Diffusion Search Research Articles

Efficient data integrity and data replication in cloud using stochastic diffusion method

Cloud computing will provide scalable computing as well as storage resources where more data intensive applications will be developed in a computing environment. Owing to the existence of such security threats in the cloud, several mechanisms are being proposed for allowing the users to audit the integrity of data along with the public key of the owner of the data even before making use of the cloud data. Replicating of data in cloud servers through multiple data centers offers better availability, scalability, and durability. The correctness of choice of the right type of public key of the previous mechanisms is based on the security of the public key infrastructure (PKI). Although traditional PKI has been widely used in the construction of public key cryptography, it still faces many security risks, especially in the aspect of managing certificates. There are different applications having different types of quality of service (QoS) needs. In order to support the QoS requirement continuously, the application of such data corruption for this work will be an efficient integrity of data replication that makes use of a stochastic diffusion search (SDS) algorithm that has been proposed. This SDS is that technique of a multi-agent global optimisation which has been based on the behaviour of ants that has been rooted in the partial evaluation of that of an objective function along with direct communication among agents. The proposed SDS algorithm will minimize the replication cost of data. The results of these experiments have shown that the mechanism will be able to demonstrate the effectiveness of this proposed algorithm which is in the replication of data as well as its recovery. The proposed method when appropriately compared with the cost effective replication of dynamic data given by Li et al. proves that the average recovery time is less by 18.18% for the 250 number of requested nodes, by 14.28% for the 500 number of requested nodes, by 11.11% for the 750 number of requested nodes and by 8.69% for the 1000 number of requested nodes.

RETRACTED ARTICLE: A wrapper based feature selection in bone marrow plasma cell gene expression data

The Microarray technology permits simultaneous monitoring of several gene expressions for every sample. But unfortunately, this classification of such samples into the distinct classes has often been found to difficult as the actual number of genes (the features) will to a great extent exceed the actual number of the samples. There is a high dimensionality in gene and its expression data which is a huge challenge in many of the problems of classification. Cloud computing is that popular concept of computing which performs a processing of a data of huge volumes by making use of highly accessible resources that are geographically distributed and accessed by the users based on the policy of pay as per use. In spite of the several steps in that of the B Cell that are now elucidate the last few stages of that of the plasma cell (PC) based differentiation that have not been understood yet. The PCs had generated at the time of primary and humoral immune responses that have started their differentiation within their light zones for the germinal centers of such light zones of that of the lymph nodes or the ones in the red pulp of the spleen having a life span of about few days. The selection of the features will aim at the maintenance of their original features of such data and at this time it will seek at identifying their main features and will weed out all those that are irrelevant for building of a learning model that is impactful. Identifying one global maximum will be Non-deterministic Polynomial (NP)-hard and if the criterion is decomposable or possesses the properties that can make some approximate type of optimization easy. The artificial bee colony (ABC) is that one that is used widely and applied for solving all real world problems. The stochastic diffusion search (SDS) will be an efficient multi-agent global search based technique of optimization applied to that of several problems and here the hybrid ABC with that of the SDS feature selection will be proposed and these images will be grouped as either normal or abnormal PC of the bone marrow that is based on this gene expression data. The support vector machine is that algorithms of supervised learning that is capable of being able to solve the complex problems in classification. This proposed method of gene selection will yield a comparable performance for the classification on being compared to that of the currently existing classifiers providing another new insight in case of feature selection.

An improved design for grid based PV control systems using modified fuzzy logic with an improved switched capacitor DC-DC converter

In Grid computing, the interconnecting photo-voltaic (PV) control system needs an efficient converter to convert the low direct current (DC) voltage into DC. Design has been improved for the PV, by using a modified fuzzy logic along with an improved switched capacitor (SC) and a DC Converter. An optimized SC DC–DC converter’s performance in steady state for improving the efficiency and the regulation of output for a class belonging to the SC converters that is applied to the PV systems control is proposed. The model has many power converters for efficiently managing the transfer of power of a long cable that has higher electro-magnetic interference. On the basis of the model proposed, this converter provides a suitable DC voltage conversion ratio. The capacitor’s resistive output on the impedance accounts for the purpose of charging and discharging losses and the resistive conduction losses has been considered. This work will further extend this DC–DC converter into method of control for a maximum power point tracking which belongs to a PV system that makes use of the variable temperature as well as the irradiance conditions and the fuzzy logic controller (FLC). The FLC that is proposed is improved by using a stochastic diffusion search technique.

