at Poznań from 1995–2005 were examined in order to identify Ambrosia pollen episodes suitable for further investigation using back-trajectory analysis. The trajectories were calculated using the transport model within the Lagrangian air pollution model, ACDEP (Atmospheric Chemistry and Deposition). Analysis identified two separate populations in Ambrosia pollen episodes, those that peaked in the early morning between 4 a.m. and 8 a.m., and those that peaked in the afternoon between 2 p.m. and 6 p.m.. Six Ambrosia pollen episodes between 2001 and 2005 were examined using backtrajectory analysis. The results showed that Ambrosia pollen episodes that peaked in the early morning usually arrived at Poznań from a southerly direction after passing over southern Poland, the Czech Republic, Slovakia and Hungary, whereas air masses that brought Ambrosia pollen to Poznań during the afternoon arrived from a more easterly direction and predominantly stayed within the borders of Poland. Back-trajectory analysis has shown that there is a possibility that long-range transport brings Ambrosia pollen to Poznań from southern Poland, the Czech Republic, Slovakia and Hungary. There is also a likelihood that Ambrosia is present in Poland, as shown by the arrival of pollen during the afternoon that originated primarily from within the country.

Examining High Magnitude Grass Pollen Episodes at Worcester, United Kingdom, Using Back-Trajectory Analysis

Trajectory analysis is a valuable tool that has been used before in aerobiological studies, to investigate the movement of airborne pollen. This study has employed back-trajectories to examine the four highest grass pollen episodes at Worcester, during the 2001 grass pollen season. The results have shown that the highest grass pollen counts of the 2001 season were reached when air masses arrived from a westerly direction. Back-trajectory analysis has a limited value to forecasters because the method is retrospective and cannot be employed directly for forecasting. However, when used in conjunction with meteorological data this technique can be used to examine high magnitude events in order to identify conditions that lead to high pollen counts.

Investigating Ambrosia Pollen Episodes in Poland Using Back-Trajectory Analysis

Background: The pollen grains of Ambrosia spp. are considered to be important aeroallergens. Previous studies have shown that the long-range transport of Ambrosia pollen to Poland is intermittent and mainly related to the passage of air masses over the Carpathian and Sudetes mountains from sources to the south, e.g. the Czech Republic, Slovakia and Hungary. In this study, Ambrosia pollen counts and back-trajectories from specific episodes in 1999 and 2002 have been analysed with the aim of identifying possible new sources of Ambrosia pollen arriving at three sites in Poland. Method: The combination of Ambrosia pollen measurements (daily average and bi-hourly concentrations) and air mass trajectory calculations were used to investigate two Ambrosia pollen episodes recorded at Rzeszow, Krakow and Poznań on the 4th and 5th September 1999 and 3rd September 2002. Ambrosia pollen counts were recorded by volumetric spore traps of the Hirst design. Trajectories were calculated using the transport model within the Lagrangian air pollution model, ACDEP (Atmospheric Chemistry and Deposition). Results: The collective results of pollen measurements and back-trajectory analysis indicate plumes of Ambrosia pollen travelling up through Poland from the southeast during the investigated episodes. In 1999, the plume was first recorded at Rzeszow in Southeastern Poland during the morning of the 4th September. Its route can be followed as it passed Krakow during the afternoon of the 4th, and later on the 4th and 5th September at Poznań. Similarly, back-trajectories calculated during the morning and afternoon from Krakow and Rzeszow on the 3rd September 2002 indicates that the air masses arrived at these sites from the East or Southeast. Conclusion: This study shows the progress of Ambrosia plumes into Poland from the southeast. Ambrosia pollen release occurs mainly during the day and so a midday peak in Ambrosia pollen concentrations may indicate a local source. However, if the plume of Ambrosia pollen tracked along its northwesterly path over Poland during investigated episodes did not originate from inside Poland, then it is likely that it came from the Ukraine. This identifies a possible new source of ragweed pollen for Poland. Trajectory analysis can only show the path along which an air mass travels, not the specific source area. Further investigation could therefore include source based transport models such as 3D Eulerian atmospheric transport models.

Multicamera trajectory analysis for semantic behaviour characterisation

In this paper we propose an innovative approach for behaviour recognition, from a multicamera environment, based on translating video activity into semantics. First, we fuse tracks from individual cameras through clustering employing soft computing techniques. Then, we introduce a higher-level module able to translate fused tracks into semantic information. With our proposed approach, we address the challenge set in PETS 2014 on recognising behaviours of interest around a parked vehicle, namely the abnormal behaviour of someone walking around the vehicle.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.

