Complex Mapping Research Articles

Surrogate modeling of pantograph-catenary system interactions

The smooth interaction between the pantograph and the catenary is crucial for the operational safety of railway vehicles. Coupled dynamic models of the pantograph–catenary system (PCS) constructed based on physical principles are important tools for analyzing their interactions; however, these models rely on accurate system parameters (such as stiffness, damping, and mass). Under actual operating conditions, the system parameters of the PCS exhibit time-varying characteristics and are difficult to measure, making it challenging for dynamic models to accurately represent the system’s behavior. Data-driven intelligent algorithms, with powerful feature extraction and nonlinear fitting capabilities, provide new approaches for solving system response prediction and state identification problems of the PCS. However, excessive reliance on large amounts of data for training may lead to poor generalization ability and pose challenges to model robustness, such as sensitivity to input noise or outliers. To address these issues, this paper proposes a surrogate model for the interaction of the PCS by integrating physical information with deep neural networks. The model introduces a novel neural operator that combines Transformers and convolutions (Convs), capable of capturing complex mapping relationships among various parameters within the dynamic model of the PCS in the frequency domain. A residual network incorporating physical information is designed to simulate the intricate correlations among system parameters. Additionally, a dynamic weighting balance algorithm is proposed to adjust the losses of different physical equations dynamically, ensuring the balance of physical information during training. The proposed model effectively performs response prediction and state identification of the PCS. It demonstrates excellent performance on both simulation and real-world data, providing new insights and methodologies for studying PCS interactions.

DeepUrbanMapper - Satellite Image to Google Maps Translation

Abstract: The research presents DeepUrbanMapper, an innovative framework developed for the translation of satellite imagery into Google Maps-style renderings utilizing advanced image-to-image translation techniques. It is based on Generative Adversarial Networks (GANs), widely known in machine learning for their ability to yield high-quality images. To train it, the process involves a carefully curated and labeled dataset that consists of paired satellite and map images, enabling DeepUrbanMapper to learn the complex mappings between these two distinct visual domains. The GAN architecture used in DeepUrbanMapper is carefully designed and optimized to preserve spatial coherence and improve visual quality of the translated image. This helps in producing output maps that are close to the ground truth in both visual and geographical aspects. The proposed method includes few novel strategies to stabilize the GAN training and to overcome the notorious mode collapse problem and ensure a consistent output. DeepUrbanMapper has been extensively evaluated quantitatively and qualitatively for its performance. The evaluation shows that the new framework significantly outperforms the existing methods in terms of visual realism of the translated image and its ability to retain detailed features of the input satellite image which opens up many possibilities urban planning, car navigation systems, GIS.

Using land surface phenology and information theory to assess and map complex landscape dynamics

Abstract Context Characterizing landscape ecological complexity and change requires integrated description of spatial and temporal landscape organization and dynamics, as suggested by the shifting mosaic concept. Remotely sensed land surface phenology allows the detection of even small differences among landscape patches and through time, allowing for the analysis of landscapes as shifting mosaics. Objectives We sought to quantify aspects of the complex landscape behaviors that are implied by spatiotemporal variation in land surface phenology. We adapted an information-theoretic (IT) framework from ecosystem ecology to capture landscape-level spatiotemporal complexity and organization and map these properties across large areas. Methods Phenology data were derived from remotely sensed, pixel-level time series of a vegetation greenness index, across a large portion of North America. We summarized multi-year, multi-pixel dynamics in transition matrices, calculated IT metrics from the matrices, and used matrix projection to quantify disequilibrium dynamics and long-term trajectories of the metrics. Results Mapping the IT metrics and their disequilibria revealed gradients in the spatiotemporal complexity and organization of multi-year land surface phenology dynamics at continental to local scales. These gradients suggest influences of biophysical and biogeographic setting, ecological development and disturbances, land use, and other drivers of landscape ecological dynamics. The spatiotemporal IT metrics were influenced by both year-to-year dynamics and spatial landscape heterogeneity, but correlations with spatial and temporal complexity measures varied among the IT metrics. Landscapes showing the strongest disequilibrium dynamics were mostly in the western part of the continent and appeared to be associated with large-scale disturbances including severe fire, forest pathogens, climate variability, and land use change—important subjects for further study. Conclusions This approach reveals novel features of the shifting landscape mosaic, with implications for understanding landscape resilience and sustainability. Resulting spatial data products describing long-term landscape dynamics have potential applications in broad-scale ecological modeling, monitoring, assessment, and prediction.

Large Scale Ultrafast Manufacturing of Wireless Soft Bioelectronics Enabled by Autonomous Robot Arm Printing Assisted by a Computer Vision-Enabled Guidance System for Personalized Wound Healing.

A Customized wound patch for Advanced tissue Regeneration with Electric field (CARE), featuring an autonomous robot arm printing system guided by a computer vision-enabled guidance system for fast image recognition is introduced. CARE addresses the growing demand for flexible, stretchable, and wireless adhesive bioelectronics tailored for electrotherapy, which is suitable for rapid adaptation to individual patients and practical implementation in a comfortable design. The visual guidance system integrating a 6-axis robot arm enables scans from multiple angles to provide a 3D map of complex and curved wounds. The size of electrodes and the geometries of power-receiving coil are essential components of the CARE and are determined by a MATLAB simulation, ensuring efficient wireless power transfer. Three heterogeneous inks possessing different rheological behaviors can be extruded and printed sequentially on the flexible substrates, supporting fast manufacturing of large customized bioelectronic patches. CARE can stimulate wounds up to 10 mm in depth with an electric field strength of 88.8 mVmm-1. In vitro studies reveal the ability to accelerate cell migration by a factor of 1.6 and 1.9 for human dermal fibroblasts and human umbilical vein endothelial cells, respectively. This study highlights the potential of CARE as a clinical wound therapy method to accelerate healing.

Neural Methods for Amortized Inference

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.

Characterization of heat-treated bearing rings via measurement of electromagnetic properties for pulsed eddy current evaluation

Nondestructive evaluation of heat-treated bearing rings is a critical technique for quality control in industries. However, there are few reports addressing the complex mapping between hardness and electromagnetic properties due to the intricate changes in the microstructure. This paper proposes a dual-method approach, combining magnetic saturation eddy current techniques and Barkhausen noise reconstruction hysteresis loop techniques, to independently establish the relationship between hardness and electromagnetic properties. The results show that the electrical properties of unqualified specimens are significantly higher than those of other specimens, with qualified specimens have slightly higher properties than untreated ones. Additionally, an inverse relationship between hardness and magnetic properties is observed. Based on the obtained electromagnetic parameters, a pulsed eddy current hardness detection simulation model is established, which has the potential to improve purely data-driven methods for hardness detection in deep learning.

Effects of environmental diversity on exploration and learning: The case of bilingualism.

Bilingual environments provide a commonplace example of increased complexity and uncertainty. Learning multiple languages entails mastery of a larger and more variable range of sounds, words, syntactic structures, pragmatic conventions, and more complex mapping of linguistic information to objects in the world. Recent research suggests that bilingual learners demonstrate fundamental variation in how they explore and learn from their environment, which may derive from this increased complexity. In particular, the increased complexity and variability of bilingual environments may broaden the focus of learners' attention, laying a different attentional foundation for learning. In this review, we introduce a novel framework, with accompanying empirical evidence, for understanding how early learners may adapt to a more complex environment, drawing on bilingualism as an example. Three adaptations, each relevant to the demands of abstracting structure from a complex environment, are introduced. Each adaptation is discussed in the context of empirical evidence attesting to shifts in basic psychological processes in bilingual learners. This evidence converges on the notion that bilingual learners may explore their environment more broadly. Downstream consequences of broader sampling for perception and learning are discussed. Finally, recommendations for future research to expand the scientific narrative on the impact of diverse environments on learning are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

GPCR signalling: Yet another variant route in a highly complex road map

GPCR signalling: Yet another variant route in a highly complex road map

Hu.MAP3.0: Atlas of human protein complexes by integration of > 25,000 proteomic experiments.

Macromolecular protein complexes carry out most functions in the cell including essential functions required for cell survival. Unfortunately, we lack the subunit composition for all human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify > 15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing ~75% of human proteins into their physical contexts. We globally characterize our complexes using protein co-variation data (ProteomeHD.2) and identify co-varying complexes suggesting common functional associations. Our map also generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 511 mutually exclusive protein pairs in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal (https://www.ebi.ac.uk/complexportal/home) as well as provide complexes through our hu.MAP3.0 web interface (https://humap3.proteincomplexes.org/). We expect our resource to be highly impactful to the broader research community.

