Iterative Map Research Articles

Next Generation Multimodal Chromatography Resins <i>via</i> an Iterative Mapping Approach: Chemical Diversity, High-Throughput Screening, and Chromatographic Modelling

Next Generation Multimodal Chromatography Resins <i>via</i> an Iterative Mapping Approach: Chemical Diversity, High-Throughput Screening, and Chromatographic Modelling

Making a Pangenome Using the Iterative Mapping Approach.

Pangenomes have replaced single reference genomes as genetic references, as they contain a better scope of the diversity found in a single species. This protocol outlines the iterative mapping approach in constructing a pangenome, including how to check the raw data, align the data to a reference, how to assemble the data, and how to remove potential contaminants from the final assembly.

Decentralized finance, centralized ownership? An iterative mapping process to measure protocol token distribution

In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well as token wrappers, and can be used to break down token holdings, even for high nesting levels. We compute individual address balances for several snapshots and analyze intertemporal distribution changes. In addition, we study reallocation and protocol usage data, and propose a proxy for measuring token dependencies and ecosystem integration. The paper offers new insights on DeFi interoperability as well as token ownership distribution and may serve as a foundation for further research.

Chimera state in coupled map lattice of matrices

In recent years, a lot of research has focused on understanding the behavior of when synchronous and asynchronous phases occur, that is, the existence of chimera states in various networks. Chimera states have wide-range applications in many disciplines including biology, chemistry, physics, or engineering.
 The object of research in this paper is a coupled map lattice of matrices when each node is described by an iterative map of matrices of order two. A regular topology network of iterative maps of matrices was formed by replacing the scalar iterative map with the iterative map of matrices in each node. The coupled map of matrices is special in a way where we can observe the effect of divergence. This effect can be observed when the matrix of initial conditions is a nilpotent matrix.
 Also, the evolution of the derived network is investigated. It is found that the network of the supplementary variable $\mu$ can evolve into three different modes: the quiet state, the state of divergence, and the formation of divergence chimeras. The space of parameters of node coupling including coupling strength $\varepsilon$ and coupling range $r$ is also analyzed in this study. Image entropy is applied in order to identify chimera state parameter zones.

Aging in the downtown corridor: Mapping the neighborhood experiences of Seattle's unhoused adults over age 50

Research has established the importance of understanding the dynamic relationship between older adults and the environments in which they are embedded. However, the meaning of place for unhoused older adults amidst an increasingly contested urban landscape is largely unknown. This exploratory study aims to further include unhoused older adults' experiences in the scholarship on aging and place by asking how unhoused adults over age 50 (1) describe their spatial patterns and experiences and (2) negotiate their relationship with common urban places. Through iterative mapping conducted in focus groups and interviews at Seattle senior centers, respondents identified how they interacted with their communities and environment. Using inductive and deductive coding of both textual and geospatial data, thematic analysis indicated that respondents: (1) experienced confinement to the downtown corridor and expulsion from surrounding areas– a phenomenon compounded by physical and subjective aging; (2) created routines amidst geographic and temporal restrictions to maximize comfort and security; (3) attempted to create residential normalcy in public places through adaptive and accommodative practices; and (4) experienced identities shaped by movement through and access to place. Current social, spatial, and political contexts of city living present many challenges for older unhoused adults. Supports that ignore people's identification with the places that are important to them are unlikely to be successful. Findings from this paper call for service, policy, and design strategies that facilitate personal agency and connection to place among unhoused people midlife and beyond.

Coupled hypergraph maps and chaotic cluster synchronization

Coupled map lattices (CMLs) are prototypical dynamical systems on networks/graphs. They exhibit complex patterns generated via the interplay of diffusive/Laplacian coupling and nonlinear reactions modelled by a single iterated map at each node; the maps are often taken as unimodal, e.g., logistic or tent maps. In this letter, we propose a class of higher-order coupled dynamical systems involving the hypergraph Laplacian, which we call coupled hypergraph maps (CHMs). By combining linearized (in-)stability analysis of synchronized states, hypergraph spectral theory, and numerical methods, we detect robust regions of chaotic cluster synchronization occurring in parameter space upon varying coupling strength and the main bifurcation parameter of the unimodal map. Furthermore, we find key differences between Laplacian and hypergraph Laplacian coupling and detect various other classes of periodic and quasi-periodic patterns. The results show the high complexity of coupled graph maps and indicate that they might be an excellent universal model class to understand the similarities and differences between dynamics on classical graphs and dynamics on hypergraphs.

