Spatiotemporal Research Articles

Scaling up time–geographic computation for movement interaction analysis

AbstractUnderstanding interactions through movement provides critical insights into urban dynamic, social networks, and wildlife behaviors. With widespread tracking of humans, vehicles, and animals, there is an abundance of large and high‐resolution movement data sets. However, there is a gap in efficient GIS tools for analyzing and contextualizing movement patterns using large movement datasets. In particular, tracing space–time interactions among a group of moving individuals is a computationally demanding task, which would uncover insights into collective behaviors across systems. This article develops a Spark‐based geo‐computational framework through the integration of Esri's ArcGIS GeoAnalytics Engine and Python to optimize the computation of time geography for scaling up movement interaction analysis. The computational framework is then tested using a case study on migratory turkey vultures with over 2 million GPS tracking points across 20 years. The outcomes indicate a drastic reduction in interaction detection time from 14 days to 6 hours, demonstrating a remarkable increase in computational efficiency. This work contributes to advancing GIS computational capabilities in movement analysis, highlighting the potential of GeoAnalytics Engine in processing large spatiotemporal datasets.

Обучаем школьников анализу языковой личности персонажа: подготовка к итоговому сочинению по литературе

The article is devoted to the problem of preparing eleventh graders to write a final essay on literature. The focus is on the methodology of working on the topic “Language and linguistic personality”. In modern humanities, the term “linguistic personality” is studied by scientists from different angles: linguodidactic, linguoritorical, communicative, linguoculturological and linguocognitive approaches. A controversy arises around the methodology for analyzing the linguistic personality, both real (native speaker) and conventional (virtual) within the framework of a literary text, which determines the relevance of the study, the novelty of which lies in the development of an algorithm for analyzing the linguistic personality of a character in a literary text based on the material, adapted for schoolchildren story by A.P. Chekhov’s “Gooseberry”. The purpose of the article is to develop, based on the research of L.N. Churilina and Yu.N. Karaulov, a simplified algorithm for analyzing linguistic personality for schoolchildren, which can be used if you choose the appropriate topic when writing a final essay on literature as part of the Unified State Exam. The research is based on linguostylistic and comparative methods, as well as methods of citation and holistic analysis of a literary work. The subject of the study is the linguistic personality of the heroes of Chekhov’s story “Gooseberry”. Based on the analysis of the story, the author demonstrates the main aspects of revealing the linguistic personality of the characters: mental and speech activity determined by the worldview of the characters, their education and intellectual development, origin and social circle, the space-time continuum in which the text production of the characters is inscribed both in the mental (thought process, objectified by internal speech), and in real discourse (speaking) taking into account the addressee and the statements of others (dialogue, direct speech, improperly direct speech). The proposed work format develops students’ skills in analyzing the linguistic personality of a character and can be successfully used by philologist teachers in preparing for the final essay on literature at school.

Modeling and experiment of femtosecond laser processing of micro-holes arrays in quartz

Quartz material irradiated by femtosecond laser has increasingly attracted widespread attention for the micro-fabrication of photonic devices. Mechanism exploration is beneficial for accelerating the digital progress of laser processing. However, the mechanism between femtosecond laser and quartz is complicated and needs further theoretical investigation. This paper established the theoretical model based on the ionization model with the Drude equation to study the space–time evolution of free electron density and its influence on the absorption coefficient, reflectivity, and ablation depth. In addition, we achieved a 10 × 10 micro-holes array with a pore size less than 10 μm, cone angle less than 2° in a 0.25 mm thick quartz on the condition of a laser pulse energy Ep = 3 μJ, scanning velocity v = 0.1 mm/s, and defocusing distance Δf = −0.3 mm via the bottom-up femtosecond laser processing. The work gives a new insight into further understanding the ablation mechanism of transparent materials etching by the femtosecond laser. It provides a practical technical scheme for preparing commercial quartz photonic devices.

