Spatiotemporal Behavior Research Articles

Optical soliton solutions in a distinctive class of nonlinear Schrödinger’s equation with cubic, quintic, septic, and nonic nonlinearities

The Biswas–Mollivic equation is a special type of nonlinear Schrödinger equation, which explains the spatio-temporal behaviour of excitable media. In this paper, we investigate the optical soliton solutions of the Biswas–Mollivic equation with cubic–quintic–septic–nonic nonlinearities using the generalized Riccati equation mapping method. This method is efficient and provides new perspectives. It also provides novel insights into the dynamics of excitable media. Our findings add to a better understanding of the complex spatio-temporal patterns that develop in excitable media and have potential applications in the design of new technologies for controlling and manipulating pattern formation. To depict optical soliton solutions graphically, we use the MATLAB software.

An experimental and numerical investigation into the sensitivity of Rayleigh–Bénard convection to heat loss through the sidewalls

Thermal buoyancy flows and related heat transfer problems play a crucial role in a variety of fields and industrial applications. The present study is structured around the main objective of systematically investigating the response of fluid convection of the Rayleigh–Bénard type to heat loss taking place through the lateral walls of the considered fluid container. We elucidate, both experimentally and numerically, new aspects which deeply affect the dynamics when the classical assumptions of adiabatic or conducting walls cease to be valid. The emerging spatio-temporal behaviors are examined with respect to several parameters or conditions, including the dimensionality of the system (made accessible by dedicated numerical simulations based on the time-dependent and non-linear governing equations), the average temperature of the liquid, the applied temperature difference, the dependence of fluid properties on temperature and the intensity of heat transfer to the ambient. The results reveal the interesting triadic relationship among the heat exchanged through non-thermally controlled walls, the hierarchy of bifurcations displayed by the flow and the related multiplicity of solutions.

Geospatial Assessment of Potential Groundwater Zones Within Igede Ekiti, Ekiti State, Nigeria

Groundwater happens to be an accessible and renewable source of water, and so has to be monitored. A Multi-criteria Decision approach was utilized which allows for data fusion of various thematic criteria using the Analytical Hierarchical approach to assign weight to the selected criteria peculiar to Igede-Ekiti, of Irepodun/Ifelodun Local Government Area. The study aims to carry out a geospatial assessment of the groundwater potential zone within Igede Ekiti, Ekiti state. Geology, Rainfall, Digital Elevation Model, Land Use land cover, Nominal difference in water Index (NDWI), Slope and Lineament density were selected to depict the potential groundwater zones, of which geospatial assessments were carried out. Selected groundwater sources in the form of boreholes and well were afterwards used to ground truth and depict the distribution of groundwater sources within the study area. The ArcMap software’s weight overlay Analysis was used to present the output in the form of a Potential Groundwater zones (PGWZ) map, with the Groundwater sources, wells and boreholes, indicating that they were well dispersed within the study area, but more densely situated around the built-up class of the study area. The results indicate that high PGWZ was more prevalent than the other classes, as the classes were distributed into very high, high and low PGWZ. The very high PGWZ covered 7%, the high PGWZ 55% and the Low PGWZ, 38%. The study can be used for further physical planning and decision-making for policymakers. Further research into the Spatio-temporal behaviour of groundwater potential is recommended.

Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.

Four-dimensional conditional averaging tomography of rotating plasma ejection from cylindrical detached plasma

Detached plasma formation is a way to reduce the heat load on the wall in magnetic fusion devices. This study proposes a novel analysis technique consisting of the conditional averaging, sliding window, and tomography to reveal the spatiotemporal behavior of the rotating radial ejection event of detached plasma, which further contributes to local heat load reduction. The used equipment is a high-speed camera and an electrostatic probe located at the periphery of the linear plasma device NAGDIS-II. By applying this method, four-dimensional (4D) behavior of the emission structure along time (1D) and space perpendicular and parallel to the magnetic field (3D) was clarified; a rotating distorted structure appears as a precursor, which is then scraped and transported radially and axially. The proposed method is widely applicable to short-term rigid-body rotating structures, especially in linear plasmas.

