Space-time Model Research Articles

Periodicity and Variability in Daily Activity Satisfaction: Toward a Space-Time Modeling of Subjective Well-Being

Understanding how geographical environments influence peoples’ subjective experiences of daily activities is of great potential for improving subjective well-being (SWB), a subject that is presently limited by a lack of available data and proper statistical methods. Focusing on Beijing and using a unique data set that linked residents’ seven-day mobility trajectories at a fine spatiotemporal resolution to their complete activity participation and momentary well-being, this article investigates the temporal dynamics of and geographical contextual effects on SWB of daily activities. We developed a unified spatial multilevel stochastic process model to simultaneously capture periodicity, stochastic dynamics, and individual heterogeneity effects. Results show that momentary SWB has a twenty-four-hour periodicity and evolves stochastically around individual equilibrium states depending on key life-circumstance variables. Migrants and low-income residents tend to have lower equilibrium states than their counterparts. Real-time air pollution exposure significantly lowers daily activity satisfaction levels, and an inverted-U-shaped relationship exists between city vibrancy and satisfaction.

Spatiotemporal analysis of the effect of global development indicators on child mortality

BackgroundChild mortality continue to be a major public health issue in most developing countries; albeit there has been a decline in global under-five deaths. The differences in child mortality can best be explained by socioeconomic and environmental inequalities among countries. In this study, we explore the effect of country-level development indicators on under-five mortality rates. Specifically, we examine potential spatio-temporal heterogeneity in the association between major world development indicators on under-five mortality, as well as, visualize the global differential time trend of under-five mortality rates.MethodsThe data from 195 countries were curated from the World Bank’s World Development Indicators (WDI) spanning from 2000 to 2017 and national estimates for under-five mortality from the UN Inter-agency Group for Child Mortality Estimation (UN IGME).We built parametric and non-parametric Bayesian space-time interaction models to examine the effect of development indicators on under-five mortality rates. We also used employed Bayesian spatio-temporal varying coefficient models to assess the spatial and temporal variations in the effect of development indicators on under-five mortality rates.ResultsIn both parametric and non-parametric models, the results show indicators of good socioeconomic development were associated with a reduction in under-five mortality rates while poor indicators were associated with an increase in under-five mortality rates. For instance, the parametric model shows that gross domestic product (GDP) (β = − 1.26, [CI − 1.51; − 1.01]), current healthcare expenditure (β = − 0.40, [CI − 0.55; − 0.26]) and access to basic sanitation (β = − 0.03, [CI − 0.05; − 0.01]) were associated with a reduction under-five mortality. An increase in the proportion practising open defecation (β = 0.14, [CI 0.08; 0.20]) an increase under-five mortality rate. The result of the spatial components spatial variation in the effect of the development indicators on under-five mortality rates. The spatial patterns of the effect also change over time for some indicators, such as PM2.5.ConclusionThe findings show that the burden of under-five mortality rates was considerably higher among sub-Saharan African countries and some southern Asian countries. The findings also reveal the trend in reduction in the sub-Saharan African region has been slower than the global trend.

Mechanics of Spacetime with Surface Tension and Preferred Curvature

In previous publications, it was argued that because energy and matter are aligned in the thin increment of present time for an observer, spacetime should and must have surface tension. In this paper, we review how to apply 4-dimensional continuum mechanics with imaginary time coordinates to derive a mechanical model of spacetime with surface tension. Then, to continue model development, we discuss the concept of preferred curvature from the physical chemistry of surfaces and attempt to apply those concepts to spacetime geometry. We show how the model exhibits quantum fluctuations at the Plank scale and components resembling dark matter and dark energy at the cosmic scale.

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning

ObjectiveIn this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space–time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.MethodsClinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).ResultsThe AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).ConclusionsThe RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

Cosmological test of [formula omitted] as function of scale factor in [formula omitted] framework

The paper presents a study of the Bianchi type-I space–time model with perfect fluid in f(R, T) modified theory of gravity, where R is the Ricci scalar, and T denotes the trace of the energy–momentum tensor. The gravitational field equations of f(R,T) are solved using expansion anisotropy as a suitable function of the average scale factor. Besides, we estimate the best-fit values of the model parameters by using the combined dataset of Cosmic Chronometers (CC) consisting of 31 measurements, the type Ia supernova dataset composed of 1048 measurements and the Baryon Acoustic Oscillations dataset consisting of ten measures with the Markov Chain Monte Carlo (MCMC) method. The resulting deceleration parameter shows that the Universe initially decelerated and later achieved an accelerated phase. Moreover, the paper analyses the cosmographic and cosmological parameters, suggest some improvements and illustrates findings through diagrams.