A green and moderate approach for the synthesis of methyl formate via dimethoxymethane disproportionation over H-zeolites

Methyl formate (MF) is an important building block in the modern chemical industry, primarily produced through homogeneous carbonylation of methanol under harsh reaction conditions. It is of great interest but remains challenges to develop new heterogeneous catalytic system under mild reaction conditions to replace the homogeneous route. Herein, we report an efficient and green strategy for MF production by dimethoxymethane (DMM) disproportionation over acidic zeolites. Up to nearly100 % DMM conversion and 0.76 g gcat−1 h−1 of MF space time yield (STY) is achieved over H-SSZ-13 under moderate conditions of 0.1 MPa and 393 K. Moreover, H-SSZ-13 shows superior stability during a 100 h continuous test. Combined with multiple characterizations, formaldehyde (FA) has been proved to be a crucial precursor for the formation of MF and the complete mechanism is proposed. This strategy provides a new and feasible approach for continuous large-scale MF production using acidic zeolite as catalyst by heterogeneous catalytic systems.

Spatiotemporal Patterns in a Space–Time Discrete SIRS Epidemic Model with Self- and Cross-Diffusion

In this work, a two-dimensional Coupled Map Lattice (CML) method is applied to a continuous Reaction–Diffusion (RD) SIRS epidemic model. The model involves a complex, nonlinear incidence rate and the spatially heterogeneous self- and cross-diffusion coefficients. The CML method applied here results in a space–time discrete counterpart of the model. The basic reproduction number is derived, and the local stability of the fixed points of the discrete model is established. The two typical bifurcations of codimension 1, such as the flip and the Neimark–Sacker (NS) bifurcations, are investigated by utilizing the center manifold theory and the normal form theorem. Furthermore, the codimension-2 fold–flip bifurcation about the endemic fixed point is discussed in detail. Turing instability criterion of the cross-diffusion epidemic model is established by utilizing the CML method under periodic boundary conditions. Finally, the numerical simulations clarify both the effectiveness of all theoretical findings and the effects of time step size and cross-diffusion on the spatiotemporal patterns. The main contribution of this work is the discovery of some interesting Turing and non-Turing patterns which may be induced by flip–Turing instability, NS–Turing instability or fold–flip–Turing instability. Concretely speaking, spiral patterns are observed near the flip bifurcation threshold value and ultimately evolve into an irregular defect pattern. Stripe pattern, labyrinth pattern and circular pattern are observed near the NS bifurcation threshold value. The disorderly interwoven ribbon pattern, mosaic pattern and some irregular oscillatory patterns are observed near the fold–flip bifurcation point. Our results greatly help in predicting the long-term behavior of the space–time discrete SIRS epidemic model under study.

Soliton robustness, interaction and stability for a variable coefficients Schrödinger(VCNLS) equation with inverse scattering transformation

The inverse scattering transformation(IST) method is an important method for solving soliton equations. In this paper, some novel solutions of a variable coefficients Schrödinger(VCNLS) equation are obtained with IST method. And we find that these waves have not the effected by other external waves, then call them on robustness waves. The VCNLS equation is derived from a non-isospectral AKNS equation hierarchy, and the spectral parameter is determined by an ordinary differential equation with polynomial nonlinearity. The VCNLS equation with the periodic and harmonic external potentials is solved via the inverse scattering transformation(IST) method, which possess the parity-time(PT) symmetric invariance. Unlike the classical NLS equation, the characteristic functions of the CVNLS equation with space–time external potential have two different symmetric reductions and two different backscattering solutions, which extends the application of IST and is helpful for the CVNLS equation. Some novel bright soliton solutions are obtained, which are different to the previous results in the classical case. The stabilities of the bright soliton solutions and the effect of the phase noise on the solutions are analyzed in a wide region through numerically. Especially, the interaction dynamics of between two solitons are investigated through addressing numerically, which have not a effected by other external waves. Thus, the novel features are different from these usual features of solitons, which have not a effected by other external waves.

