Iterative Map Research Articles

Evolutionary oligopoly games with cooperative and aggressive behaviors

We propose an oligopoly model where players can choose between two kinds of behaviors, denoted as cooperative and aggressive, respectively. Each cooperative agent chooses the quantity to produce in order to maximize her own profit as well as the profits of other agents (at least partially), whereas an aggressive player decides the quantity to produce by maximizing his own profit while damaging (at least partially) competitors’ profits. At each discrete time, players face a binary choice to select the kind of behavior to adopt, according to a proportional imitation rule, expressed by a replicator equation based on a comparison between accumulated profits. This means that the behavioral decisions are driven by an evolutionary process where fitness is measured in terms of current profits as well as a weighted sum of past gains. The model proposed is expressed by a nonlinear two-dimensional iterated map, whose asymptotic behavior describes the long-run population distribution of cooperative and aggressive agents. We show under which conditions one of the following long-run behaviors prevails: (i) all players choose the same strategy; (ii) both behaviors coexist according to a mixed stationary equilibrium; and (iii) a self-sustained (i.e. endogenous) oscillatory (periodic or chaotic) time pattern occurs. The influence of memory and that of the levels of cooperative/aggressive attitudes on the dynamics are analyzed as well.

Analysis of the -CSA-ES with Repair by Projection Applied to a Conically Constrained Problem.

Theoretical analyses of evolution strategies are indispensable for gaining a deep understanding of their inner workings. For constrained problems, rather simple problems are of interest in the current research. This work presents a theoretical analysis of a multi-recombinative evolution strategy with cumulative step size adaptation applied to a conically constrained linear optimization problem. The state of the strategy is modeled by random variables and a stochastic iterative mapping is introduced. For the analytical treatment, fluctuations are neglected and the mean value iterative system is considered. Nonlinear difference equations are derived based on one-generation progress rates. Based on that, expressions for the steady state of the mean value iterative system are derived. By comparison with real algorithm runs, it is shown that for the considered assumptions, the theoretical derivations are able to predict the dynamics and the steady state values of the real runs.

A multi-modal image fusion framework based on guided filter and sparse representation

To completely integrate the structures of images, a cartoon-texture decomposition based image fusion framework is proposed in this paper. The cartoon components represent the background and main structures information. Due to its high similarity, the sparse representation is utilized based on an overcomplete cartoon dictionary. Moreover, a modified orthogonal matching pursuit is proposed to improve the fusion image continuity and eliminate artifacts interferences. The texture components describe more distinct structures, which have diverse scale characteristics. To preserve more relevant infrared details in fused texture, an iterative visual saliency map is proposed. Furthermore, the weight maps are computed on the basis of a designed multi-scale guided filter and saliency map; then, the weight maps are utilized to guide the texture fusion, aiming to improve the visual and scale-aware salience. The final fused image is obtained by combining the cartoon fusion and texture fusion results. The comprehensive and objection evaluations demonstrate the outperformance on accuracy, robustness and versatility of the proposed fusion framework.

Linked Entity Attribute Pair (LEAP): A Harmonization Framework for Data Pooling.

PURPOSEAs data-sharing projects become increasingly frequent, so does the need to map data elements between multiple classification systems. A generic, robust, shareable architecture will result in increased efficiency and transparency of the mapping process, while upholding the integrity of the data.MATERIALS AND METHODSThe American Association for Cancer Research’s Genomics Evidence Neoplasia Information Exchange (GENIE) collects clinical and genomic data for precision cancer medicine. As part of its commitment to open science, GENIE has partnered with the National Cancer Institute’s Genomic Data Commons (GDC) as a secondary repository. After initial efforts to submit data from GENIE to GDC failed, we realized the need for a solution to allow for the iterative mapping of data elements between dynamic classification systems. We developed the Linked Entity Attribute Pair (LEAP) database framework to store and manage the term mappings used to submit data from GENIE to GDC.RESULTSAfter creating and populating the LEAP framework, we identified 195 mappings from GENIE to GDC requiring remediation and observed a 28% reduction in effort to resolve these issues, as well as a reduction in inadvertent errors. These results led to a decrease in the time to map between OncoTree, the cancer type ontology used by GENIE, and International Classification of Disease for Oncology, 3rd Edition, used by GDC, from several months to less than 1 week.CONCLUSIONThe LEAP framework provides a streamlined mapping process among various classification systems and allows for reusability so that efforts to create or adjust mappings are straightforward. The ability of the framework to track changes over time streamlines the process to map data elements across various dynamic classification systems.

First Report of Cherry Virus Turkey in Sweet Cherry in Greece

The number of viruses identified in sweet cherry has been constantly increasing over the last few years, following the broad application of high-throughput sequencing (HTS). Some of these were reported to cause leaf symptoms and yield reduction. In 2009 to 2013, surveys were performed on sweet cherry orchards located in northern Greece for the presence of Betaflexiviridae viruses using generic and specific molecular assays (Foissac et al. 2005). Sanger sequencing of a generic reverse transcription polymerase chain reaction (RT-PCR) product originated from a sample collected from a symptomless sweet cherry (cv. Ferrovia) revealed a virus sequence sharing 75% nucleotide (nt) similarity with a highly conserved portion of the betaflexivirus polymerase gene. Total RNA, extracted with the TRIzol reagent (Invitrogen, Carlsbad, CA) from leaves of this sample, was subjected to HTS analysis on an Illumina Hi-seq 4000 platform (Novogene, Tiangen, China). The run, after quality control, yielded 26,002,319 of 150-nt-long paired-end reads. De novo assembly of these reads using Geneious Prime 2019.2.3 and Velvet and subsequent BLAST analysis (BLASTn/x) of 82 contigs revealed, apart from several known sweet cherry viruses (cherry virus A, Prunus virus F, little cherry virus 1), the presence of a newly described Robigovirus species. More specifically, the BLAST results revealed that eight de novo contigs ranging from 277 to 2,287 nt long were similar to the sequence of the recently reported cherry virus Turkey (CVTR) (Caglayan et al. 2019). The nearly complete genome sequence consisting of 8,362 nt (99.5%) of the Greek CVTR isolate (CVTR-C18GR, accession no. MT043307) was reconstructed using iterative mapping of the reads to the de novo contigs, with 4,690 reads mapped in total and Sanger sequencing of RT-PCR amplicons to fill the sequence gaps. Similarity analysis of the CVTR-C18GR sequence with the full virus sequences from GenBank CVTR-BUR12 (MK600387) and CVTRAZQ (MH177869) showed 90.47 and 90.60% nt identities, respectively. Phylogenetic analyses confirmed the clustering of C18GR with other CVTR isolates. To investigate for the virus presence in Greece, primers F2Rob (5′-GTCAGAGAGAGGTATCTATGTC-3′) and RT1Rob (5′-CAGGCTGTTCATAACCT-3′), which amplify 577 nt of CVTR replicase, were designed. Sixty-six samples from sweet cherry trees showing no virus-like symptoms were collected from various orchards and screened for the presence of CVTR. Eight of them were found to be infected with the new virus. Sanger sequencing of sample C11GR amplicon confirmed the presence of CVTR in Greek fields (96% nt similarity with C18GR, accession no. MT043308). Sequence similarity between C11GR and C18GR isolates was also high (97% nt) in partial TGB2, TGB3, and CP genomic regions amplified with primer pair 7006 F (5′-GAAAAGTGATTATTCAGCRCCAGT-3′) and 7708 R (5′-CTTTCACCCACTCATCACCTATCTCC-3′) (accession no. MT461407). This is the first report of this virus in Greece, thus extending the information on its geographical distribution. Robigoviruses cause variable symptoms and diseases in sweet and sour cherries, and for that reason they are included in the European Commission Directive concerning official inspections (2019/2072/EU). Additional screenings are necessary to evaluate the presence and impact of CVTR in sweet cherry orchards in Greece.

An image encryption approach based on chaotic maps and genetic operations

This paper puts forward an image encryption approach using chaotic maps and genetic operations. First, the Keccak algorithm is employed to compute the hash values of a plain-image as the initial values for chaotic map. The sensitivity and pseudo randomness of chaotic map used for the initial conditions allows for pseudorandom sequences to be obtained by iterative logistic map to shuffle and permute the pixel positions and values of the image. Second, in combination with the Henon map and the DNA coding technique, genetic operations at the bit level are used to achieve pixel selection, crossover and mutation, as well as further completion of pixel diffusion and scrambling, which significantly increases the difficulty of deciphering the algorithm. Finally, the diffusion and confusion features of the algorithm are further strengthened by bidirectional exclusive OR operations with chaotic sequences. The theoretical analysis and simulation results indicate that the algorithm is sensitive to keys and can effectively defend statistical and differential attacks, indicating that it has good security and application potential.

Quality Improvement in a Pandemic.

Quality Improvement in a Pandemic.

Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices

Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental assessment, crop management, and appropriate targeting of agricultural technologies. There is growing research interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies. However, usability of information generated from many of remotely sensed data is often constrained by accuracy problems. This is of particular concern in mapping complex agro-ecosystems in countries where small farm holdings are dominated by diverse crop types. This study is a contribution to the ongoing efforts towards overcoming accuracy challenges faced in remote sensing of agricultural ecosystems. We applied time-series analysis of vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor to detect seasonal patterns of irrigated and rainfed cropping patterns in five townships in the Central Dry Zone of Myanmar, which is an important agricultural region of the country has been poorly mapped with respect to cropping practices. To improve mapping accuracy and map legend completeness, we implemented a combination of (i) an iterative participatory approach to field data collection and classification, (ii) the identification of appropriate size and types of predictor variables (VIs), and (iii) evaluation of the suitability of three Machine Learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and C5.0 algorithms under varying training sample sizes. Through these procedures, we were able to progressively improve accuracy and achieve maximum overall accuracy of 95% When a small sized training dataset was used, accuracy achieved by RF was significantly higher compared to SVM and C5.0 (P < 0.01), but as sample size increased, accuracy differences among the three machine learning algorithms diminished. Accuracy achieved by use of NDVI was consistently better than that of EVI (P < 0.01). The maximum overall accuracy was achieved using RF and 8-days NDVI composites for three years of remote sensing data. In conclusion, our findings highlight the important role of participatory classification, especially in areas where cropping systems are highly diverse and differ over space and time. We also show that the choice of classifiers and size of predictor variables are essential and complementary to the participatory mapping approach in achieving desired accuracy of cropping pattern mapping in areas where other sources of spatial information are scarce.

X-ray ptychographic mode of self-assembled CdSe/CdS octapod-shaped nanocrystals in thick polymers

This work describes the application of X-ray ptychography for the inspection of complex assemblies of highly anisotropic nanocrystals embedded in a thick polymer matrix. More specifically, this case deals with CdSe/CdS octapods, with pod length L = 39 ± 2 nm and pod diameter D = 12 ± 2 nm, dispersed in free-standing thick films (24 ± 4 µm) of polymethyl methacrylate and polystyrene, with different molecular weights. Ptychography is the only imaging method available to date that can be used to study architectures made by these types of nanocrystals in thick polymeric films, as any other alternative direct method, such as scanning/transmission electron microscopy, can be definitively ruled out as a result of the large thickness of the free-standing films. The electron density maps of the investigated samples are reconstructed by combining iterative difference map algorithms and a maximum likelihood optimization algorithm. In addition, post image processing techniques are applied to both reduce noise and provide a better visualization of the material morphological details. Through this process, at a final resolution of 27 nm, the reconstructed maps allow us to visualize the intricate network of octapods inside the polymeric matrices.

A method for calculating the Lyapunov exponent spectrum of DC‐DC converter feeding with a switching constant power load

AbstractNonlinear dynamic behaviors such as the bifurcation and chaos of DC‐DC converters with switching constant power load are studied in this paper. Based on the consideration of constant power load switching, a discrete iterative mapping model of the system including the switching process is established, and its Lyapunov exponent spectrum algorithm is derived. The main procedure of the algorithm is as follows: Firstly, the system is divided into six smooth segments, and the zero‐time discontinuous mapping of the load and mode switching is introduced. Secondly, the local mapping of each smooth segment and the zero‐time discontinuous mapping of the switching phase are used to construct the composite Poincaré mapping of the system by the chain rule, and then the discrete iterative mapping model of the system is established. Finally, the Lyapunov exponent spectrum algorithm of the system is derived based on the model to determine the type of nonlinear dynamic behavior of the system. This algorithm overcomes the problems of the existing methods, which lead to inaccurate results due to the neglect of the switching process, and achieves the purpose of accurately describing the dynamic characteristics of power electronic circuits. The simulation and experimental results have verified the validity of the proposed model and algorithm. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Using species distribution models to guide seagrass management