A Dually Flat Embedding of Spacetime

A model of spacetime is presented. It has an extension to five dimensions, and in five dimensions the geometry is the dual of the Euclidean geometry w.r.t. an arbitrary positive-definite metric. Dually flat geometries are well-known in the context of information geometry. The present work explores their role in describing the geometry of spacetime. It is shown that the positive-definite metric with its flat 5-d connection can coexist with a pseudometric for which the connection is that of Levi-Civita. The 4-d geodesics are characterized by five conserved quantities, one of which can be chosen freely and is taken equal to zero in the present work. An explicit expression for the parallel transport operators is obtained. It is used to construct a pseudometric for spacetime by choosing an arbitrary possibly degenerate inner product in the tangent space of a reference point, for instance, that of Minkowski. By parallel transport, one obtains a pseudometric for spacetime, the metric connection of which extends to a 5-d connection with vanishing curvature tensor. The de Sitter space is considered as an example.

Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies

This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response. A surrogate model is used to approximate the response of computationally expensive spatial and temporal fields resulting from some computational mechanics simulations. Within a design context, a surrogate takes a vector of design variables that describe a current design and returns an approximation of the design’s response through a pseudo-time variable. To compare various radial basis function (RBF) surrogate modeling approaches, the prediction of a load displacement path of a snap-through structure is used as an example numerical problem. This work specifically considers the scenario where analytical sensitivities are available directly from the computational mechanics’ solver and therefore gradient enhanced surrogates are constructed. In addition, the gradients are used to perform a domain transformation preprocessing step to construct surrogate models in a more isotropic domain, which is conducive to RBFs. This work demonstrates that although the gradient-based domain transformation scheme offers a significant improvement to the performance of the space-time surrogate models (STSMs), the network surrogate model (NSM) is far more robust. This research offers explanations for the improved performance of NSMs over STSMs and recommends future research to improve the performance of STSMs.

Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Modeling in Forecasting Covid-19 Cases in Indonesia

Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of Covid-19 in Indonesia has grown rapidly that the World Health Organization (WHO) has declared Covid-19 a pandemic. Covid-19 cases have spread to 34 provinces in Indonesia. Covid-19 data in Indonesia involves space and time so the appropriate modeling is the space-time model. Space-time modeling of the Covid-19 case in 34 provinces in Indonesia using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model has not been carried out. The purpose of this research is to get the best GSTARIMA model and forecat the Covid-19 case for the next several times. This model incorporates time and location interdependence with different parameters for each location. Identification of the order of the AR and MA was carried out through the STACF and STPACF plots. For simplicity of interpretation, the spatial order is chosen first order. In this study, the queen contiguity and the inverse distance location weighting matrix were used. The parameter estimation used is Ordinary Least Square (OLS). The results show that the best model for predicting Covid-19 cases in 34 provinces in Indonesia is the GSTARIMA model (1,1,0)1 using an inverse distance weighting matrix with the smallest RMSE value of 1.22.Keywords: Covid-19, GSTARIMA, Queen Contiguity, Inverse Distance, OLS.

Path integral suppression of badly behaved causal sets

Causal set theory is a discrete model of spacetime that retains a notion of causal structure. We understand how to construct causal sets that approximate a given spacetime, but most causal sets are not at all manifold-like, and must be dynamically excluded if something like our Universe is to emerge from the theory. Here we show that the most common of these ‘bad’ causal sets, the Kleitman–Rothschild orders, are strongly suppressed in the gravitational path integral, and we provide evidence that a large class of other ‘bad’ causal sets are similarly suppressed. It thus becomes plausible that continuum behavior could emerge naturally from causal set quantum theory.

Quantum Vacuum Excitation of a Quasinormal Mode in an Analog Model of Black Hole Spacetime.