Efficient and Accurate Hausdorff Distance Computation Based on Diffusion Search

The Hausdorff distance (HD) between two point sets is widely used in similarity measures, but the high computational cost of HD algorithms restrict their practical use. In this paper, we analyze the time complexity to compute an accurate Hausdorff distance and find that reducing the iterations of the inner loop significantly contributes in reducing the average time cost. Based on the observation that the nearest neighbor (NN) of the breakpoint in the current inner loop suggests a higher probability to break the next inner loop, we present a novel and efficient approach for computing the accurate Hausdorff distance based on a diffusion search. The current breakpoint is recorded as the diffusion center of the next inner loop so that the efficiency of the HD computation can be significantly improved without scanning every point. According to the type of 3-D model (sparse and dense point sets), a novel HD computation framework consisting of two specific diffusion search methods is proposed. First, a Z-order-based HD algorithm (ZHD) for a sparse point sets is proposed. Second, to avoid the low efficiency of a diffusion search when computing the Hausdorff distance between dense point sets with the ZHD algorithm, an octree-based HD algorithm (OHD) is proposed. Evaluation results demonstrate that the ZHD algorithm and the OHD algorithm can greatly reduce the time complexity of HD computations for sparse and dense point sets, respectively. In addition, we conduct several comparative experiments against the most famous HD computation methods. Experimental results show that the proposed approach achieves performance as good as the most efficient available algorithm and exhibits better performance in dealing with spatial data that is highly overlapping.

The topography of the environment alters the optimal search strategy for active particles

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent [Formula: see text] are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from [Formula: see text] to [Formula: see text] (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with [Formula: see text] assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher's size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers' taxis to biochemical rates of reaction.

Effects of the target aspect ratio and intrinsic reactivity onto diffusive search in bounded domains

We study the mean first passage time (MFPT) to a reaction event on a specific site in a cylindrical geometry—characteristic, for instance, for bacterial cells, with a concentric inner cylinder representing the nuclear region of the bacterial cell. A similar problem emerges in the description of a diffusive search by a transcription factor protein for a specific binding region on a single strand of DNA. We develop a unified theoretical approach to study the underlying boundary value problem which is based on a self-consistent approximation of the mixed boundary condition. Our approach permits us to derive explicit, novel, closed-form expressions for the MFPT valid for a generic setting with an arbitrary relation between the system parameters. We analyse this general result in the asymptotic limits appropriate for the above-mentioned biophysical problems. Our investigation reveals the crucial role of the target aspect ratio and of the intrinsic reactivity of the binding region, which were disregarded in previous studies. Theoretical predictions are confirmed by numerical simulations.

The induced motion of a probe coupled to a bath with random resettings

We consider a probe linearly coupled to the center of mass of a nonequilibrium bath. We study the induced motion on the probe for a model where a resetting mechanism is added to an overdamped bath dynamics with quadratic potentials. The fact that each bath particle is at random times reset to a fixed position is known for optimizing diffusive search strategies, but here stands for the nonequilibrium aspect of the bath. In the large bath scaling limit the probe is governed by an effective Langevin equation. Depending on the value of the parameters, there appear three regimes: (i) an equilibrium-like regime but with a reduced friction and an increased effective temperature; (ii) a regime where the noise felt by the probe is continuous but non-Gaussian and exhibits fat-tails; (iii) a regime with a non-Gaussian noise exhibiting power-law distributed jumps. The model thus represents an exactly solvable case for the origin of nonequilibrium probe dynamics.

Swarmic approach for symmetry detection of cellular automata behaviour

Since the introduction of cellular automata in the late 1940s they have been used to address various types of problems in computer science and other multidisciplinary fields. Their generative capabilities have been used for simulating and modelling various natural, physical and chemical phenomena. Besides these applications, the lattice grid of cellular automata has been providing a by-product interface to generate graphical contents for digital art creation. One important aspect of cellular automata is symmetry, detecting of which is often a difficult task and computationally expensive. In this paper a swarm intelligence algorithm—Stochastic Diffusion Search—is proposed as a tool to identify points of symmetry in the cellular automata-generated patterns.

