Space Reconstruction Research Articles

Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean.

Defend and remould–residents’ place identity construction in traditional villages in the rural tourism context: a case study of Cuandixia village, Beijing

This study aims to elucidate the process and internal mechanism of place identity construction in traditional villages under the impact of tourism by taking Cuandixia village as a case. The research methods comprise participatory observation and in-depth interviews with the residents. The main results are as follows: the impact of tourism on traditional villages is mainly reflected in space reconstruction, livelihood change, social relations restructuring and culture change; under the impact of tourism, the representation of residents’ identity construction shows complexity, with positive and negative effects; and the place identity construction of residents affects their perception of and attitudes toward tourism. Moreover, self-esteem and self-efficacy principles play a key role in their perception of tourism. This study provides some reference for further investigation of the tourism development model and the mental mechanism of residents in traditional villages.

Shape sensing using neural network of radial basis function based on Fiber Bragg Grating

AbstractShape sensing using Fiber Bragg Grating can be applied in many fields such as medical and robotics, and the accuracy of its reconstruction is the key to current research. At present, the application research in two‐dimensional planes has reached a high accuracy, how to improve the accuracy of three‐dimensional space reconstruction is the current challenge. An interpolation method using Radial Basis Function neural network is proposed for curve reconstruction in three‐dimensional space, which is compared and analyzed with the existing commonly used cubic‐spline interpolation method. Through theoretical validation and experimental verification, both the tip error and the average distance error of the sensor are reduced.

Modeling COVID-19 Using a Modified SVIR Compartmental Model and LSTM-Estimated Parameters

This article presents a modified version of the SVIR compartmental model for predicting the evolution of the COVID-19 pandemic, which incorporates vaccination and a saturated incidence rate, as well as piece-wise time-dependent parameters that enable self-regulation based on the epidemic trend. We have established the positivity of the ODE version of the model and explored its local stability. Artificial neural networks are used to estimate time-dependent parameters. Numerical simulations are conducted using a fourth-order Runge–Kutta numerical scheme, and the results are compared and validated against actual data from the Autonomous Communities of Spain. The modified model also includes explicit parameters to examine potential future scenarios. In addition, the modified SVIR model is transformed into a system of one-dimensional PDEs with diffusive terms, and solved using a finite volume framework with fifth-order WENO reconstruction in space and an RK3-TVD scheme for time integration. Overall, this work demonstrates the effectiveness of the modified SVIR model and its potential for improving our understanding of the COVID-19 pandemic and supporting decision-making in public health.

A proton-recoil track imaging system for fast neutrons: the RIPTIDE detector

Fast neutron detection is often based on the neutron-proton elastic scattering reaction: the ionization caused by recoil protons in a hydrogenous material constitutes the basic information for the design and development of a class of neutron detectors. Although experimental techniques have continuously improved, proton-recoil track imaging remains still at the frontier of n-detection systems, due to the high photon sensitivity required. Several state-of-the-art approaches for neutron tracking by using n-p single and double scattering — referred to as Recoil Proton Track Imaging (RPTI) — can be found in the literature. So far, they have showed limits in terms of detection efficiency, complexity, cost, and implementation. In order to address some of these deficiencies, we propose the design of RIPTIDE, a novel recoil-proton track imaging detector in which the light output produced by a fast scintillator is used to perform a complete reconstruction in space and time of the interaction events. The proposed idea is viable thanks to the dramatic advances in low noise and single photon counting achieved in the last decade by new scientific CMOS cameras as well as pixel sensors, like Timepix or MIMOSIS. In this contribution, we report the advances on the RIPTIDE concept: Geant4 Monte Carlo simulations, light collection tests as well as state-of-the-art approach to image readout, processing and fast analysis.

The Introduction and Application of Immersive Experience by Museum Exhibition in Space Reconstruction

Based on the necessity of museum exhibition with introduction of immersive experience in spatial reconstruction, this paper explores the ideas and application methods of museum exhibition with introduction of immersive experience in spatial reconstruction on the basis of analyzing the value, significance and demand of museum exhibition introducing immersive experience in spatial reconstruction.

Optimization Approaches of Multi-Dimensional Environments in Rural Space Reproduction Driven by Tourism

Rural tourism is an important driving force for the transformation of rural society in the process of urbanization. However, besides promoting rural economy, it also interrupts the development of the rural living, human, manage and economic multi-dimensional environments. Space reconstruction in rural settlements is an important means to optimize multi-dimensional environments and revitalize rural tourism. Focusing on two types of rural settlements, namely the “relocation village” and the “sightseeing and settlement symbiosis village”, this study has revealed the affecting mechanism of space reconstruction in rural tourist locations on the local multi-dimensional environment by establishing a multi-dimensional environment evaluation system through an analytic, hierarchical process. Subsequently, based on the theoretical perspective of the reproduction of the space and the paradigm of the three-fold model, a comprehensive optimization path of rural multi-dimensional environments has been proposed. The results show that in the space reconstruction process driven by tourism: (1) the multi-dimensional environments of the “sightseeing and settlement symbiosis village” generally develop better than the “relocation village”; (2) the ecological environment is the direct benefit while the management environment and economic environment are the bottlenecks; (3) the “sightseeing and settlement symbiosis village” is more appealing to tourists who are interested in rural tourism since it keeps more rustic characteristics than the “relocation village”; (4) in order to optimize the multi-dimensional environments, “relocation village” should pay more attention to the space needs of residents, improve space utilization rates, and increase the residents’ sense of belonging; (5) “sightseeing and settlement symbiosis village” needs to resolve the contradiction between the tourism and living demands, improve the reproduction process driven by diversified entities, and reconstruct the neighborhood network under rural tourism.

A numerical approach to a 2D porous-medium mathematical model: Application to an atherosclerosis problem

This work deals with the study and implementation of a Local Space-Time DG ADER approach, in the finite volume framework with Weighted Essentially Non-Oscillatory (WENO) reconstruction in space, in the context of 2D porous media problems. The particular application performed concerns a biomedical problem dealing with the first stages of atherosclerosis disease, where the artery is considered as a porous medium, although the methodology described can be applied to other situations. The mathematical model on which this research is based is given by a system of two-dimensional nonlinear reaction-diffusion equations with a nonlinear source term in one of the equations, which is a variant of the model proposed originally in N. El Khatib, S. Genieys & V. Volpert (2007) Math. Model. Nat. Phenom., vol 2(2), pp. 126–141 [1]. The 2D model considered in this work has two main features: (i) the artery is taken as a porous medium and (ii) a nonlinear non-homogeneous Neumann boundary condition is incorporated, aimed to account for the recruitment of immune cells through the upper boundary, as a response to the production of cytokines. Certain theoretical properties of the stationary solutions and the evolution solution are stated and proved. Some of them are also verified with the numerical simulation results.

Wind velocity field estimation from aircraft derived data using Gaussian process regression.

Wind velocity field knowledge is crucial for the future air traffic management paradigm and is key in many applications, such as aircraft performance studies. This paper addresses the problem of spatio-temporal windc velocity field estimation. The north and east wind components within a given air space are estimated as a function of time. Both wind velocity field reconstruction in space for a past or present time instant and short-term prediction are performed. Wind data are obtained indirectly from the states of the aircraft broadcast by the Mode-S and ADS-B aircraft surveillance systems. The Gaussian process regression method, which is a flexible and universal estimator, is employed to solve both problems. Under general conditions, the method is statistically consistent, meaning that the method converges to the ground truth when increasingly more data are available, which is especially interesting, since aircraft data availability is expected to grow in the future through the deployment of the European System-Wide Information Management. Besides estimation, the Gaussian process regression method provides the probability distribution of any particular estimate, allowing confidence intervals to be computed. Moreover, the spatial modelling is performed using raw data without relying on grids and estimation can be performed at any spatio-temporal location. Furthermore, since the training phase of the method described in this paper is fast, requiring less than 5 minutes on a standard desktop computer, it can be used online to continuously track the state of the wind velocity field, thus allowing for data assimilation. In the case study presented in this paper, the Gaussian process regression method is tested on different days with different wind intensities. The available data set is split into several training and testing data sets, which are used to check the consistency of the results of wind velocity field reconstruction and prediction. Finally, the Gaussian process regression method is validated using the European Centre for Medium-Range Weather Forecasts ERA5 meteorological reanalysis data. The obtained results show that Gaussian process regression can be used to reliably estimate the wind velocity field from aircraft derived data.

The Research on the Spatial Governance Tools and Mechanism of Megacity Suburbs Based on Spatial Evolution: A Case of Beijing

The research on the spatial governance of the suburbs of megacities is of great significance for coordinating the spatial relationship between the central urban area and the suburbs of megacities, and implementing the regional functional layout of megacities. It is helpful to formulate scientific spatial governance strategies, and coordinate suburban space and central urban areas to achieve coordinated and sustainable development. This paper uses spatial form indicators to study suburban space governance from the spatial evolution characteristics of suburban construction land, constructs the relationship between different spatial evolution characteristics and the utility of spatial governance tools in the suburbs of megacities, and discusses the mechanism of suburban spatial governance tools. The study found that the spatial governance of megacities runs through the whole process of spatial evolution. Together with the three stages of space evolution, “space shaping, space restoration, space reconstruction”, we present three spatial governance mechanism types: “extensive development with positive guidance as the theme”, “exploratory adjustment with transformation practice as the theme”, “fine governance with management and control intervention as the theme”. In addition, the study also found that direct and indirect suburban space governance tools have different action paths and usage characteristics, and there is a synergistic mechanism between the two types of tools.

“RIPTIDE” — an innovative recoil-proton track imaging detector

Neutron detectors perform key tasks in many research fields as nuclear, particle and astroparticle physics as well as neutron dosimetry, radiotherapy, and radiation protection. Neutron detectors exhibiting tracking capability are still missing, although several approaches to neutron momentum reconstruction have been proposed. In this context, we aim at developing a novel RecoIl-Proton Track Imaging DEtection system “RIPTIDE”, in which the light output of a fast scintillation signal is used to perform a complete reconstruction in space and time of the neutron-proton elastic scattering. The 3D track reconstruction is going to be implemented by state-of-the-art high-sensitivity imaging detector (CMOS, MCP-Timepix). Preliminary Geant4 simulations of the proposed set-up show up a good detection efficiency in a compact active volume. The envisaged electronic readout can be easily adapted according to a specific application (event-by-event mode or integration mode). The system can be rescaled by increasing the detection volume or by combining several detection modules. Further developments of the basic detection technique can be adapted for fast charged particle detection tracking as well.

Grain Production Space Reconstruction and Its Influencing Factors in the Loess Plateau.

Grain production space, ecological service space and urban–rural development space are the classifications of land systems from the perspective of the dominant function of the land system. Grain production space reconstruction concentrates on the principal contradictions of land system changes, and is the key to exploring the transformation of land system. Therefore, the pathways, process and influencing factors of grain production space reconstruction in the Loess Plateau of Chian from 1980 to 2018 was explored from three dimensions of quantity–quality–spatial pattern in this study. Results showed that the quantity of grain production space showed a slight downward trend with a net decrease of 9156 km2 between 1980 and 2018, but its total quality showed a fluctuating growth trend under rain-fed conditions. Due to the intensification of human activities, grain production space was gradually fragmented, and the distribution tended to be decentralized, and the shape gradually became regular. Meanwhile, both the quantity and quality gravity center of grain production space moved to the northwest by 8.32 km and 86.03 km, respectively. The reconstruction of grain production space in the Loess Plateau was mainly realized through four pathways: Grain for Green, Urban Expansion, Deforestation and Reclamation, and Land Consolidation. The grain production space was mainly reconstructed through the pathway of Grain for Green after 2000. The four reconstruction pathways were the result of a combination of natural environment and socio-economic factors, but influencing factors had different strengths and directions for each reconstruction pathway. From the perspective of social economy–land use–ecological environment coupling, in order to maintain the sustainable development of the land systems, it is necessary to reduce the trade-offs of the functions of land systems as much as possible and strive to coordinate the relationship among grain production, ecological protection and high-quality development.

A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

Cardiovascular disease is the number one cause of death in humans. Therefore, cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug development. Recently, cell-based techniques developed for arrhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large datasets. We carried out the first report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm (MNDA) analysis for drug-induced arrhythmia recognition and classification. The biosensing system cultured cardiomyocytes as physiologically relevant models, used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes, and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating recording. The best performing MNDA parameter, approximate entropy, enabled the system to recognize the appearance of sertindole- and norepinephrine-induced arrhythmia in the recording. The MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of arrhythmia. This new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

A Study on Reconstruction of Movement and Space in Bashō's Haiku: Movement and Space on Horse

A Study on Reconstruction of Movement and Space in Bashō's Haiku: Movement and Space on Horse

Technological Implication of the Digital Twin Approach on the Intelligent Education System

The new area of education has progressively become where virtual reality interacts and integrates. The influence of credit management in the learning process is improved by digital twin technology. The next study paradigms of learning space will be the reconstruction of smart learning spaces based on digital twins. Hence, this paper, Digital Twin Approach (DTA), has been proposed to reconstruct the learning area and encourage the education revolution and a new learning community. In real-time manual interaction, the simulated twin will imitate diverse scenarios and create situations through AI decision-making and automatic twin implementation. The use of digital twin technology offers a new technology integration and development path to smart-university management in the investigation of the construction of smart university facilities. Thus, the experimental results show that the DTA technology can motivate students to study and boost learning when effectively utilized.