Vacuum quantum fluctuations near horizons are known to yield correlated emission by the Hawking effect. We use a driven-dissipative quantum fluid of microcavity polaritons as an analog model of a quantum field theory on a black-hole spacetime and numerically calculate correlated emission. We show that, in addition to the Hawking effect at the sonic horizon, quantum fluctuations may result in a sizable stationary excitation of a quasinormal mode of the field theory. Observable signatures of the excitation of the quasinormal mode are found in the spatial density fluctuations as well as in the spectrum of Hawking emission. This suggests an intrinsic fluctuation-driven mechanism leading to the quantum excitation of quasinormal modes on black hole spacetimes.

A Prediction Model and Factor Importance Analysis of Multiple Measuring Points for Concrete Face Rockfill Dam during the Operation Period

Dam settlement monitoring is a crucial project in the safety management of concrete face rockfill dams (CFRD) over their whole life cycle. With the development of an automatic monitoring system, a large amount of settlement data was collected. To precisely predict the structural health of dams, a combined multiple monitoring points (MMP) model and a machine learning model has been developed. In this paper, based on the physical factors of the CFRD, we comprehensively analyzed the influence of water level load transfer, rockfill rheology and soil properties on the settlement during the impoundment operation period. Then, we established a space-time distribution model of the CFRD during its operation period under multiple factors. An extreme gradient boosting (XGBoost) model was used for fitting prediction, and the model was evaluated using various performance indicators. The results show that spatial parameters such as the upper filling height, rockfill thickness, panel-point distance and soil material correlate to the deformation characteristics of the rockfill dam. Taking the monitoring data of the settlement of the Liyuan CFRD as an example, the new MMP model was evaluated and used to predict the settlement of the full-section points with higher accuracy, which has certain application and popularization value for related projects. Then, to evaluate the contribution of the components of the new MMP model, the SHapley Additive explanation (SHAP) methods are used to evaluate the importance of the selected factors, and the reasonability of these factors is verified.

Determination of the best weight matrix for the Generalized Space Time Autoregressive (GSTAR) model in the Covid-19 case on Java Island, Indonesia

One factor that goes into the construction of a space–time model, in this case, the Generalized Space–Time Autoregressive (GSTAR) model, is the weight matrix, which acts as a spatial representation and illustrates the relationship that exists between different locations. The temporal and spatial correlations are both accounted for in this model simultaneously. The accuracy with which the weight matrix is chosen significantly impacts the prediction results. The distance inverse and uniform weight matrices are two examples of weight matrices utilized frequently. A recent study on the adjustment of the weight matrix uses distance with rail transit, queen contiguity matrices, and techniques with graph theory. This research aims to find a weight matrix that closely resembles the real-world spatial context by analyzing and contrasting the five weight matrices presented earlier. Afterward, the weight matrix is fed into the GSTAR model as an input variable. In this article, each weight matrix’s various properties will be broken down and analyzed in detail. The case study used in preparation for the Christmas and New Year’s celebrations in 2023 focused on the daily increase in Covid-19 cases across six provinces on Java Island, Indonesia. The Java island was selected as Indonesia’s capital because it serves as the country’s economic hub and connects all provinces to one another. This enables large-scale spatial links between sites with the same time lag or different time delays. According to the findings that were uncovered, the MST weight matrix is the one that should be utilized, and it should have a prediction error tolerance of 19%.

The impact of symmetry violations on determining optimal control trajectories for complex robotic systems

Complex robotic systems, their properties, features and interactions are considered, the article offers the definition of a complex system on the basis of 5 properties: openness, non-morphic variability of three types (structural, spatial and information), Double-code, event aggregation and violation of physical symmetries. The influence of violations of physical symmetries on the determination of optimal control trajectories is reflected in the paradigm approach to the study of complex robotic systems that are not formalized as mathematical objects. The basic concepts, postulates and hypotheses are formulated. Ideal designs of variability of complex systems; energy causal sets; energies; events, causes, effects and evolution; spacetime, quanta and vacuum; interaction of individuals; operators of physical interactions, aggregated events, text and attachments of words. Three basic models for the study of complex systems - the model of physical interactions, the neurolinguistic model and the model of control at incomplete compatibility are proposed and briefly described. The structure of the kernel of the platform of physical simulation modeling for the researches of complex systems is resulted. Three types of space-time quantum modeling - minimal, semantic and evolutionary - are described. The article provides an illustration of results of application of the proposed approach to the study of actions of complex systems. It is noted that the resulting mathematical structure exhibits fractal properties. Typical trajectories of evolution - «homeostat»; «attenuation of actions»; «invariant»; «accident»; «window of possibilities» have been allocated. The article provides a number of principles of research of complex methodological and methodical systems. Recommendations were made on the scope of the proposed approach.