TEC Map Completion Through a Deep Learning Model: SNP‐GAN

AbstractThe limited availability of ground receiver stations causes an approximate 52% of data gaps in Massachusetts Institute of Technology (MIT)‐ total electron content (TEC) global maps. The completed TEC maps are highly desirable for both scientific research and space weather applications. Compared to the conventional image inpainting methods, the deep learning methods using generative adversarial networks (GANs) offer an effective image inpainting tool. We adapt the Spectrally Normalized Patch GAN (SNP‐GAN) for the TEC map completion using a traditional complete TEC data source, the International Global Navigation Satellite System TEC (IGS‐TEC) maps, as the training data. For 10‐fold cross‐validation of 20‐year IGS‐TEC data, SNP‐GAN reduces the root mean squared error (RMSE) by more than 30% compared to our previous model, the deep convolutional GAN with Poisson blending (DCGAN‐PB). Two case studies using MIT‐TEC data for 2013 and 2016 storms also demonstrate that SNP‐GAN outperforms DCGAN‐PB in terms of recovering equatorial and low latitude TEC structures. Meanwhile, the end‐to‐end styled generator of SNP‐GAN saves time in the map completion step by avoiding iterative mapping used in DCGAN‐PB. Both deep learning methods not only preserve the large‐scale TEC structures well, but also reveal mesoscale (100–1,000 km) TEC structures that are missing in IGS‐TEC. This work represents an important progress for efficient and automatic TEC map completion with high accuracy.

Iterative mapping for high-precision calibration and displacement measurements

The precision of calibration severely affects the precision of subsequent measurements. In the calibration, the corresponding image and global target coordinates are two distinct input datasets. The image coordinates may be estimated precisely with the use of an optimal feature detection algorithm. However, the global coordinates are usually chosen as the given 3D manufacturing centers, and are different from the reconstructed three-dimensional (3D) optical centers. In this study, an iterative coordinate mapping (ICM) method is proposed to optimize the 3D optical centers for high-precision calibration and displacement measurements. Precise optical 3D coordinates for a stereo-vision system calibration scheme are generated based on the results of the left and right camera calibrations. Herein, the principles of single-camera calibration, ICM method, precise coordinate generation, and stereo-vision system calibration schemes are presented. Computer simulations were performed to verify the effectiveness of the correction of 3D coordinates. Experiments were conducted to analyze the stability of the calibrated parameters and the precision of the reconstructed 3D coordinates. The results show that the ICM is with higher precision.

Neural network gradient-index mapping

A universal method to design gradient-index (GRIN) optical elements is proposed here for a given desired light ray bundle. Fermat’s principle can be transformed into a spatial parametric ray equation where a spatial Cartesian coordinate is used as a parameter of the equation. The ray equation can thus be written in a time-independent form, which ensures that a refractive index distribution is in principle obtainable from a spatial light ray distribution. Based on the ray equation, an iterative GRIN mapping method using the neural network (NN) is then constructed to map a refractive index distribution that enables light rays to trace corresponding desired paths. Maxwell’s fisheye lens is used to demonstrate how well the GRIN mapping method works. The refractive index distribution is shown to be well reconstructed from only knowledge of the light ray paths.

Mitochondrial DNA sequencing of a wet-collection syntype demonstrates the importance of type material as genetic resource for lantern shark taxonomy (Chondrichthyes: Etmopteridae).

After initial detection of target archival DNA of a 116-year-old syntype specimen of the smooth lantern shark, Etmopterus pusillus, in a single-stranded DNA library, we shotgun-sequenced additional 9 million reads from this same DNA library. Sequencing reads were used for extracting mitochondrial sequence information for analyses of mitochondrial DNA characteristics and reconstruction of the mitochondrial genome. The archival DNA is highly fragmented. A total of 4599 mitochondrial reads were available for the genome reconstruction using an iterative mapping approach. The resulting genome sequence has 12 times coverage and a length of 16 741 bp. All 37 vertebrate mitochondrial loci plus the control region were identified and annotated. The mitochondrial NADH2 gene was subsequently used to place the syntype haplotype in a network comprising multiple E. pusillus samples from various distant localities as well as sequences from a morphological similar species, the shortfin smooth lantern shark Etmopterus joungi. Results confirm the almost global distribution of E. pusillus and suggest E. joungi to be a junior synonym of E. pusillus. As mitochondrial DNA often represents the only available reference information in non-model organisms, this study illustrates the importance of mitochondrial DNA from an aged, wet collection type specimen for taxonomy.