Study on Spatiotemporal Behavior of Self-driving Tourists in Inner Mongolia

Against the backdrop of normalized tourism and upgraded consumption, self-driving tours have become one of the main choices for tourists. Faced with the booming self-driving tour market, it is of great significance to explore the spatial behavior characteristics and temporal constraint factors of self-driving tourists within a geographical range using GPS trajectory data. This exploration aids in the planning and design of self-driving tour routes, tourism products, and promotes the healthy and sustainable development of the self-driving tour industry. This paper selects Inner Mongolia Autonomous Region as the self-driving tour hotspot area, collects GPS data of self-driving tourists from 2017 to 2022 on the online platform "Foooooot" using Python, and conducts in-depth exploration and comparative analysis of the spatiotemporal behavior trajectories of self-driving tourists, aiming to explore the spatiotemporal behavior characteristics of self-driving tourists and their constraining factors.

Spatiality of Human Visceral Leishmaniasis in Maranhão, Brazil

Objective: the study analyzed the spatio-temporal and epidemiological behavior of HVL in the state of Maranhão, between 2010 and 2020. Theoretical framework: In Brasil, HVL is one of several diseases with compulsory notification, and is registered in 21 Federation Units, establishing itself in the five Brasilian regions. The Northeast region has the highest number of recorded cases, considered a focus of transmission of the disease, with Maranhão being one of the states with the highest occurrence. Method: the study used secondary data from the Notifiable Diseases Information System. The incidence rate, lethality rate, mortality rate, Odds Ratio and Annual Increment Rate were calculated. Results and conclusions: pointed to an endemic scenario of the disease. The predominant profile was male, aged between 1 and 9 years old, brown color/race, primary education and urban residence area. It is necessary to monitor the environment where vectors are present to control them and reduce the possibility of illness. Implications: the study is relevant as it contributes with epidemiological data and health indicators from an endemic region, so that health authorities can implement surveillance actions, health education and carry out public health interventions respecting socioeconomic differences and local environmental. Novelty: the identification of areas at risk for the disease in a given region allows us to know their spatiality, the population's chances of becoming ill and the rate of increase in the disease.

Impact of Microclimate on Perception and Physical Activities in Public Spaces of New Urban Areas in Beijing, China

The development of new urban areas in Beijing has alleviated overcrowding in old urban centers and has ample public spaces for recreational activities. However, these public spaces are not ideally designed and have not been as successful as expected. Few studies have investigated the ineffective use of these public spaces in terms of microclimatic and thermal comfort factors. Our study investigated microclimatic factors, the subjective assessment of thermal comfort, the intensity of human activities, and the spatial features of public spaces in conjunction with surrounding buildings in a mixed commercial and residential complex in the Fangshan District, Beijing. We used a mixed-methods approach comprising microclimate measurements, questionnaires (n = 150), spatiotemporal behavior mapping, and field measurements. Our results showed that the human perception of the microclimate is related to the exposure duration and other microclimatic factors. The perception of people who spend longer periods outdoors is often inconsistent with objectively measured thermal comfort values. Activity intensity (low, medium, and high) was also related to the duration of time spent outdoors. Microclimatic factors affect the number of people at different activity intensities and the trajectory of the activities. Different spatial features cause different microclimate formations and can directly influence the human subjective assessment of thermal comfort. This study uniquely links the microclimate to human perceptions, physical activities, and spatial features in service of redesigning public spaces. We developed a comprehensive methodology that expands the post-occupancy evaluation and proposes new urban public space designs that consider microclimates. This study also provides a new perspective for promoting physical activity by enhancing the thermal comfort of the environment to achieve physical and mental health goals.

Spatio-Temporal Network-based Bank Transactional Behavior Analysis to Detect Suspicious Activities

Money laundering refers to the act of disguising the proceeds of illegal activities as legitimate funds. This is a significant problem as it enables criminals to profit from illegal activities and finance further criminal endeavors. Money laundering is also linked to other crimes, such as drug trafficking, terrorism financing, and corruption. To combat money laundering, governments and financial institutions have implemented various measures, such as Know Your Customer (KYC) regulations, