Processing In Heterogeneous Environment Research Articles

Optimal resetting strategies for search processes in heterogeneous environments

In many physical situations, there appears the problem of reaching a single target that is spatially distributed. Here we analyse how stochastic resetting, also spatially distributed, can be used to improve the search process when the target location is quenched, i.e. it does not evolve in time. More specifically, we consider a model with minimal but sufficient ingredients that allows us to derive analytical results for the relevant physical quantities, such as the first passage time distribution. We focus on the minimisation of the mean first passage time (MFPT) and its fluctuations (standard deviation), which proves to be non-trivial. Our analysis shows that the no-disorder case is singular: for small disorder, the resetting rate distribution that minimises the MFPT leads to diverging fluctuations—which impinge on the practicality of this minimisation. Interestingly, this issue is healed by minimising the fluctuations: the associated resetting rate distribution gives first passage times that are very close to the optimal ones.

Spatial correlation reverses the compound effect of multiple stressors on rocky shore biofilm.

Understanding how multifactorial fluctuating environments affect species and communities remains one of the major challenges in ecology. The spatial configuration of the environment is known to generate complex patterns of correlation among multiple stressors. However, to what extent the spatial correlation between simultaneously fluctuating variables affects ecological assemblages in real‐world conditions remains poorly understood. Here, we use field experiments and simulations to assess the influence of spatial correlation of two relevant climate variables – warming and sediment deposition following heavy precipitation – on the biomass and photosynthetic activity of rocky intertidal biofilm. First, we used a response‐surface design experiment to establish the relation between biofilm, warming, and sediment deposition in the field. Second, we used the response surface to generate predictions of biofilm performance under different scenarios of warming and sediment correlation. Finally, we tested the predicted outcomes by manipulating the degree of correlation between the two climate variables in a second field experiment. Simulations stemming from the experimentally derived response surface showed how the degree and direction (positive or negative) of spatial correlation between warming and sediment deposition ultimately determined the nonlinear response of biofilm biomass (but not photosynthetic activity) to fluctuating levels of the two climate variables. Experimental results corroborated these predictions, probing the buffering effect of negative spatial correlation against extreme levels of warming and sediment deposition. Together, these results indicate that consideration of nonlinear response functions and local‐scale patterns of correlation between climate drivers can improve our understanding and ability to predict ecological responses to multiple processes in heterogeneous environments.

An Intelligent Sample Selection Method for Space-Time Adaptive Processing in Heterogeneous Environment

In the heterogeneous interference and target-rich environment, the clutter-plus-noise covariance matrix (CCM) estimate with a good match for the cell under test is significant for space–time adaptive processing. In this paper, an intelligent sample selection method is proposed to estimate the CCM and, thus, to suppress the clutter in the heterogeneous background. First, the proposed method applies the sample amplitude and the spatial relationship between the sample and the antenna beam center to describe the characteristics of samples, which preliminarily selects the crucial homogeneous and heterogeneous samples based on the joint histogram property. Then, the generalized inner product value and the spatial included angle are used to work as two representative features for the training samples as well as the testing samples. Based on the feature vector, the multi-dimensional feature projection based on the kernel function is carried out to achieve the offline training classifier, and the testing samples are classified with intelligence in the multi-dimensional feature space by utilizing the trained support vector machine. Due to the utilization of space–time property in representative feature extraction and intelligent strategy for the decision-making process, the proposed method acquires more reliable and efficient sample selection results compared with most of conventional sample selection methods. The experimental results based on the simulated and measured data show the effectiveness of the proposed method.

Robust Space Time Adaptive Processing in Heterogeneous Environment

Airborne phased array radar receives strong clutter when it detects ground moving target, and clutter seriously degrades the performance of target detection. Space time adaptive processing (STAP) is able to suppress clutter, while it needs training samples to estimate clutter covariance matrix. In heterogeneous interference environment, the covariance matrix cannot be well estimated and the performance of clutter suppression degrades. This paper proposes a method of covariance matrix estimation to improve the performance of STAP in heterogeneous interference environments. The method first discards training samples which may be contaminated by target signals. Then, the correlation coefficients of the cell under test and the remaining samples are estimated, and the coefficients are used as weighting coefficients to measure the contributions of the corresponding samples to the estimation of covariance matrix. The accuracy of the estimated covariance matrix can be improved significantly, and thus results in an evident performance improvement of STAP in heterogeneous environments. Experimental results demonstrate the effectiveness of the proposed method.

Neighbouring populations, opposite dynamics: influence of body size and environmental variation on the demography of stream-resident brown trout (Salmo trutta).

In organisms such as fish, where body size is considered an important state variable for the study of their population dynamics, size-specific growth and survival rates can be influenced by local variation in both biotic and abiotic factors, but few studies have evaluated the complex relationships between environmental variability and size-dependent processes. We analysed a 6-year capture-recapture dataset of brown trout (Salmo trutta) collected at 3 neighbouring but heterogeneous mountain streams in northern Spain with the aim of investigating the factors shaping the dynamics of local populations. The influence of body size and water temperature on survival and individual growth was assessed under a multi-state modelling framework, an extension of classical capture-recapture models that considers the state (i.e. body size) of the individual in each capture occasion and allows us to obtain state-specific demographic rates and link them to continuous environmental variables. Individual survival and growth patterns varied over space and time, and evidence of size-dependent survival was found in all but the smallest stream. At this stream, the probability of reaching larger sizes was lower compared to the other wider and deeper streams. Water temperature variables performed better in the modelling of the highest-altitude population, explaining over a 99 % of the variability in maturation transitions and survival of large fish. The relationships between body size, temperature and fitness components found in this study highlight the utility of multi-state approaches to investigate small-scale demographic processes in heterogeneous environments, and to provide reliable ecological knowledge for management purposes.

