Sedimentation and polar order of active bottom-heavy particles

Self-propelled particles in an external gravitational field have been shown to display both an increased sedimentation length and polar order even without particle interactions. Here, we investigate self-propelled particles which additionally are bottom-heavy, that is they feel a torque aligning them to swim against the gravitational field. For bottom-heavy particles the gravitational field has the two opposite effects of i) sedimentation and ii) upward alignment of the particles' swimming direction. We perform a multipole expansion of the one-particle distribution with respect to orientation and derive expressions for sedimentation length and mean particle orientation which we check against Brownian Dynamics simulations. For large strength of gravity or small particle speeds and aligning torque, we observe sedimentation with increased sedimentation length compared with passive colloids but also active colloids without bottom-heaviness. Increasing, for example, swimming speed the sedimentation profile is inverted and the particles swim towards the top wall of the enclosing box. We find maximal orientational order at intermediate swimming speeds for both cases of particles with bottom-heaviness and those without. Ordering unsurprisingly is increased for the bottom-heavy particles, but this difference disappears at higher levels of activity and for very high activities ordering goes to zero in both cases.Comment: 6 pages, 3 figure

Active Brownian particles moving in a random Lorentz gas

Biological microswimmers often inhabit a porous or crowded environment such as soil. In order to understand how such a complex environment influences their spreading, we numerically study non-interacting active Brownian particles (ABPs) in a two-dimensional random Lorentz gas. Close to the percolation transition in the Lorentz gas, they perform the same subdiffusive motion as ballistic and diffusive particles. However, due to their persistent motion they reach their long-time dynamics faster than passive particles and also show superdiffusive motion at intermediate times. While above the critical obstacle density ηcthe ABPs are trapped, their long-time diffusion belowηcis strongly influenced by the propulsion speed v0. With increasing v0, ABPs are stuck at the obstacles for longer times. Thus, for large propulsion speed, the long-time diffusion constant decreases more strongly in a denser obstacle environment than for passive particles. This agrees with the behavior of an effective swimming velocity and persistence time, which we extract from the velocity autocorrelation function

Spin-lattice coupling effects in the Holstein double-exchange model

Based on the Holstein double-exchange model and a highly efficient single cluster Monte Carlo approach we study the interplay of double-exchange and polaron effects in doped colossal magneto-resistance (CMR) manganites. The CMR transition is shown to be appreciably influenced by lattice polaron formation.Comment: 2 pages, 2 figures, submitted to SCES'0

An Enterprise Architecture Framework to organize Model Repositories

Abstract. Models are a valuable knowledge asset for an enterprise. An enterprise model repository can improve sharing of enterprise knowledge and thus can exploit the use of the knowledge for various applications. In this work we present a framework for the organisation of enterprise models. The framework was derived from enterprise architecture frameworks. It distinguishes three dimensions: aspect, perspective, and modelling language family. For each of these dimensions we derive possible values. The framework can be used for enterprise repositories but also for knowledge exchange in a community as proposed by the Open Model Initiative