Probing the spacetime and accretion model for the Galactic Center: Comparison of Kerr and dilaton black hole shadows

Context. In the 2017 observation campaign, the Event Horizon Telescope (EHT) for the first time gathered enough data to image the shadow of the supermassive black hole (SMBH) in M 87. Most recently in 2022, the EHT has published the results for the SMBH at the Galactic Center, Sgr A*. In the vicinity of black holes, the influence of strong gravity, plasma physics, and emission processes govern the behavior of the system. Since observations such as those carried out by the EHT are not yet able to unambiguously constrain models for astrophysical and gravitational properties, it is imperative to explore the accretion models, particle distribution function, and description of the spacetime geometry. Our current understanding of these properties is often based on the assumption that the spacetime is well described by the Kerr solution to general relativity, combined with basic emission and accretion models. We explore alternative models for each property performing general relativistic magnetohydrodynamic (GRMHD) and general relativistic radiative transfer (GRRT) simulations. Aims. By choosing a Kerr solution to general relativity and a dilaton solution to Einstein-Maxwell-dilaton-axion gravity as exemplary black hole background spacetimes, we aim to investigate the influence of accretion and emission models on the ability to distinguish black holes in two theories of gravity. Methods. We carried out 3D GRMHD simulations of both black holes, matched at their innermost stable circular orbit, in two distinct accretion scenarios: standard and normal evolution (SANE) and a magnetically arrested disk (MAD). Using GRRT calculations, we modeled the thermal synchrotron emission and subsequently applied a nonthermal electron distribution function, exploring representative parameters to compare with multiwavelength observations. We further considered Kerr and dilaton black holes matched at their unstable circular photon orbits, as well as their event horizons. Results. From the comparison of GRMHD simulations, we find a wider jet opening angle and higher magnetization in the Kerr spacetime. Generally, MAD models show larger magnetic flux than SANE, as is expected. The GRRT image morphology shows differences between spacetimes due to the Doppler boosting in the Kerr spacetime. However, from pixel-by-pixel comparison, we find that in a real-world observation an imaging approach may not be sufficient to distinguish the spacetimes using the current finite resolution of the EHT. From multiwavelength emission and spectral index analysis, we find that the accretion model and spacetime have only a small impact on the spectra compared to the choice of the emission model. Matching the black holes at the unstable photon orbit or the event horizon further decreases the observed differences.

A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach.

The global pandemic of SARS-CoV-2 (COVID-19) has been linked to adversely impacting individuals with opioid use disorder in the United States. This study focuses on analyzing opioid-involved mortality in the context of COVID-19 in the U.S. from a geospatial perspective. We investigated spatiotemporal patterns of opioid-involved deaths during 2020 and compared the spatiotemporal pattern of these deaths with patterns for the previous three years (2017-2019) to understand changes in the context of the COVID-19 pandemic. A counterfactual analysis framework together with a space-time random forest (STRF) model were used to estimate the increase in opioid-involved deaths related to the pandemic. To gain further insight into the relationship between opioid deaths and COVID-19-related factors, we built a space-time random forest model for the City of Chicago, that experienced a steep increase in opioid-related deaths during 2020. High ranking indicators identified by the model such as the number of positive COVID-19 cases adjusted by population and the change in stay-at-home dwell time during the pandemic were used to generate a vulnerability index for opioid overdoses during the COVID-19 pandemic in Chicago.

A compass time-space model-based virtual IP routing scheme for NTSN satellite constellations

Recently, Non-Terrestrial Satellite Networks (NTSTs) have gained more and more attentions due to global coverage, low latency, and high-speed communications. The routing scheme is one of the primary challenges for NTSNs, due to the mega scale of an NTSN constellation and the dynamic topology feature. To solve many pressing problems, a Compass time-space Model-based Virtual IP (CMVIP) routing scheme is proposed in this paper. In order to compensate for discontinuities in existing topology models, a compass-shaped time-space model is proposed. It can be adapted with Inter-Satellite-Link (ISL) and Ground-Satellite-Link (GSL) transmissions. A distributed algorithm with multiple optimization objectives and multiple constraints is applied for routing path discovery. To more realistically verify the specific scheme, a traffic model that supports two different services is proposed. Access and bearer services are the two main applications of an NTSN. Experimental results demonstrate that the proposed scheme achieves superior Quality-of-Service (QoS) performance. In addition, comparison results demonstrate that the CMVIP routing scheme is superior to the Virtual-Topology-based Shortest Path (VT-SP) routing algorithm.

Bivariate spatial clustering in differential time trends of related tropical diseases: Application to diarrhea and intestinal parasite infections

There has been a rapid development of space–time multivariate disease mapping methods that focus on space–time variation of risks. Examining the posterior estimates of the space–time random effects can provide compelling epidemiological information that is necessary for public health monitoring. In this study, we propose and evaluate the posterior estimates of the random effects to examine spatial and temporal trends of two tropical diseases. Our model is a multivariate Bayesian space–time model with common spatial and temporal trends, and a space–time interaction term that allows different time trends for different areas. When applied to diarrhea and intestinal parasites data from Ghana, the model that consolidates all random effects as a multivariate conditional autoregressive prior was the best fit. The implementation is based on the notion that diarrheal and intestinal parasite infections share common risk factors Our novel contribution concerns the posterior joint evaluations of the spatial and temporal random effects into a 5 × 5 cross-tabulation table. The method presented is useful for developing and implementing joint epidemiological control strategies, especially in countries where resources are scarce.

Mimicking the [formula omitted]CDM universe through inhomogeneous space–time

Starting from an inhomogeneous space–time model of the universe we could recreate a scenario of recent time accelerating universe dominated by Dark Energy type of fluid. The background matter component of such a universe was considered to be made up of a combination of an anisotropic fluid, a barotropic fluid and the pressure less cold dark matter. It was found that inhomogeneity exhibits itself as the curvature term in such a universe. We corroborated our model with recent supernova Ia-JLA data together with H0 data and BAO data. Cosmographic analysis of the dynamical variables further show that the model can mimic the ΛCDM cosmology very closely.

Noncommutative Yang model and its generalizations

Long time ago, Yang [Phys. Rev. 72, 874 (1947)] proposed a model of noncommutative spacetime that generalized the Snyder model to a curved background. In this paper, we review his proposal and the generalizations that have been suggested during the years. In particular, we discuss the most general algebras that contain as subalgebras both de Sitter and Snyder algebras, preserving Lorentz invariance, and are generated by a two-parameter deformation of the canonical Heisenberg algebra. We also define their realizations on quantum phase space, giving explicit examples, both exact and in terms of a perturbative expansion in deformation parameters.

A decade of child pedestrian safety in England: a Bayesian spatio-temporal analysis

BackgroundChild pedestrian injury is a public health and health equality challenge worldwide, including in high-income countries. However, child pedestrian safety is less-understood, especially over long time spans. The intent of this study is to understand factors affecting child pedestrian safety in England over the period 2011–2020.MethodsWe conducted an area-level study using a Bayesian space-time interaction model to understand the association between the number of road crashes involving child pedestrians in English Local Authorities and a host of socio-economic, transport-related and built-environment variables. We investigated spatio-temporal trends in child pedestrian safety in England over the study period and identified high-crash local authorities.ResultsWe found that child pedestrian crash frequencies increase as child population, unemployment-related claimants, road density, and the number of schools increase. Nevertheless, as the number of licensed vehicles per capita and zonal-level walking/cycling increase, child pedestrian safety increases. Generally, child pedestrian safety has improved in England since 2011. However, the socio-economic inequality gap in child pedestrian safety has not narrowed down. In addition, we found that after adjusting for the effect of covariates, the rate of decline in crashes varies between local authorities. The presence of localised risk factors/mitigation measures contributes to variation in the spatio-temporal patterns of child pedestrian safety.ConclusionsOverall, southern England has experienced more improvement in child pedestrian safety over the last decade than the northern regions. Our study revealed socio-economic inequality in child pedestrian safety in England. To better inform safety and public health policy, our findings support the importance of a targeted system approach, considering the identification of high-crash areas while keeping track of how child pedestrian safety evolves over time.