Grid-characteristic method using high-order interpolation on tetrahedral hierarchical meshes with a multiple time step

The purpose of the present paper is to develop a grid-characteristic method for high-performance computer systems using unstructured tetrahedral hierarchical meshes, a multiple time step and the high-order interpolation for simulating complex spatial dynamic processes in heterogeneous environments. This method has the precise formulation of contact conditions and is suitable for the physically correct solution of seismology and seismic prospecting problems in complex heterogeneous environments. The use of the hierarchical meshes allows us to take into account a large number of nonhomogeneous inclusions (cracks, cavities, etc.). The use of this grid-characteristic method makes it possible to use a multiple time step and thereby increase productivity and significantly reduce the computation time. The methods developed for high-order interpolation on unstructured tetrahedral meshes can solve the problems of seismology and seismic prospecting with approximation in space of up to the fifth degree (inclusive).

A mathematical model of the granulation process in a fluidized bed using the theory of fractal sets

We consider the approach to the description of fluidized bed in granulator with using theories of fractal sets. If telegraph equation with fractional derivative of time that describes the anomalous diffusion given the inertial effects. Anomalous diffusion equation in the light of inertial effects is a generalization for the diffusion processes in heterogeneous environments with a chaotic structure.

Surface Photochemistry: Organic Molecules within Nanocavities of Calixarenes

In order to gain more information regarding photochemical processes in heterogeneous environments (opaque or powdered samples) laser induced time resolved luminescence and diffuse reflectance transient absorption spectroscopies were used for the study of benzophenone (and other neutral organic molecules) as guests and p-tert-butylcalix[n]arenes with n = 4, 6 and 8 (H(n)CLX[n]) and partially or totally O-propylated p-tert-butylcalix[4]arenes (H(n)Pr(m)CLX[4], n = 2, 1 and 0; m = 2, 3 and 4, respectively) were used as hosts. One of the main conclusions was that the solid support can deeply affect or even control the photochemistry of an adsorbed probe. A simple new methodology for lifetime distribution analysis of the decay of the probes included into calixarenes and other nanocavities was applied with success for decay data obtained with the use of intensified charge couple devices, i.e. intensified charge couple devices, ICCDs. Diffuse reflectance laser flash photolysis and gas chromatography - mass spectrometry techniques also provided complementary information, the former about transient species and the latter regarding the final products formed after light absorption.

Enhancing the efficiency of supply chain processes through web services

Among business enterprises, keen competition is accelerating the introduction of Supply Chain Management (SCM). SCM entails the utilization of cutting-edge information technology in elaborately designing, managing and integrating supply chain processes so that participating companies' processes are interoperational at the global level. Recently, new Business Process Management (BPM) technology has attracted much attention as an indispensable tool for managing the supply chain synthetically and systematically. This next-generation technology, a great advance on existing BPM systems, can greatly enhance overall process efficiency in run-time. The critical path(s) in a process, and the slack time of each task, being the typical determiners of supply chain process efficiency, are the basis of a method, proposed in this paper, of efficiently executing global supply chain processes. The proposed method acts as a dispatching rule that can guide prioritization of the tasks in order to improve the run-time efficiency of supply chain processes. We provide several simulated results to demonstrate the effectiveness of our method, propose a web-service-based system architecture for the communication of the run-time data of tasks among processes in heterogeneous environments, and present a prototype of the system implemented.

Competition landscapes: scaling up local biotic and abiotic processes in heterogeneous environments

I present a model based on benthic populations by first defining a competition landscape where competitive interactions are confined to small areas, defined by the distribution of resource renewal from a global pool. Using truncated kernels and results from 1-dimensional percolation theory, I then derive an approximation allowing the scaling up of local interactions among individuals to patch and landscape levels. This approximation is used to explore how spatially structured habitats can affect competition intensity. The competition landscape is then compared to a simulation of mussel colonization under various flow velocity patterns, and is shown to predict the qualitative effect of the scale of patchiness in habitat quality on total competition intensity. The interaction between large scale hydrodynamic forcing and local competition is also shown to provide an alternate hypothesis explaining the observed among-year reversal of the relationship between topographic scale and mussel abundance in some intertidal communities. The model more generally highlights the nonlinear scaling up of competitive interactions in competition landscapes, and provides a framework for the study of self-organization in heterogeneous ecosystems.

Enhanced Time-Resolved Fourier Transform Infrared Spectroscopic Imaging for Reversible Dynamics

Time-resolved (TR) Fourier transform infrared (FTIR) spectroscopic imaging provides noninvasively spatial and spectral observations of molecular dynamic processes in heterogeneous environments. The technique, however, is limited by the readout time of the imaging focal plane array detector due to a tradeoff between the multichannel detection advantage and the achievable temporal resolution. We describe a generalized implementation of TR-FTIR spectroscopic imaging that, without affecting data quality, allows potential measurements of up to 2 orders of magnitude greater in time resolution than previously reported; that is, time resolutions of milliseconds to tenths of milliseconds are achievable for reversible dynamic processes. As an example, molecular rotations occurring in embedded liquid crystalline microdomains are monitored with a 2.5 ms time resolution allowing an accurate determination of events inherent in the reorientation and relaxation processes.