Variable threshold algorithm for division of labor analyzed as a dynamical system

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

A novel multigene family encodes diversified variable regions

Antigen recognition in the adaptive immune response by Ig and T-cell antigen receptors (TCRs) is effected through patterned differences in the peptide sequence in the V regions. V-region specificity forms through genetically programmed rearrangement of individual, diversified segmental elements in single somatic cells. Other Ig superfamily members, including natural killer receptors that mediate cell-surface recognition, do not undergo segmental reorganization, and contain type-2 C (C2) domains, which are structurally distinct from the C1 domains found in Ig and TCR. Immunoreceptor tyrosine-based inhibitory motifs that transduce negative regulatory signals through the cell membrane are found in certain natural killer and other cell surface inhibitory receptors, but not in Ig and TCR. In this study, we employ a genomic approach by using the pufferfish (Spheroides nephelus) to characterize a nonrearranging novel immune-type receptor gene family. Twenty-six different nonrearranging genes, which each encode highly diversified V as well as a V-like C2 extracellular domain, a transmembrane region, and in most instances, an immunoreceptor tyrosine-based inhibitory motif-containing cytoplasmic tail, are identified in an ≈113 kb P1 artificial chromosome insert. The presence in novel immune-type receptor genes of V regions that are related closely to those found in Ig and TCR as well as regulatory motifs that are characteristic of inhibitory receptors implies a heretofore unrecognized link between known receptors that mediate adaptive and innate immune functions.

Adaptive evolution of a candidate gene for aging in Drosophila

Examination of the phenotypic effects of specific mutations has been extensively used to identify candidate genes affecting traits of interest. However, such analyses do not reveal anything about the evolutionary forces acting at these loci, or whether standing allelic variation contributes to phenotypic variance in natural populations. The Drosophila gene methuselah (mth) has been proposed as having major effects on organismal stress response and longevity phenotype. Here, we examine patterns of polymorphism and divergence at mth in population level samples of Drosophila melanogaster, D. simulans, and D. yakuba. Mth has experienced an unusually high level of adaptive amino acid divergence concentrated in the intra- and extracellular loop domains of the receptor protein, suggesting the historical action of positive selection on those regions of the molecule that modulate signal transduction. Further analysis of single nucleotide polymorphisms (SNPs) in D. melanogaster provided evidence for contemporary and spatially variable selection at the mth locus. In ten surveyed populations, the most common mth haplotype exhibited a 40% cline in frequency that coincided with population level differences in multiple life-history traits including lifespan. This clinal pattern was not associated with any particular SNP in the coding region, indicating that selection is operating at a closely linked site that may be involved in gene expression. Together, these consistently nonneutral patterns of inter- and intraspecific variation suggest adaptive evolution of a signal transduction pathway that may modulate lifespan in nature.

Modelling adaptive web applications

Conceptual Modelling approaches for the web need extensions to specify dynamic personalization properties in order to design more powerful web applications. Current approaches provide techniques to support dynamic personalization, usually focused on implementation details. This article presents an extension of the OO-H conceptual modeling approach to address the particulars associated with the design and specification of dynamic personalization. The main benefit is that this specification can be modified without recompile the rest of the application modules. We describe how conventional navigation and presentation diagrams are influenced by personalization properties. In order to model the variable part of the interface logic OO-H has a personalization architecture that leans on a rule engine. Rules are defined based on a User Model and a Reference Model.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focussed recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the best choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy. (C) 2004 Elsevier B.V. All rights reserved.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Adaptive Fault-Tolerant Teleoperation

While the robots gradually become a part of our daily lives, they already play vital roles in many critical operations. Some of these critical tasks include surgeries, battlefield operations, and tasks that take place in hazardous environments or distant locations such as space missions. In most of these tasks, remotely controlled robots are used instead of autonomous robots. This special area of robotics is called teleoperation. Teleoperation systems must be reliable when used in critical tasks; hence, all of the subsystems must be dependable even under a subsystem or communication line failure. These systems are categorized as unilateral or bilateral teleoperation. A special type of bilateral teleoperation is described as force-reflecting teleoperation, which is further investigated as limited- and unlimited-workspace teleoperation. Teleoperation systems configured in this study are tested both in numerical simulations and experiments. A new method, Virtual Rapid Robot Prototyping, is introduced to create system models rapidly and accurately. This method is then extended to configure experimental setups with actual master systems working with system models of the slave robots accompanied with virtual reality screens as well as the actual slaves. Fault-tolerant design and modeling of the master and slave systems are also addressed at different levels to prevent subsystem failure. Teleoperation controllers are designed to compensate for instabilities due to communication time delays. Modifications to the existing controllers are proposed to configure a controller that is reliable in communication line failures. Position/force controllers are also introduced for master and/or slave robots. Later, controller architecture changes are discussed in order to make these controllers dependable even in systems experiencing communication problems. The customary and proposed controllers for teleoperation systems are tested in numerical simulations on single- and multi-DOF teleoperation systems. Experimental studies are then conducted on seven different systems that included limited- and unlimited-workspace teleoperation to verify and improve simulation studies. Experiments of the proposed controllers were successful relative to the customary controllers. Overall, by employing the fault-tolerance features and the proposed controllers, a more reliable teleoperation system is possible to design and configure which allows these systems to be used in a wider range of critical missions.