Bubble rise velocity and trajectory in xanthan gum crystal suspension

An experimental set-up was used to visually observe the characteristics of bubbles as they moved up a column holding xanthan gum crystal suspensions. The bubble rise characteristics in xanthan gum solutions with crystal suspension are presented in this paper. The suspensions were made by using different concentrations of xanthan gum solutions with 0.23 mm mean diameter polystyrene crystal particles. The influence of the dimensionless quantities; namely the Reynolds number, Re, the Weber number, We, and the drag co-efficient, cd, are identified for the determination of the bubble rise velocity. The effect of these dimensionless groups together with the Eötvös number, Eo, the Froude number, Fr, and the bubble deformation parameter, D, on the bubble rise velocity and bubble trajectory are analysed. The experimental results show that the average bubble velocity increases with the increase in bubble volume for xanthan gum crystal suspensions. At high We, Eo and Re, bubbles are spherical-capped and their velocities are found to be very high. At low We and Eo, the surface tension force is significant compared to the inertia force. The viscous forces were shown to have no substantial effect on the bubble rise velocity for 45 < Re < 299. The results show that the drag co-efficient decreases with the increase in bubble velocity and Re. The trajectory analysis showed that small bubbles followed a zigzag motion while larger bubbles followed a spiral motion. The smaller bubbles experienced less horizontal motion in crystal suspended xanthan gum solutions while larger bubbles exhibited a greater degree of spiral motion than those seen in the previous studies on the bubble rise in xanthan gum solutions without crystal.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Analysis of Molecular Dynamics Simulations of Protein Folding

Microsecond long Molecular Dynamics (MD) trajectories of biomolecular processes are now possible due to advances in computer technology. Soon, trajectories long enough to probe dynamics over many milliseconds will become available. Since these timescales match the physiological timescales over which many small proteins fold, all atom MD simulations of protein folding are now becoming popular. To distill features of such large folding trajectories, we must develop methods that can both compress trajectory data to enable visualization, and that can yield themselves to further analysis, such as the finding of collective coordinates and reduction of the dynamics. Conventionally, clustering has been the most popular MD trajectory analysis technique, followed by principal component analysis (PCA). Simple clustering used in MD trajectory analysis suffers from various serious drawbacks, namely, (i) it is not data driven, (ii) it is unstable to noise and change in cutoff parameters, and (iii) since it does not take into account interrelationships amongst data points, the separation of data into clusters can often be artificial. Usually, partitions generated by clustering techniques are validated visually, but such validation is not possible for MD trajectories of protein folding, as the underlying structural transitions are not well understood. Rigorous cluster validation techniques may be adapted, but it is more crucial to reduce the dimensions in which MD trajectories reside, while still preserving their salient features. PCA has often been used for dimension reduction and while it is computationally inexpensive, being a linear method, it does not achieve good data compression. In this thesis, I propose a different method, a nonmetric multidimensional scaling (nMDS) technique, which achieves superior data compression by virtue of being nonlinear, and also provides a clear insight into the structural processes underlying MD trajectories. I illustrate the capabilities of nMDS by analyzing three complete villin headpiece folding and six norleucine mutant (NLE) folding trajectories simulated by Freddolino and Schulten [1]. Using these trajectories, I make comparisons between nMDS, PCA and clustering to demonstrate the superiority of nMDS. The three villin headpiece trajectories showed great structural heterogeneity. Apart from a few trivial features like early formation of secondary structure, no commonalities between trajectories were found. There were no units of residues or atoms found moving in concert across the trajectories. A flipping transition, corresponding to the flipping of helix 1 relative to the plane formed by helices 2 and 3 was observed towards the end of the folding process in all trajectories, when nearly all native contacts had been formed. However, the transition occurred through a different series of steps in all trajectories, indicating that it may not be a common transition in villin folding. The trajectories showed competition between local structure formation/hydrophobic collapse and global structure formation in all trajectories. Our analysis on the NLE trajectories confirms the notion that a tight hydrophobic core inhibits correct 3-D rearrangement. Only one of the six NLE trajectories folded, and it showed no flipping transition. All the other trajectories get trapped in hydrophobically collapsed states. The NLE residues were found to be buried deeply into the core, compared to the corresponding lysines in the villin headpiece, thereby making the core tighter and harder to undo for 3-D rearrangement. Our results suggest that the NLE may not be a fast folder as experiments suggest. The tightness of the hydrophobic core may be a very important factor in the folding of larger proteins. It is likely that chaperones like GroEL act to undo the tight hydrophobic core of proteins, after most secondary structure elements have been formed, so that global rearrangement is easier. I conclude by presenting facts about chaperone-protein complexes and propose further directions for the study of protein folding.