Predicting instabilities of a tunable ring laser with an iterative map model

Simple mathematical models have been unable to predict the conditions leading to instabilities in a tunable ring laser. Here we propose a nonlinear iterative map model for tunable ring lasers. Solving a reduced nonlinear Schrödinger equation for each component in the laser cavity, we obtain an algebraic map for each component. Iterating through the maps gives the total effect of one round trip. By neglecting the nonlinearity, we find a linearly chirped Gaussian to be the analytic fixed point solution, which we analyze asymptotically. We then numerically solve the full nonlinear model, allowing us to probe the underlying interplay of dispersion, modulation, and nonlinearity as the pulse evolves over hundreds of round trips of the cavity. In the nonlinear case, we find that the chirp saturates and the Fourier transform of the pulse becomes more rectangular in shape. Finally, for a nominal plane in the parameter space, we uncover a rich, sharp boundary separating the stable region and the unstable region where instabilities degrade the pulse into an unsustainable state.

Suppression of chaotic bursting synchronization in clustered scale-free networks by an external feedback signal.

Oscillatory activities in the brain, detected by electroencephalograms, have identified synchronization patterns. These synchronized activities in neurons are related to cognitive processes. Additionally, experimental research studies on neuronal rhythms have shown synchronous oscillations in brain disorders. Mathematical modeling of networks has been used to mimic these neuronal synchronizations. Actually, networks with scale-free properties were identified in some regions of the cortex. In this work, to investigate these brain synchronizations, we focus on neuronal synchronization in a network with coupled scale-free networks. The networks are connected according to a topological organization in the structural cortical regions of the human brain. The neuronal dynamic is given by the Rulkov model, which is a two-dimensional iterated map. The Rulkov neuron can generate quiescence, tonic spiking, and bursting. Depending on the parameters, we identify synchronous behavior among the neurons in the clustered networks. In this work, we aim to suppress the neuronal burst synchronization by the application of an external perturbation as a function of the mean-field of membrane potential. We found that the method we used to suppress synchronization presents better results when compared to the time-delayed feedback method when applied to the same model of the neuronal network.

Talking the same language on patient empowerment: Development and content validation of a taxonomy of self-management interventions for chronic conditions.

ContextThe literature on self‐management interventions (SMIs) is growing exponentially, but it is characterized by heterogeneous reporting that limits comparability across studies and interventions. Building an SMI taxonomy is the first step towards creating a common language for stakeholders to drive research in this area and promote patient self‐management and empowerment.ObjectiveTo develop and validate the content of a comprehensive taxonomy of SMIs for long‐term conditions that will help identify key characteristics and facilitate design, reporting and comparisons of SMIs.MethodsWe employed a mixed‐methods approach incorporating a literature review, an iterative consultation process and mapping of key domains, concepts and elements to develop an initial SMI taxonomy that was subsequently reviewed in a two‐round online Delphi survey with a purposive sample of international experts.ResultsThe final SMI taxonomy has 132 components classified into four domains: intervention characteristics, expected patient/caregiver self‐management behaviours, outcomes for measuring SMIs and target population characteristics. The two‐round Delphi exercise involving 27 international experts demonstrated overall high agreement with the proposed items, with a mean score (on a scale of 1‐9) per component of 8.0 (range 6.1‐8.8) in round 1 and 8.1 (range 7.0‐8.9) in round 2.ConclusionsThe SMI taxonomy contributes to building a common framework for the patient self‐management field and can help implement and improve patient empowerment and facilitate comparative effectiveness research of SMIs.Patient or public contribution.Patients’ representatives contributed as experts in the Delphi process and as partners of the consortium.

A secure image cryptosystem via multiple chaotic maps

This work focused on introducing a new two-dimensional (2D) chaotic system. It is combined of some existing maps, the logistic, iterative chaotic map with infinite collapse, and Henon maps; we called it 2D-LCHM. The assessment of the actual performance of 2D-LCHM presents its sensitivity to a tiny change in the initial condition. Besides, its dynamics behavior is very complicated. It also has hyperchaotic properties and good ergodicity. The proposed system is occupied with designing a new image encryption system. Changing the image pixels’ locations is the primary step in the encryption process. The original image is splitting into four blocks to create four different keys based on 2D-LCHM; this reduces the computation time and increases the complexity. To obtain the encryption image, we have to repeat the partitioning process several times for each block. The encryption image’s efficiency is shown through some performance analysis such as; image histogram, the number of pixels changes rate (NPCR), the unified average changing intensity (UACI), pixels correlation, and entropy. The proposed system is compared with some efficient encryption algorithms in terms of chaocity attributes and image performance; the analysis result showed consistent improvement.