SAR images denoising using a novel stochastic diffusion wavelet scheme

The rapid processing of remote sensing (RS) images is crucial in real-time monitoring. However, the computation cost of RS is high and traditional methods are not effective. Cloud computing with its capability of parallel computing provides an effective service for executing RS processing. A cloud-integrated web platform for maintaining geographic information system (GIS) and RS application such as oil spill detection, meteorological monitoring through synthetic aperture radar (SAR) images is a fast-growing application. SAR RS helps also in studying land and sea-based phenomena, especially the capability of acquiring weather forecasts all-day. Wavelet transform is a very well-known tool for prime applications in time series, function estimation, and image analysis. In this work, an effective non-deterministic polynomial computation technique for noise mitigation of SAR Images which has its basis on Hybrid wavelet transform (WT) is proposed. Proper noise reduction parameters should be chosen while selecting wavelet transform for SAR images. So, in the proposed method Stochastic diffusion search (SDS) optimization algorithm is utilized for selecting the optimal noise reduction parameter thus leading better filtration performance. Effective choice of wavelet noise mitigation techniques includes wavelet function, decomposition levels, and threshold selection rules for improved noise reduction. Experimental results show that MSE, PSNR and standard deviation are enhanced with the proposed method.

Spatial Distribution and Ribosome-Binding Dynamics of EF-P in Live Escherichiacoli.

ABSTRACTIn vitro assays find that ribosomes form peptide bonds to proline (Pro) residues more slowly than to other residues. Ribosome profiling shows that stalling at Pro-Pro-X triplets is especially severe but is largely alleviated in Escherichia coli by the action of elongation factor EF-P. EF-P and its eukaryotic/archaeal homolog IF5A enhance the peptidyl transfer step of elongation. Here, a superresolution fluorescence localization and tracking study of EF-P–mEos2 in live E. coli provides the first in vivo information about the spatial distribution and on-off binding kinetics of EF-P. Fast imaging at 2 ms/frame helps to distinguish ribosome-bound (slowly diffusing) EF-P from free (rapidly diffusing) EF-P. Wild-type EF-P exhibits a three-peaked axial spatial distribution similar to that of ribosomes, indicating substantial binding. The mutant EF-PK34A exhibits a homogeneous distribution, indicating little or no binding. Some 30% of EF-P copies are bound to ribosomes at a given time. Two-state modeling and copy number estimates indicate that EF-P binds to 70S ribosomes during 25 to 100% of translation cycles. The timescale of the typical diffusive search by free EF-P for a ribosome-binding site is τfree ≈ 16 ms. The typical residence time of an EF-P on the ribosome is very short, τbound ≈ 7 ms. Evidently, EF-P binds to ribosomes during many or most elongation cycles, much more often than the frequency of Pro-Pro motifs. Emptying of the E site during part of the cycle is consistent with recent in vitro experiments indicating dissociation of the deacylated tRNA upon translocation.

Self-Tuning VGPI Controller Based on ACO Method Applied for WTGS system

<p>The stability and the fast response are two parameters to evaluate the efficiency of any system, and the acknowledgement of the mathematic model studied and its parameters are strongly required. In order to build the regulation and the control of the system, different methods are used. Some are traditional (PI, PD, PID…); whereas, others are modern (Fuzzy logic, neural networks, statistical algorithms, genetic algorithms, VGPI and so on…).</p>In this paper, we focused on the presentation of a new method which we call the scheduling regulation based on a particle swarm optimization. A stochastic diffusion search method that takes inspiration from the social behaviors of real ants with their environment. Ant colony optimization algorithms (ACO) presents a promising performance which is a self-organized regulation system with no need to the acknowledgment of both the mathematic model and the parameters of the systems from a side, and it can insure the stability and the fast response of the system from another side.