Seagrasses provide essential and global ecosystem services. However, due to natural and anthropogenic disturbance, seagrass meadows around the world have declined dramatically in recent decades. Researchers and managers have been calling for increased frequency and accuracy in the mapping of seagrass distributions to benefit seagrass conservation for over a decade, and we argue that a critical advancement in the management of seagrasses could come through increased and iterative model-based mapping of potential habitat as an accessory to seagrass monitoring. We further demonstrate how focus on errors of commission and omission during model interpretation could guide management activities, using the Texas Gulf Coast as a case study representing seagrass habitats worldwide. We used the species distribution modeling program Maxent to predict the location of suitable habitat for each seagrass species along the Texas Coast based on local current velocity, distance from boat launches (i.e., an index of human disturbance), nitrogen, light availability, salinity, and temperature. Models accurately predicted suitable habitat for all seagrass species, including Halodule wrightii (AUC = 0.830 ± 0.032), Thalassia testudinum (AUC = 0.901 ± 0.058), Syringodium filiforme (AUC = 0.911 ± 0.036), Halophila engelmannii (AUC = 0.865 ± 0.092), and Ruppia maritima (AUC = 0.868 ± 0.040). The relative importance of environmental factors differed between models. Distributions for H. wrightii and T. testudinum were most influenced by surface nitrate concentrations. S. filiforme, H. engelmannii, and R. maritima distributions were most influenced by benthic light availability. Human disturbances often lead to elevated nitrate concentrations and decreased benthic light availability, and our models generally predicted a lack of suitable habitat near sites characterized by abundant human development. We considered model errors of commission and omission for each species to identify candidate regions for seagrass transplantation and habitat restoration, respectively. Overall, we believe that the utility of the approach we have developed in the Texas Gulf Coast case study along extends beyond a single study site, and our methods will assist conservation of seagrass meadows worldwide.

Intelligent diagnosis of natural gas pipeline defects using improved flower pollination algorithm and artificial neural network

With the increasing service life of pipelines, natural gas pipelines can gradually age and produce various corrosion defects. Hence, in order to ensure the efficiency and safety of pipeline transportation in the peak period of natural gas consumption, the intelligent diagnosis technology of the pipelines is of vital importance. In this paper, iterative chaotic map with infinite collapses (ICMIC) and comprehensive opposition (CO) learning strategy are used to optimize flower pollination algorithm (FPA) to enhance search abilities of original FPA algorithm. Among them, the ICMIC enhances the diversity of population, and the local CO learning strategy enhances its exploitation ability. Fifteen classical benchmark functions are used to test the optimization performance of the improved flower pollination algorithm (IFPA). Considering the category, complexity and application of test functions, a more reasonable evaluation formula is proposed. The test shows that the performance of IFPA algorithm is obviously better than other classical intelligent algorithms. Based on the excellent performance of IFPA, the IFPA algorithm is used to optimize the initial weights and thresholds of back propagation (BP) neural network. Therefore, a comprehensive IFPA-BP network model is constructed for the intelligent diagnosis of natural gas pipeline defects. The results show that the proposed model can effectively overcome the problem that BP neural network is prone to fall into local optimal value, and it can accurately identify the pipelines defects. This will facilitate intelligent diagnosis of natural gas pipelines defects.

On the fixed points of iterative contractive mappings defined via implicit relation

In this paper, we consider an implicit relation to generalize iterative fixed point results in the literature in the context of metric spaces. We conclude that several existing results are immediate consequences of our main results.

Faraday pilot-wave dynamics in a circular corral

A millimetric droplet of silicone oil may bounce and self-propel on the free surface of a vertically vibrating fluid bath due to the droplet’s interaction with its accompanying Faraday wave field. This hydrodynamic pilot-wave system exhibits many dynamics that were previously thought to be peculiar to the quantum realm. When the droplet is confined to a circular cavity, referred to as a ‘corral’, a range of dynamics may occur depending on the details of the geometry and the decay time of the subcritical Faraday waves. We herein present a theoretical investigation into the behaviour of subcritical Faraday waves in this geometry and explore the accompanying pilot-wave dynamics. By computing the Dirichlet-to-Neumann map for the velocity potential in the corral geometry, we can evolve the quasi-potential flow between successive droplet impacts, which, when coupled with a simplified model for the droplet’s vertical motion, allows us to derive and implement a highly efficient discrete-time iterative map for the pilot-wave system. We study the onset of the Faraday instability, the emergence and quantisation of circular orbits and simulate the exotic dynamics that arises in smaller corrals.

The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis.

It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

"You're Always Jumping Through Hoops": Journey Mapping the Care Experiences of Individuals With Opioid Use Disorder-associated Endocarditis.