Complete mitochondrial genome of the giant root-rat (Tachyoryctes macrocephalus)

The endangered giant root-rat (Tachyoryctes macrocephalus, also known as giant mole rat) is a fossorial rodent endemic to the afro-alpine grasslands of the Bale Mountains in Ethiopia. The species is an important ecosystem engineer with the majority of the global population found within 1000 km2. Here, we present the first complete mitochondrial genome of the giant root-rat and the genus Tachyoryctes, recovered using shotgun sequencing and iterative mapping. A phylogenetic analysis including 15 other representatives of the family Spalacidae placed Tachyoryctes as sister genus to Rhizomys with high support. This position is in accordance with a recent study revealing the topology of the Spalacidae family. The full mitochondrial genome of the giant root-rat presents an important resource for further population genetic studies.

An efficient image encryption scheme based on double affine substitution box and chaotic system

Privacy of information is one of the most important and unavoidable issues in our digitally advance era. A huge amount of information transmitted over different servers and networking protocols. The sphere of digitally advanced world is tied with information in different forms of facilitations, which includes online banking systems, ecommerce and many more. Providing the ease of access, anything online makes our confidential information open to different threats. Therefore, to enjoy ease of access and at the same time secure our digital information from theft, we need a robust information security system. In this article, we have designed a novel and an efficient security system, which provides secrecy to our digital information. The designed encryption scheme is fundamentally a combination of chaos and nonlinear confusion components. We have developed a new mechanism of adding confusion, namely S8 permutation of double affine transformation to construct 40320 new substitution boxes (S-boxes) having nonlinearity 112 from a single S-box. Moreover, nonlinear Lorenz dynamical system is utilized to select any three S-boxes from 40320 newly generated nonlinear components. To add diffusion in our proposed algorithm, we have utilized Chirikov discrete iterative map. The excellence of an offered digital image encryption has been examined and evaluated with standard benchmarks. The simulation results reveal that the quality of the image encryption passes all these tests and is comparable to current benchmarks.

ATA2021ATA2021 Annual Conference & Expo(Virtual*)Tuesdays and Thursdays in June 2021(ABSTRACTS)

ATA2021ATA2021 Annual Conference & Expo(Virtual*)Tuesdays and Thursdays in June 2021(ABSTRACTS)

Stochastic Gene Expression Revisited.

We investigate the model of gene expression in the form of Iterated Function System (IFS), where the probability of choice of any iterated map depends on the state of the phase space. Random jump times of the process mark activation periods of the gene when pre-mRNA molecules are produced before mRNA and protein processing phases occur. The main idea is inspired by the continuous-time piecewise deterministic Markov process describing stochastic gene expression. We show that for our system there exists a unique invariant limit measure. We provide full probabilistic description of the process with a comparison of our results to those obtained for the model with continuous time.

Securing multiple-single-channel color image using unequal spectrum decomposition and 2D-SLIM biometric keys

A novel multiple-single-channel color image cryptosystem based on unequal spectrum decomposition (USD) and 2D sine improved 1ogistic iterative chaotic map with infinite collapse modulation map (2D-SLIM) is proposed. In this method, R, G, and B channels of each color image of an authorized user is fused with corresponding LL sub-band of a gray-scale carrier image by inverse discrete wavelet transform (DWT) to obtain single-channel and watermarked image. The individual biometric key of an authorized user is produced by his/her phase-encoded irisprint and then modulated by chaotic random phase mask (CRPM). So the parameters of CRPM are used as decryption keys with the uniqueness of the irisprint. The watermarked image as input image is normalized, multiplied with biometric key, and then Fresnel transformed. The Fresnel spectrum is divided into two complex-value masks by using unequal spectrum decomposition in which one phase mask is exploited as decryption key and other as ciphertext. The final ciphertext is obtained by adding individual ciphertexts. The encryption and decryption process can be realized with a hybrid optoelectronic system. Numerical simulations have been performed to verify the validity and feasibility of the proposed system.

Inversion of Phytoplankton Pigment Vertical Profiles from Satellite Data Using Machine Learning

Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribution of phytoplankton pigments from surface satellite observations, allowing their global estimation with a high spatial and temporal resolution. After imputing missing values through iterative completion Self-Organizing Maps, smoothing and reducing the vertical distributions through principal component analysis, we used a Self-Organizing Map to cluster the reduced profiles with satellite observations. These referent vector clusters were then used to invert the vertical profiles of phytoplankton pigments. The methodology was trained and validated on the MAREDAT dataset and tested on the Tara Oceans dataset. The different regression coefficients R2 between observed and estimated vertical profiles of pigment concentration are, on average, greater than 0.7. We could expect to monitor the vertical distribution of phytoplankton types in the global ocean.