Chemokine Receptor Expression on Normal Blood CD56(+) NK-Cells Elucidates Cell Partners That Comigrate during the Innate and Adaptive Immune Responses and Identifies a Transitional NK-Cell Population

Studies of chemokine receptors (CKR) in natural killer- (NK-) cells have already been published, but only a few gave detailed information on its differential expression on blood NK-cell subsets. We report on the expression of the inflammatory and homeostatic CKR on normal blood CD56(+low) CD16(+) and CD56(+high)  CD16(-/+low) NK-cells. Conventional CD56(+low) and CD56(+high) NK-cells present in the normal PB do express CKR for inflammatory cytokines, although with different patterns CD56(+low) NK-cells are mainly CXCR1/CXCR2(+) and CXCR3/CCR5(-/+), whereas mostly CD56(+high) NK-cells are CXCR1/CXCR2(-) and CXCR3/CCR5(+). Both NK-cell subsets have variable CXCR4 expression and are CCR4(-) and CCR6(-). The CKR repertoire of the CD56(+low) NK-cells approaches to that of neutrophils, whereas the CKR repertoire of the CD56(+high) NK-cells mimics that of Th1(+) T cells, suggesting that these cells are prepared to migrate into inflamed tissues at different phases of the immune response. In addition, we describe a subpopulation of NK-cells with intermediate levels of CD56 expression, which we named CD56(+int) NK-cells. These NK-cells are CXCR3/CCR5(+), they have intermediate levels of expression of CD16, CD62L, CD94, and CD122, and they are CD57(-) and CD158a(-). In view of their phenotypic features, we hypothesize that they correspond to a transitional stage, between the well-known CD56(+high) and CD56(+low) NK-cells populations.

Social engagement and adaptive functioning during early childhood: Identifying and distinguishing among subgroups differing with regard to social engagement

This study tested the hypothesis that social engagement (SE) with peers is a fundamental aspect of social competence during early childhood. Relations between SE and a set of previously validated social competence indicators, as well as additional variables derived from observation and sociometric interviews were assessed using both variable-centered and person-centered approaches (N = 1453, 696 girls) in 4 samples (3 U.S.A., 1 Portuguese). Directly observed SE was positively associated with broad-band measures of socially competent behavior, peer acceptance, being a target of peers' attention, and also with broad-band personality dimensions. Using individual Q-items significantly associated with SE in 3 of our 4 samples, a hierarchical cluster analysis yielded a 5-cluster solution that grouped cases efficiently. Tests on relations between cluster membership and the set of social competence and other variables revealed significant main effects of cluster membership in the full sample and within each individual sample, separately. With the exception of tests for peer negative preference, children in the lowest SE cluster also had significantly lower overall social competence, personality functioning scores than did children in higher SE clusters.

Using SPL to Develop AAL Systems Based on Self-adaptive Agents

Abstract One of the most important challenges of this decade is the Internet of Things (IoT) that pursues the integration of real-world objects in Internet. One of the key areas of the IoT is the Ambient Assisted Living (AAL) systems, which should be able to react to variable and continuous changes while ensuring their acceptance and adoption by users. This means that AAL systems need to work as self-adaptive systems. The autonomy property inherent to software agents, makes them a suitable choice for developing self-adaptive systems. However, agents lack the mechanisms to deal with the variability present in the IoT domain with regard to devices and network technologies. To overcome this limitation we have already proposed a Software Product Line (SPL) process for the development of self-adaptive agents in the IoT. Here we analyze the challenges that poses the development of self-adaptive AAL systems based on agents. To do so, we focus on the domain and application engineering of the self-adaptation concern of our SPL process. In addition, we provide a validation of our development process for AAL systems.

Social engagement and adaptive functioning during early childhood: Identifying and distinguishing among subgroups differing with regard to social engagement

Materia Suplementar disponível em: http://dx.doi.org/10.1037/dev0000142.supp

Adaptive strategies of two Portuguese grapevines varieties (Aragonês. syn. Tempranillo and Trincadeira) face drought: physiological and structural responses.

At Mediterranean regions and particularly in southern Portugal, it is imperative to identify grape varieties more adapted to warm and dry climates in order to overcome future climatic changes. Two Vitis vinifera genotypes, Aragonez (syn. Tempranillo) and Trincadeira, were selected to assess their physiological responses to soil water stress. Vines were subjected to four irrigation regimes: irrigated during all phenological cycle, non-irrigated during all phenological cycle, non irrigated until veraison, irrigated after veraison. Predawn leaf water potential was much higher in Trincadeira than Aragonez in non- irrigated plants. This result is in accordance with its higher stomatal control efficiency in this variety (Trincadeira). Photosynthetic capacity (Amax at saturating light intensity) decreased due to stomatal and biochemical limitations under water stress. However, recovery capacity of leaf water status after irrigation was faster in Trincadeira. Yield and yield x Brix increased when irrigation occurred after veraison, particularly in Trincadeira. These results show that Trincadeira presents a drought adaptation than Aragonez. Ratio of variable to maximum fluorescence Fv/Fm and total leaf chlorophyll related with leaf water potential for both species. Reflectance Normalized Difference Vegetation Index (NDVI705), Red Edge Inflexion Point Index and Photochemical Reflectance Index were related with irrigation treatment. Relative water content and specific leaf area were similar between varieties. In conclusion, we suggested that there is variation among the genotypes and the main physiological parameters for variety selection, for drought, were leaf water potential, stomatal conductance and reflectance indexes.