Surrogate-model-based low-carbon optimization method for tolerance allocation of mechanical components under bidirectional constraints

The low-carbon optimization of tolerance allocation involves reducing production costs and carbon emissions, while ensuring dimensional precision, making it a crucial pathway to achieving low-carbon manufacturing. However, the complex mapping mechanism between tolerance, cost, and carbon emissions makes it challenging to construct effective mechanistic models. This complexity arises from both the forward constraints of multiple-coupled design features of components and backward constraints on the differentiated processing conditions of an enterprise. Therefore, a low-carbon optimization method for tolerance allocation based on surrogate models is proposed. Firstly, the RReliefF algorithm is utilized to analyze the influence of design features and enterprise processing condition factors on the low-carbon performance of tolerance allocation, identifying key influencing factors. Secondly, a convolutional neural network (CNN)-based surrogate model is established to predict the low-carbon performance of tolerance allocation. Specifically, datasets of tolerance, manufacturing costs, and carbon emissions under different design feature constraints are constructed. To improve the training efficiency and accuracy of the prediction model, the heterogeneous data of design features are processed using the one-hot encoding technique. Subsequently, the mapping relationship between tolerance, cost, and carbon emissions is established based on the CNN. Finally, with the aim of minimizing costs and carbon emissions, a low-carbon optimization method for tolerance allocation is established based on the surrogate model and the product size chain. The convolutional neural network-Harris Hawk optimization algorithm (CNN-HHO) is utilized to generate tolerance allocation schemes that minimize both cost and carbon emissions while satisfying dimensional precision. Through a series of multi-peak test functions and practical case studies of the overrunning clutch, the results indicate that the proposed CNN-HHO algorithm demonstrates exceptional accuracy and stability in the validation on the test functions. Furthermore, the optimized overrunning clutch solution achieves approximately 2.61% cost reduction and 2.63% decrease in carbon emissions, providing further support for the effectiveness of the proposed method.

ELECTRICAL CARDIAC REMODELING IN MEN WITH ARTERIAL HYPERTENSION AFFECTED BY PSORIASIS VULGARIS

Psoriasis and arterial hypertension are often observed in men of working age. Biologically active molecules such as insulin-like growth factor, vascular endothelial growth factor (VEGF), etc. play a significant role in the pathogenesis of these diseases. These factors contribute to cardiac remodeling, which is one of the arrhythmogenic substrates. The aim of the study was a comprehensive assessment of electrical cardiac parameters in male patients suffering from arterial hypertension affected by psoriasis vulgaris. Materials and Methods. The study was conducted in 2021–2023. It involved 110 male patients undergoing outpatient treatment for stage 2 arterial hypertension. Their average age was 52.4±6.9 years. The patients were divided into 2 groups: Group 1 (comparison) – 50 patients with arterial hypertension; Group 2 (control) – 60 patients with hypertension and psoriasis vulgaris in the progressive moderate severity stage (PASI index between 10 and 20). Results. In males with arterial hypertension affected by psoriasis vulgaris, late ventricular potentials were statistically significantly more common (33 %) compared to those with arterial hypertension without psoriasis (16 %) (χ²=4.32; p=0.048). In patients with both arterial hypertension and psoriasis, a greater number of local peaks in the ventricular complex along all Frank orthogonal leads were registered in spectral-temporal mapping of the QRS complex. Conclusion. The concomitant psoriasis vulgaris in males with arterial hypertension negatively effects the parameters of cardiac electrical remodeling.

An efficient feature reuse distillation network for lightweight image super-resolution

In recent research, single-image super-resolution (SISR) using deep Convolutional Neural Networks (CNN) has seen significant advancements. While previous methods excelled at learning complex mappings between low-resolution (LR) and high-resolution (HR) images, they often required substantial computational and memory resources. We propose the Efficient Feature Reuse Distillation Network (EFRDN) to alleviate these challenges. EFRDN primarily comprises Asymmetric Convolutional Distillation Modules (ACDM), incorporating the Multiple Self-Calibrating Convolution (MSCC) units for spatial and channel feature extraction. It includes an Asymmetric Convolution Residual Block (ACRB) to enhance the skeleton information of the square convolution kernel and a Feature Fusion Lattice Block (FFLB) to convert low-order input signals into higher-order representations. Introducing a Transformer module for global features, we enhance feature reuse and gradient flow, improving model performance and efficiency. Extensive experimental results demonstrate that EFRDN outperforms existing methods in performance while conserving computing and memory resources.

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment of actual forest structural complexity, we derive a near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications. The GEDI Waveform Structural Complexity Index data product, derived from our analyses, provides researchers and conservationists with a single, easily interpretable metric by combining various aspects of canopy structure.

Photonic modes prediction via multi-modal diffusion model

Abstract The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell's equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the Multi-Modal Diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces Multi-Modal deep learning to construct complex mapping between structural information and light field as high-dimensional vectors, and generates light field images based on this mapping.