Maximizing the symmetry of Maxwell’s equations

Maxwell’s equations can be successfully extended to electromagnetic fields having three complex-valued components rather than their usual three real-valued components. Here the implications of interpreting the imaginary-valued components as extending into time rather than space are explored. The complex-valued Maxwell equations remain consistent with the original Maxwell equations and the experimental results that they predict. Further, the extended equations predict novel phenomena such as the existence of electromagnetic waves that propagate not only through regular space but also through a separate temporal space (time) that is implied by the three imaginary components of the fields. In a vacuum, part of these imaginary valued waves propagates through time at the same rate as an observer stationary in space. While the imaginary valued field components are not directly observable, analysis indicates that they should be indirectly detectable experimentally based on secondary effects that occur under special circumstances. Experimental investigation attempting to falsify or support the existence of complex valued electromagnetic fields extending into time is merited due to the substantial theoretical and practical implications involved.

Knowledge-aided multi-dictionary block sparsity-aware STAP for airborne polarimetric conformal array radar

Airborne polarimetric conformal array radar has gained increased attention recently. However, the conformal array configuration and polarization factors may lead to non-stationary clutter, which significantly degrades the performance of space–time adaptive processing (STAP), particularly in short-range clutter environments. In this paper, we introduce a knowledge-aided multi-dictionary block sparse Bayesian learning (KA-MDBSBL) algorithm to improve the clutter suppression performance. Using prior knowledge, the proposed algorithm designs multi-dictionary matrices for each training sample, rather than a single dictionary matrix for the cell under test (CUT). We take advantage of the identical clutter profile under each dictionary matrix. In the multi-dictionary case, we enforce shared sparsity in the clutter profiles. Additionally, we utilize the inherent block structure of the dictionary matrix to jointly recover clutter and noise power through a fast convergence learning framework. Subsequently, the clutter plus noise covariance matrix is reconstructed using precisely estimated clutter and noise power, along with the dictionary matrix corresponding to the CUT. Numerical simulations are included to demonstrate the effectiveness and superiority of the proposed algorithm.

Sodium-assisted MoS2 for boosting CO2 hydrogenation to methanol: The crucial role of sodium in defect evolution and modification

The effective conversion of CO2 to methanol utilizing green hydrogen under moderate conditions represents a promising approach for achieving carbon neutrality. MoS2 demonstrates exceptional catalytic performance at temperatures below 200 °C, however, generating in-plane sulfur defects is challenging due to the intact planar structure. Introducing heteroatoms to induce sulfur vacancy formation and modulate their chemical environment remains a significant obstacle. In this study, we developed a NaCl-assisted method for synthesizing MoS2 and discovered that a small quantity of sodium not only promotes the formation of sulfur vacancies during the induction period, but also encourages CO2 hydrogenation via the carboxylate route, as opposed to the CO2 dissociation to CO* route over non-modified sulfur vacancies. Furthermore, catalysts at various stages throughout the 60 h induction period were characterized, revealing that the increase in sulfur vacancies and enhanced H2 dissociation capability are primary factors contributing to improved methanol yield. The sodium-modified MoS2 achieves a methanol space–time yield of up to 571 mgMeOH gcat.−1 h−1 at 200 °C and 5 MPa with a 4.8% CO2 conversion and 96% methanol selectivity. The turnover frequency based on total sulfur vacancies reaches 170 h−1. This research is anticipated to offer a new strategy for enhancing the catalytic performance of CO2 hydrogenation to methanol using heteroatom-assisted defect engineering.

FLRW Transit Cosmological Model in f (R, T) Gravity

A Friedmann–Lemaitre–Robertson–Walker space–time model with all curvatures k=0, ±1 is explored in f(R,T) gravity, where R is the Ricci scalar, and T is the trace of the energy–momentum tensor. The solutions are obtained via the parametrization of the scale factor that leads to a model transiting from a decelerated universe to an accelerating one. The physical features of the model are discussed and analyzed in detail. The study shows that f(R,T) gravity can be a good alternative to the hypothetical candidates of dark energy to describe the present accelerating expansion of the universe.