Prediction of the moderator temperature field in a heavy water reactor based on a cellular neural network

Reactors with heavy water coolants and moderators have been used extensively in today's power industry. Monitoring of the moderator condition plays an important role in ensuring normal operation of a power plant. A cellular neural network, the architecture of which has been adapted for hardware implementation, is proposed for use in a system for prediction of the heavy water moderator temperature. A reactor model composed in accordance with the CANDU Darlington heavy water reactor design was used to form the training sample collection and to control correct operation of the neural network structure. The sample components for the adjustment and configuration of the network topology include key parameters that characterize the energy generation process in the core. The paper considers the feasibility of the temperature prediction only for the calandria's central cross-section. To solve this problem, the cellular neural network architecture has been designed, and major parts of the digital computational element and methods for their implementation based on an FPLD have also been developed. The method is described for organizing an optical coupling between individual neural modules within the network, which enables not only the restructuring of the topology in the training process, but also the assignment of priorities for the propagation of the information signals of neurons depending on the activity in a situation analysis at the neural network structure inlet. Asynchronous activation of cells was used based on an oscillating fractal network, the basis for which was a modified ring oscillator. The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated. A comparative analysis of the model behavior and the results of the neural network operation have shown that the use of the neural network approach is effective in safety systems of power plants.

Modeling and Optimization of a CoolingTower-Assisted Heat Pump System

To minimize the total energy consumption of a cooling tower-assisted heat pump (CTAHP) system in cooling mode, a model-based control strategy with hybrid optimization algorithm for the system is presented in this paper. An existing experimental device, which mainly contains a closed wet cooling tower with counter flow construction, a condenser water loop and a water-to-water heat pump unit, is selected as the study object. Theoretical and empirical models of the related components and their interactions are developed. The four variables, viz. desired cooling load, ambient wet-bulb temperature, temperature and flow rate of chilled water at the inlet of evaporator, are set to independent variables. The system power consumption can be minimized by optimizing input powers of cooling tower fan, spray water pump, condenser water pump and compressor. The optimal input power of spray water pump is determined experimentally. Implemented on MATLAB, a hybrid optimization algorithm, which combines the Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with the greedy diffusion search (GDS) algorithm, is incorporated to solve the minimization problem of energy consumption and predict the system’s optimal set-points under quasi-steady-state conditions. The integrated simulation tool is validated against experimental data. The results obtained demonstrate the proposed operation strategy is reliable, and can save energy by 20.8% as compared to an uncontrolled system under certain testing conditions.

Optimal potentials for diffusive search strategies

We consider one dimensional diffusive search strategies subjected to external potentials. The location of a single target is drawn from a given probability density function (PDF) and is fixed for each stochastic realization of the process. We optimize the quality of the search strategy as measured by the mean first passage time (MFPT) to the position of the target. For a symmetric but otherwise arbitrary distribution we find the optimal potential that minimizes the MFPT. The minimal MFPT is given by a nonstandard measure of the dispersion, which can be related to the cumulative Rényi entropy. We compare optimal times in this model with optimal times obtained for the model of diffusion with stochastic resetting, in which the diffusive motion is interrupted by intermittent jumps (resets) to the initial position. Additionally, we discuss an analogy between our results and a so-called square-root principle.

Facilitated Diffusion of Transcription Factor Proteins with Anomalous Bulk Diffusion

What are the physical laws of the diffusive search of proteins for their specific binding sites on DNA in the presence of the macromolecular crowding in cells? We performed extensive computer simulations to elucidate the protein target search on DNA. The novel feature is the viscoelastic non-Brownian protein bulk diffusion recently observed experimentally. We examine the influence of the protein-DNA binding affinity and the anomalous diffusion exponent on the target search time. In all cases an optimal search time is found. The relative contribution of intermittent three-dimensional bulk diffusion and one-dimensional sliding of proteins along the DNA is quantified. Our results are discussed in the light of recent single molecule tracking experiments, aiming at a better understanding of the influence of anomalous kinetics of proteins on the facilitated diffusion mechanism.

Active Cargo Positioning from Actin-Polarity Sensing by Small Myosin Assemblies

Molecular motors navigate the cytoskeleton to position vesicles and organelles at specific locations in the cell. Cytoskeletal filaments assemble into parallel, antiparallel or disordered networks, providing a complex environment that constrains active transport properties. Using surface micro-patterns of nucleation-promoting factors to control the geometry of actin polymerization, we studied in vitro the interplay between the actin-network architecture and cargo transport by small myosin assemblies. With two parallel nucleation lines, we produced an antiparallel network of overlapping filaments. We found that 200-nm beads coated with processive myosin-5 motors displayed directed movements towards the midline of the pattern, where the net polarity of the actin network was null, and accumulated there. The bead distribution was dictated by the spatial profiles of bead velocity and diffusion coefficient, indicating that a diffusion-drift process was at work. Interestingly, beads coated with skeletal heavy mero-myosin-2 motors showed a similar behavior. However, although velocity gradients were sharper with myosin 2, the much larger bead diffusion observed with this non-processive motor resulted in less precise positioning. Strikingly, bead positioning did not depend on the spacing between the nucleation lines. Our observations are well described by a three-state model of bead transport, in which active beads locally sense the net polarity of the filament network by frequently detaching from and reattaching to the filaments. A stochastic sequence of processive runs and diffusive searches results in a biased random walk with an effective drift velocity and diffusion coefficient. Positioning relies on spatial gradients of the net actin polarity, as well as on the run length of the cargo in the attached state. Altogether, our results on a minimal acto-myosin system demonstrate the key role played by the actin-network architecture on motor transport.