Spatiotemporal distribution and mechanism of Mesozoic magmatism in the Taiwan Strait and its adjacent areas: insights from seismic, geochemical, and geochronological data

ABSTRACT This paper presents a complete map of the Mesozoic volcanic-plutonic complexes in the Taiwan Strait and its adjacent areas in terms of seismic profiles, ages, geochemistry, isotopic systematics, and published data. Two periods of igneous activity in the Taiwan Strait can be distinguished by seismic profiles: Jurassic (200.4–148.2 Ma); and Latest Jurassic/Cretaceous to Maastrichtian (144–66 Ma), of which the Cretaceous is the best preserved and could possibly be the most widespread. Mesozoic magmatic activity with the NE-striking distribution was migrating southeastward (oceanward). LA-MC-ICPMS zircon U – Pb dating yields the age data of 109.5 ± 0.4 Ma for PTDH1 granodiorite and 92.6 ± 0.4 Ma for PTDH4 rhyolite in the Taiwan Strait, respectively. The 581.66-m-thick Upper Jurassic volcanic rocks of the Chuan2 well are the thickest Mesozoic igneous rocks in the Taiwan Strait. The petrogenetic discrimination diagram of the Cretaceous volcanic-plutonic complexes along the coast of South China and the Taiwan Strait generally is belonging to volcanic arc or intraplate type calc-alkaline rock series. The high potassium calc-alkaline rock series of the Cretaceous volcanic-plutonic complexes that is most widely distributed in the coastal areas of South China and the Taiwan Strait. They are controlled by the subduction of the Palaeo-Pacific domain.

Bifurcation and Stability of a Haptotaxis Mathematical Model for Complex MAP

This paper studies the bifurcation and stability of a parabolic–ordinary-parabolic system to a model with three reactive species: molecular Anchors [Formula: see text], Nanoparticulates [Formula: see text] and Matrix constituents [Formula: see text] ([Formula: see text]). Global asymptotic stability is studied for both the ODE system and the reaction–diffusion system. The results show that the positive equilibrium is globally asymptotically stable in both the ODE and the corresponding reaction–diffusion systems. Furthermore, by selecting the haptotaxis coefficient as a bifurcation factor, we analyze the existence, direction, and stability of the Hopf bifurcation. The results also reveal that haptotaxis is crucial in determining the stability and bifurcation behavior of the model, suggesting that it has a destabilizing effect and can undergo Hopf bifurcation. Moreover, numerical simulations are presented to verify and illustrate the theoretical results.

Accurate reconstruction of terahertz spectral images with enhanced spatial resolution via complex mapping.

The wavelength of terahertz waves varies by two orders of magnitude. Long-wavelength terahertz images suffer from low spatial resolution due to the millimeter-level diffraction limit. Conventional resolution-enhancing methods are generally limited by sample types and field of view. To overcome these challenges, we propose a resolution-enhancing algorithm for terahertz spectral imaging. This algorithm leverages the advantage of ultra-broadband complex spectral imaging and determines the mapping relationship between the short- and long-wavelength images through clustering and genetic algorithm optimization. The numerical modality supports nearly all optical configurations and sample types. Transmission and reflection measurements validate the superior performance, demonstrating up to 6-fold resolution improvement. Moreover, the complex spectra can be accurately recovered, enabling precise extraction of broadband complex permittivity and subsequent analysis for sub-diffraction-limit objects.

Structures of influenza A and B replication complexes give insight into avian to human host adaptation and reveal a role of ANP32 as an electrostatic chaperone for the apo-polymerase

Replication of influenza viral RNA depends on at least two viral polymerases, a parental replicase and an encapsidase, and cellular factor ANP32. ANP32 comprises an LRR domain and a long C-terminal low complexity acidic region (LCAR). Here we present evidence suggesting that ANP32 is recruited to the replication complex as an electrostatic chaperone that stabilises the encapsidase moiety within apo-polymerase symmetric dimers that are distinct for influenza A and B polymerases. The ANP32 bound encapsidase, then forms the asymmetric replication complex with the replicase, which is embedded in a parental ribonucleoprotein particle (RNP). Cryo-EM structures reveal the architecture of the influenza A and B replication complexes and the likely trajectory of the nascent RNA product into the encapsidase. The cryo-EM map of the FluB replication complex shows extra density attributable to the ANP32 LCAR wrapping around and stabilising the apo-encapsidase conformation. These structures give new insight into the various mutations that adapt avian strain polymerases to use the distinct ANP32 in mammalian cells.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

On the sequential topological complexity of group homomorphisms

We define and develop a homotopy invariant notion for the sequential topological complexity of a map f:X→Y, denoted TCr(f), that interacts with TCr(X) and TCr(Y) in the same way Jamie Scott's topological complexity map TC(f) interacts with TC(X) and TC(Y). Furthermore, we apply TCr(f) to studying group homomorphisms ϕ:Γ→Λ.In addition, we give the characterization of cohomological dimension of group homomorphisms.