Tenure-Based Housing Spatial Patterns and Residential Segregation in Guangzhou under the Background of Housing Market Reform

China’s urban housing distribution system has been transformed from a redistribution system to a market-oriented distribution system, which has profoundly affected the ways and opportunities for urban residents to obtain housing resources and has triggered a large-scale reconstruction of urban residential social space. Based on the national 5th and 6th census data of Guangzhou, this paper analyzes the spatial patterns of housing tenure and tenure-based residential segregation in 2000 and 2010 with the research aim of analyzing the internal logic of urban housing distribution and residential segregation in urban China using Guangzhou as an example. The study finds that the home ownership rate in Guangzhou dropped from 62.31% in 2000 to 49.72% in 2010, with the percentage of social housing particularly low. The index of evenness and concentration is used to analyze tenure-based residential segregation. The results show that the tenure-based residential segregation index in 2000 and 2010 is between 0.4 and 0.6, which implies that residential segregation is basically moderate and that social housing is more segregated than open market housing. On the whole, market mechanisms have gradually played a fundamental role in tenure-based residential restructuring and segregation since 2000, and governmental and institutional factors also significantly influence such elements.

Discrete analysis of Schwarz waveform relaxation for a diffusion reaction problem with discontinuous coefficients

In this paper, we investigate the effect of the space and time discretisation on the convergence properties of Schwarz Waveform Relaxation (SWR) algorithms. We consider a reaction-diffusion problem with discontinuous coefficients discretised on two non-overlapping domains with several numerical schemes (in space and time). A methodology to determine the rate of convergence of the classical SWR method with standard interface conditions (Dirichlet-Neumann or Robin-Robin) accounting for discretisation errors is presented. We discuss how such convergence rates differ from the ones derived at a continuous level (i.e. assuming an exact discrete representation of the continuous problem). In this work we consider a second-order finite difference scheme and a finite volume scheme based on quadratic spline reconstruction in space, combined with either a simple backward Euler scheme or a two-step “Padé” scheme (resembling a Diagonally Implicit Runge Kutta scheme) in time. We prove those combinations of space-time schemes to be unconditionally stable on bounded domains. We illustrate the relevance of our analysis with specifically designed numerical experiments.

Quinpi: Integrating Conservation Laws with CWENO Implicit Methods

Many interesting applications of hyperbolic systems of equations are stiff, and require the time step to satisfy restrictive stability conditions. One way to avoid small time steps is to use implicit time integration. Implicit integration is quite straightforward for first-order schemes. High order schemes instead also need to control spurious oscillations, which requires limiting in space and time also in the linear case. We propose a framework to simplify considerably the application of high order non-oscillatory schemes through the introduction of a low order implicit predictor, which is used both to set up the nonlinear weights of a standard high order space reconstruction, and to achieve limiting in time. In this preliminary work, we concentrate on the case of a third-order scheme, based on diagonally implicit Runge Kutta (mathsf {DIRK}) integration in time and central weighted essentially non-oscillatory (mathsf {CWENO}) reconstruction in space. The numerical tests involve linear and nonlinear scalar conservation laws.

Study on fault feature extraction of ECS turbine bearing by combination of EEMD and HMM

According to nonstationary characteristics of aircraft environment control system turbine bearings, the objective in the paper is to present EEMD and HMM method to extract characteristic quantities. Firstly, vibration signals under different operating conditions, such as normal bearing and cage fracture under partial load condition, are decomposited by EEMD to filter noise signal and advance signal-to-noise. Then HMM of those signals is calculated, contrasted and analyzed after space reconstruction. The experimental result show that the HMM can be used as the characteristic quantity of ECS turbine bearing on running state. Moreover, this method can accurately and effectively identify the running state of the bearing.

Colour image reconstruction in indoor space based on machine vision

Colour image reconstruction in indoor space based on machine vision