Swarmic autopoiesis and computational creativity

ABSTRACTIn this paper two swarm intelligence algorithms are used, the first leading the “attention” of the swarm and the latter responsible for the tracing mechanism. The attention mechanism is coordinated by agents of Stochastic Diffusion Search where they selectively attend to areas of a digital canvas (with line drawings) which contains (sharper) corners. Once the swarm's attention is drawn to the line of interest with a sharp corner, the corresponding line segment is fed into the tracing algorithm, Dispersive Flies Optimisation which “consumes” the input in order to generate a “swarmic sketch” of the input line. The sketching process is the result of the “flies” leaving traces of their movements on the digital canvas which are then revisited repeatedly in an attempt to re-sketch the traces they left. This cyclic process is then introduced in the context of autopoiesis, where the philosophical aspects of the autopoietic artist are discussed. The autopoetic artist is described in two modalities: gluttonous and contented. In the Gluttonous Autopoietic Artist mode, by iteratively focussing on areas-of-rich-complexity, as the decoding process of the input sketch unfolds, it leads to a less complex structure which ultimately results in an empty canvas; therein reifying the artwork's “death”. In the Contented Autopoietic Artist mode, by refocussing the autopoietic artist's reflections on “meaning” onto different constitutive elements, and modifying her reconstitution, different behaviours of autopoietic creativity can be induced and therefore, the autopoietic processes become less likely to fade away and more open-ended in their creative endeavour.

Searches for diffuse fluxes of astrophysical neutrinos with the ANTARES telescope

In this proceedings we report on the status of searches for diffuse fluxes of cosmic neutrinos with the ANTARES neutrino telescope data. A complete overview of diffuse neutrino searches will be given, together with the search for a neutrino emission from regions such as the Fermi Bubbles or the Galactic Plane. A non-significant, though intriguing, excess of events above the atmospheric background is observed in all-sky analysis both for the track and shower channels.

Telomere Recognition and Assembly Mechanism of Mammalian Shelterin

Shelterin is a six-subunit protein complex that plays crucial roles in telomere length regulation, protection, and maintenance. Although several shelterin subunits have been studied invitro, the biochemical properties of the fully assembled shelterin complex are not well defined. Here, we characterize shelterin using ensemble biochemical methods, electron microscopy, and single-molecule imaging to determine how shelterin recognizes and assembles onto telomeric repeats. We show that shelterin complexes can exist in solution and primarily locate telomeric DNA through a three-dimensional diffusive search. Shelterin can diffuse along non-telomeric DNA but is impeded by nucleosomes, arguing against extensive one-dimensional diffusion as a viable assembly mechanism. Our work supports a model in which individual shelterin complexes rapidly bind to telomeric repeats as independent functional units, which do not alter the DNA-binding mode of neighboring complexes but, rather, occupy telomeric DNA in a "beads on a string" configuration.

Estimation of distribution algorithms based on increment clustering for multiple optima in dynamic environments

Aiming to locate and track multiple optima in dynamic multimodal environments, an estimation of distribution algorithms based on increment clustering is proposed. The main idea of the proposed algorithm is to construct several probability models based on an increment clustering which improved performance for locating multiple local optima and contributed to find the global optimal solution quickly for dynamic multimodal problems. Meanwhile, a policy of diffusion search is introduced to enhance the diversity of the population in a guided fashion when the environment is changed. The policy uses both the current population information and the part history information of the optimal solutions available. Experimental studies on the moving peaks benchmark are carried out to evaluate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms from the literature. The results show that the proposed algorithm is effective for the function with moving optimum and can adapt to the dynamic environments rapidly.