Model-Based Therapeutics for Type 1 Diabetes Mellitus

The incidence of Type 1 diabetes is growing yearly. Worryingly, the aetiology of the disease is inconclusive. What is known is that the total number of affected individuals, as well as the severity and number of associated complications are growing for this chronic disease. With increasing complications due to severity, length of exposure, and poor control, the disease is beginning to consume an increasingly major portion of healthcare costs to the extent that it poses major economic risks in several nations. Research has shown that intensive insulin therapy aimed at certain minimum glycosylated haemoglobin threshold levels reduces the incidence of complications by up to 76% compared to conventional insulin therapy. Moreover, the effects of such intensive therapy regimes over a 6.5y duration persists for at least 10y after, a so called metabolic memory. Thus, early intervention can slow the momentum of complications far more easily than later intervention. Early, safe, intensive therapy protocols offer potential solutions to the growing social and economic effects of diabetes. Since the 1970s, the artificial endocrine pancreas has been heralded as just this type of solution. However, no commercial product currently exists, and ongoing limitations in sensors and pumps have resulted in, at best, modest clinical advantages over conventional methods of insulin administration or multiple daily injection. With high upfront costs, high costs of consumables, significant complexity, and the extensive infrastructure and support required, these systems and devices are only used by 2-15% of individuals with Type 1 diabetes. Clearly, there is an urgent need to address the large majority of the Type 1 diabetes population using conventional glucose measurement and insulin administration. For these individuals, current conventional or intensive therapies are failing to deliver recommended levels of glycaemic control. This research develops an understanding of clinical glycaemic control using conventional insulin administration and glucose measurement techniques in Type 1 diabetes based on a clinically validated in silico virtual patient simulation. Based on this understanding, a control protocol for Type 1 diabetes that is relatively simple and clinically practical is developed. The protocol design incorporates physiological modelling and engineering techniques to adapt to individual patient clinical requirements. By doing so, it produces accurate, patient-specific recommendations for insulin interventions. Initially, a simple, physiological compartmental model for the pharmacokinetics of subcutaneously injected insulin is developed. While the absorption process itself is subject to significant potential variability, such models enable a real-time estimation of plasma insulin concentration. This information would otherwise be lacking in the clinical environment of outpatient Type 1 diabetes treatment due to the inconvenience, cost, and laboratory turnaround for plasma insulin measurements. Hence, this validated model offers significant opportunity to optimise therapy selection. An in silico virtual patient simulation tool is also developed. A virtual patient cohort is developed on patient data from a representative cohort of the broad diabetes population. The simulation tool is used to develop a robust, adaptive protocol for prandial insulin dosing against a conventional intensive insulin therapy, as well as a controls group representative of the general diabetes population. The effect on glycaemic control of suboptimal and optimal, prandial and basal insulin therapies is also investigated, with results matching clinical expectations. To gauge the robustness of the developed adaptive protocol, a Monte Carlo analysis is performed, incorporating realistic and physiological errors and variability. Due to the relatively infrequent glucose measurement in outpatient Type 1 diabetes, a method for identifying the diurnal cycle in effective insulin sensitivity and modelling it in retrospective patient data is also presented. The method consists of identifying deterministic and stochastic components in the patient effective insulin sensitivity profile. Circadian rhythmicity and sleep-wake phases have profound effects on effective insulin sensitivity. Identification and prediction of this rhythm is of utmost clinical relevance, with the potential for safer and more effective glycaemic control, with less frequent measurement. It is thus a means of further enhancing any robust protocol and making it more clinically practical to implement. Finally, this research presents an entire framework for the realistic, and rapid development and testing of clinical glycaemic control protocols for outpatient Type 1 diabetes. The models and methods developed within this framework allow rapid and physiological identification of time-variant, patient-specific, effective insulin sensitivity profiles. These profiles form the responses of the virtual patient and can be used to develop and robustly test clinical glycaemic control protocols in a broad range of patients. These effective insulin sensitivity profiles are also rich in dynamics, specifically those circadian in nature which can be identified, and used to provide more accurate glycaemic prediction with the potential for safer and more effective control

Plasma Spraying of Kaolinite for Preparing Reactive Alumino-Silicate Glass Coatings

Thermally treated kaolinite is used to develop a range of alumino-silicate-based precursor materials but its behavior during plasma spraying has not been well-researched. In this study, two types of kaolinite samples were investigated in the form of low defect (KGa-1b) and high defect (KGa-2) varieties. The extreme temperatures of the plasma stream (up to 20 000 K) induced flash melting to produce a highly porous alumino-silicate glass without any crystallization of new Al−Si oxide minerals. The glass is comprised largely of intact or deformed spheres (average diameters 1.14–1.44 μm), which indicates rapid quenching and solidification before impact. The subspherical structures contain up to 40 % closed pore space caused by the rapid escape of water during melting. The low-density, porous alumino-silicate glass coatings with predicted specific surface areas (>0.95 m2/g) and hardnesses >1.8 GPa represent a potentially reactive but physically stable substrate ideal for further chemical functionalization

The role of local atmospheric forcing on the modulation of the ocean mixed layer depth in reanalysis and a coupled single column ocean model

The role of the local atmospheric forcing on the ocean mixed layer depth (MLD) over the global oceans is studied using ocean reanalysis data products and a single-column ocean model coupled to an atmospheric general circulation model. The focus of this study is on how the annual mean and the seasonal cycle of the MLD relate to various forcing characteristics in different parts of the world's ocean, and how anomalous variations in the monthly mean MLD relate to anomalous atmospheric forcings. By analysing both ocean reanalysis data and the single-column ocean model, regions with different dominant forcings and different mean and variability characteristics of the MLD can be identified. Many of the global oceans' MLD characteristics appear to be directly linked to different atmospheric forcing characteristics at different locations. Here, heating and wind-stress are identified as the main drivers; in some, mostly coastal, regions the atmospheric salinity forcing also contributes. The annual mean MLD is more closely related to the annual mean wind-stress and the MLD seasonality is more closely to the seasonality in heating. The single-column ocean model, however, also points out that the MLD characteristics over most global ocean regions, and in particular the tropics and subtropics, cannot be maintained by local atmospheric forcings only, but are also a result of ocean dynamics that are not simulated in a single-column ocean model. Thus, lateral ocean dynamics are essentially in correctly simulating observed MLD

In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200