Characterisation of atmospheric deposited particles during a dust storm in urban areas of Eastern Australia

The characteristics of dust particles deposited during the 2009 dust storm in the Gold Coast and Brisbane regions of Australia are discussed in this paper. The study outcomes provide important knowledge in relation to the potential impacts of dust storm related pollution on ecosystem health in the context that the frequency of dust storms is predicted to increase due to anthropogenic desert surface modifications and climate change impacts. The investigated dust storm contributed a large fraction of fine particles to the environment with an increased amount of total suspended solids, compared to dry deposition under ambient conditions. Although the dust storm passed over forested areas, the organic carbon content in the dust was relatively low. The primary metals present in the dust storm deposition were aluminium, iron and manganese, which are common soil minerals in Australia. The dust storm deposition did not contain significant loads of nickel, cadmium, copper and lead, which are commonly present in the urban environment. Furthermore, the comparison between the ambient and dust storm chromium and zinc loads suggested that these metals were contributed to the dust storm by local anthropogenic sources. The potential ecosystem health impacts of the 2009 dust storm include, increased fine solids deposition on ground surfaces resulting in an enhanced capacity to adsorb toxic pollutants as well as increased aluminium, iron and manganese loads. In contrast, the ecosystem health impacts related to organic carbon and other metals from dust storm atmospheric deposition are not considered to be significant.

Spatial distribution of aerosol black carbon over India during pre-monsoon season

Aerosol black carbon (BC) mass concentrations ([BC]), measured continuously during a multi-platform field experiment, Integrated Campaign for Aerosols gases and Radiation Budget (ICARB, March-May 2006), from a network of eight observatories spread over geographically distinct environments of India, (which included five mainland stations, one highland station, and two island stations (one each ill Arabian Sea and Bay of Bengal)) are examined for their spatio-temporal characteristics. During the period of study, [BC] showed large variations across the country, with values ranging from 27 mu g m(3) over industrial/urban locations to as low as 0.065 mu g m(-3) over the Arabian Sea. For all mainland stations, [BC] remained high compared to highland as well as island stations. Among the island stations, Port Blair (PBR) had higher concentration of BC, compared to Minicoy (MCY), implying more absorbing nature of Bay of Bengal aerosols than Arabian Sea. The highland station Nainital (NTL), in the central Himalayas, showed low values of [BC], comparable or even lower than that of the island station PBR, indicating the prevalence of cleaner environment over there. An examination of the changes in the mean temporal features, as the season advances from winter (December-February) to pre-monsoon (March-May), revealed that: (a) Diurnal variations were pronounced over all the mainland stations, with all afternoon low and a nighttime high: (b) At the islands, the diurnal variations, though resembled those over the mainlands, were less pronounced; and (c) In contrast to this, highland station showed an opposite pattern with an afternoon high and a late night or early morning low. The diurnal variations at all stations are mainly caused by the dynamics of local Atmospheric Boundary Layer (ABL), At the entire mainland as well as island stations (except HYD and DEL), [BC] showed a decreasing trend from January to May, This is attributed to the increased convective mixing and to the resulting enhanced vertical dispersal of species in the ABL. In addition, large short-period modulations were observed at DEL and HYD, which appeared to be episodic, An examination of this in the light of the MODIS-derived fire count data over India along with the back-trajectory analysis revealed that advection of BC from extensive forest fires and biomass-burning regions upwind were largely responsible for this episodic enhancement in BC at HYD and DEL.

Dynamic coupling between the LID and NMP domain motions in the catalytic conversion of ATP and AMP to ADP by adenylate kinase

The catalytic conversion of adenosine triphosphate (ATP) and adenosine monophosphate (AMP) to adenosine diphosphate (ADP) by adenylate kinase (ADK) involves large amplitude, ligand induced domain motions, involving the opening and the closing of ATP binding domain (LID) and AMP binding domain (NMP) domains, during the repeated catalytic cycle. We discover and analyze an interesting dynamical coupling between the motion of the two domains during the opening, using large scale atomistic molecular dynamics trajectory analysis, covariance analysis, and multidimensional free energy calculations with explicit water. Initially, the LID domain must open by a certain amount before the NMP domain can begin to open. Dynamical correlation map shows interesting cross-peak between LID and NMP domain which suggests the presence of correlated motion between them. This is also reflected in our calculated two-dimensional free energy surface contour diagram which has an interesting elliptic shape, revealing a strong correlation between the opening of the LID domain and that of the NMP domain. Our free energy surface of the LID domain motion is rugged due to interaction with water and the signature of ruggedness is evident in the observed root mean square deviation variation and its fluctuation time correlation functions. We develop a correlated dynamical disorder-type theoretical model to explain the observed dynamic coupling between the motion of the two domains in ADK. Our model correctly reproduces several features of the cross-correlation observed in simulations. (C) 2011 American Institute of Physics. doi:10.1063/1.3516588]