Serious infectious complications of opioid use disorder (OUD), and specifically endocarditis, are becoming more common in the US. Individuals with OUD-associated endocarditis require long periods of complex medical care, often face recurrent addiction- and infection-related complications, and have dismal clinical outcomes. The objective of this study was to perform journey mapping analysis to capture common trajectories and patterns of care for people with OUD-associated endocarditis. This was an analysis of qualitative semi-structured interviews of individuals who received care for OUD-associated endocarditis. Interviews were conducted among individuals receiving care at a single academic healthcare system in Boston, Massachusetts. Ten participants meeting DSM-5 criteria for at least mild OUD and a culture-positive diagnosis of endocarditis who had previously completed care for OUD-associated endocarditis were recruited from inpatient and ambulatory settings. Details of participant's care episodes were extracted and visualized in an iterative journey mapping process. A grounded theory approach was then used to identify shared themes and care patterns among participants' journey maps. Common patterns of care included early addiction treatment and intensive outpatient care preceding periods without rehospitalization, while leaving outpatient care and return to drug use often directly preceded rehospitalization. Participants frequently left care by choice and proactively reengaged with care. Journey mapping is a novel, patient-centered approach to capturing the care experiences and trajectories of a patient population experiencing significant stigma, who engage with the healthcare system in unexpected and fragmented ways. For individuals with OUD-associated endocarditis, we identified critical moments to support and engage patients to prevent return to drug use and rehospitalization.

Data-driven product design toward intelligent manufacturing: A review

With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.

Process equation as a model for the development of cells

This paper proposes a behavioral model for cells that shows their different dynamics from a high pluripotent stem cell to any distinct cell fate. The proposed model considers a cell as a black-box for a living system and tries to depict the presumed behaviors of the system. The model is a multistable iterated map with sensitive dependence on initial conditions. It indicates various stages of cell evolution in strict order from stem cells in embryos to differentiated cells functioning in an organ. The results show that the dynamical system inters to a situation with an infinite number of coexisting attractors by decreasing the parameter g . In the first stages of division, the system has a more complex dynamic, and so the model has chaotic attractor. Passing the time in the division process results in stronger cell-cell interactions and so the dynamic of the system becomes more ordered and simpler. Finally, at the end of the division process, the system has the simplest dynamic, which is an equilibrium in the model.

Discrete Iterative Map Model-Based Stability Analysis of Capacitor Current Ripple-Controlled SIDO CCM Buck Converter

Capacitor current ripple (CCR) control is applied to single-inductor dual-output (SIDO) buck converter due to its advantages in reducing cross regulation and fast transient response. However, it shows that the stability of CCR-controlled SIDO CCM buck converter can be affected by circuit parameters. In this article, a 5-D discrete iterative map model of CCR-controlled SIDO CCM buck converter is established based on Poincaré maps. The bifurcation analysis and eigenvalues loci clearly shows that the effect of control parameters and load parameters on the stability of CCR-controlled SIDO CCM buck converter. Besides, the effect of studied circuit parameters on the stability range of the converter is investigated. Furthermore, the stability boundaries and stable regions of load parameters and control parameters are obtained by using eigenvalues analysis. The study results show that the increasing of proportional parameters would cause instability and it will reduce the stable region of load parameters. The converter has a stable region for load parameters and these two load parameters have influence on each other. Finally, experimental results are presented to verify the theoretical analysis in this article.

Mechanisms of Subcellular Spatially Discordant Calcium Alternans in Cardiac Myocytes

Subcellular spatially discordant calcium alternans (SDCA) is a behavior in which calcium transient alternates with the opposite phase in different regions of a cell. Experimentally, SDCA has been observed in both atrial and ventricular myocytes in various experimental conditions. Previous studies suggested that SDCA could be resulted from a Turing instability under the condition of negative calcium-to-voltage coupling. However, this theory cannot explain the SDCA observed in voltage clamp experiments and those noted when the calcium-to-voltage coupling is positive. Here we use computer simulations and theoretical treatments to investigate the dynamical mechanisms of SDCA. We first carried out simulations with a physiologically detailed ventricular myocyte model. Under the action potential clamped condition, the spatial calcium release patterns can be either concordant or discordant, and the discordant patterns are random, which depends on the initial conditions. Under the free-running action potential condition, the spatial calcium release pattern depends not only on the properties of calcium-to-voltage coupling but also on the pacing rates. At fast pacing rates, the release pattern changes from concordant to discordant when the calcium-to-voltage coupling changes from positive to negative. When the pacing rate decreases to a certain value, the effect of calcium-to-voltage coupling on the transition from concordant to discordant is reversed. We then proposed a FitzHugh-Nagumo model with a global delayed feedback loop, which well captures the key dynamics of the ventricular cell model. Finally, we extracted a coupled iterated map model to perform nonlinear dynamics analysis. This map model reveals the bifurcations leading to the patterns observed in the detailed ventricular cell model and the simplified FitzHugh-Nagumo model.