Space–Time Patterns of Nest Site and Nesting Area Selection by the Italian Population of European Rollers: A 3-Year Study of a Farmland Bird Species

The European Roller Coracias garrulus has suffered greatly from breeding habitat loss due to the renovation of old farmhouses and rural buildings and changing agricultural practices that took place extensively across Europe in the last decades. As a consequence, this species experienced a significant decline, and local extinctions of breeding populations were recorded in several European countries. We investigated nest sites and nesting area selection by the Italian Roller population during the breeding period (May–August) between 2016 and 2018. We collected 711 points from field surveys and used four types of point pattern analysis to detect space-time patterns of nest site and nesting area selection. We found that: (a) the spatial distribution of selected (i.e., occupied) nest sites was significantly nonrandom (p < 0.01) for all years and months; (b) only 2.6% of the selected nest sites was located within parks or reserves; (c) there were significant (p < 0.01) latitudinal, longitudinal, and altitudinal shifts of selected nest sites between May and August; (d) the geographical barycentres of selected nest sites shifted northward by about 80 km per month from May (southernmost barycentre) to August (northernmost barycentre); (e) four main nesting areas (7886 km2 in total) occurred in central and southern Italy, whose utilization by the European Rollers differed between months but not between years; (f) the detected nesting areas corresponded mainly to non-irrigated arable lands (41.22% of their extent) and natural grasslands (12.80%). Our results are useful to support conservation strategies for the breeding sites of this farmland species, which is not a regular visitor to protected areas in Italy.

Projection-based reduced-order modelling of time-periodic problems, with application to flow past flapping hydrofoils

Simulating forced time-periodic flows in industrial applications presents significant computational challenges, partly due to the need to overcome costly transients before achieving time-periodicity. Reduced-order modelling emerges as a promising method to speed-up computations. We extend upon the work of Lotz et al. (2024) where a time-periodic space–time model is introduced. We present a time-periodic reduced-order model that directly finds the time-periodic solution without requiring extensive time integration. The reduced-order model gives a reduction in variables in both space and time. Our approach involves a POD-Galerkin reduced-order model based on a time-periodic full-order model that employs isogeometric analysis, residual-based variational multiscale turbulence modelling and weak boundary conditions. The projection-based reduced-order model inherits these features. We evaluate the reduced-order model with numerical experiments on moving hydrofoils. The motion is known a priori and we restrict ourselves to two spatial dimensions. In these experiments we vary the Strouhal and Reynolds numbers, and the motion profile respectively. Reduced-order model solutions agree well with those of the full-order model. The errors over the entire time period of thrust and lift forces are less than 0.2%. This includes complex scenarios such as the transition from drag to thrust production with increasing Strouhal number. Our time-periodic reduced-order model offers speed-ups ranging from O(102) to O(103) compared to the full-order model, depending upon the basis size. This makes it an appealing solution for prescribed time-periodic problems, with potential for additional speedup through nonlinear reduction techniques such as hyper-reduction.

Spatiotemporal epidemiology of substance-related accidental acute toxicity deaths in Canada from 2016 to 2017

ObjectivesIn Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space–time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster.MethodsTwo years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space–time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster.ResultsEight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters.ConclusionOur findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

Gasoline production by oligomerization of 1-butene at low pressure: Kinetic model and reactor simulation

A five lump kinetic model (C4=, C8=, C12=, C1-C4 and C5-C12) has been established for 1-butene (C4=) oligomerization at low pressure (1.5 bar) and 275 °C on an HZSM-5 zeolite catalyst agglomerated in a γ-Al2O3 mesoporous matrix. The kinetic parameters have been calculated based on the results obtained in a packed-bed reactor for different concentrations of 1-butene in the feed (with N2 as diluent) and different values of space time (0.5–9.5 gcatalyst h molC-1). The model quantifies the evolution with time on stream of the concentrations of the lumps, considering the selective deactivation of the catalyst for the oligomerization, cracking and pool of secondary reactions. Furthermore, it allows to calculate the remnant activity of the catalyst in the pseudo-steady state (>5h on stream) for the different reaction groups, as well as the corresponding product distribution. The kinetic model has been used in the simulation of the packed-bed reactor to study the effect of space time and 1-butene partial pressure variables. At the conditions range studied experimentally, 39.4 % gasoline yield (C5-C12 fraction, mainly composed of olefins) and a 1-butene conversion of 45 % are achieved in the pseudo-steady state of the catalyst, at a space time of 9.5 gcatalyst h molC-1 and a partial pressure of 1-butene of 0.8 bar. Additionally, for values exceeding those studied experimentally, the model predicts at the pseudo-steady state a gasoline yield of 60 % at a space time of 30 gcatalyst h molC-1 and a partial pressure of 1-butene of ∼ 0.5 bar.

Investigation of the wave solutions of two space–time fractional equations in physics

In this study, two fractional differential equations describing the characteristics of three-dimensional nonlinear ion-acoustic waves (IAWs) in plasmas are considered with the conformable fractional derivative (CFD). 3+1-dimensional space–time fractional Jimbo–Miwa (JM) equation and 3+1-dimensional space–time fractional modified KdV–Zakharov–Kuznetsov (mKdV-ZK) equation are reduced into ordinary differential equations and an investigation into the exact solutions is carried out through the modified exponential function method (MEFM). These exact wave solutions are beneficial in interpreting the interior structure and characteristics of physical phenomena modeled by the aforementioned nonlinear evolution equations (NLEEs) arising in magnetized plasma. Various analytical methods were applied to such equations. Therefore, it is vital to determine the proper technique for the present problem. The proposed scheme has demonstrated a different spectrum of wave structures. The portrait of the exact solutions permits the exploration of plasmas, plasma models, and optics. The software system Mathematica is employed and the rational, exponential, trigonometric, and hyperbolic function solutions are acquired. The graphical results are presented for the physical nature of the solutions. The results are compared with the integer-order models.

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space.An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias.For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

Study on the impact of digital economy on green innovation in Anhui Province through space–time evolution

Study on the impact of digital economy on green innovation in Anhui Province through space–time evolution

High performance holographic video compression using spatio-temporal phase unwrapping

In this work, we present a high-performance holographic data compression technique. This approach is based in the temporal correlation found in a holographic video generated using an optimized random phase. This temporal correlation makes possible high levels of compression by using a spatio-temporal (ST) representation, partial phase unwrapping, and a lossless compression algorithm like DEFLATE. In this approach, two holographic videos are created using the optimized random phase (ORAP) technique and the Gerchberg-Saxton (GS) algorithm. The phase holograms corresponding to each frame of the videos are arranged into a ST representation, enabling spatial and temporal phase unwrapping. We test phase unwrapping along the spatial and temporal dimensions separately, followed by volume reduction after applying the DEFLATE lossless compression algorithm to the unwrapped phase in each case. The holographic video is then reconstructed from the compressed temporal and spatial unwrapped phase. The results demonstrate the high efficiency of the proposed method, with ORAP holograms achieving a compression percentage of 92.670 % after applying DEFLATE to a ST representation with partial phase unwrapping along the temporal dimension.

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector’s original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection.

Space–time clusters and co-occurrence of Plasmodium vivax and Plasmodium falciparum malaria in West Bengal, India

BackgroundMalaria, a prominent vector borne disease causing over a million annual cases worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space–time clustering and seasonal trends of Plasmodium vivax and Plasmodium falciparum malaria cases in West Bengal.MethodsSeasonal and trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space–time cluster analysis of both malaria cases were performed at block level using Kulldorff’s space–time scan statistics from April 2011 to March 2021 to detect statistically significant space–time clusters.ResultsFrom the time series decomposition, a clear seasonal pattern is visible for both malaria cases. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plain. From the multivariate space–time scan statistics, the co-occurrence of both cases were detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria cases.ConclusionsSeasonal trends from the time series decomposition analysis show a gradual decline for both P. vivax and P. falciparum cases in West Bengal. The space–time scan statistics identified high-risk blocks for P. vivax and P. falciparum malaria and its co-occurrence. Both malaria types exhibit significant spatiotemporal variations over the study area. Identifying emerging high-risk areas of P. falciparum malaria over the Gangetic belt indicates the need for more research for its spatial shifting. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication.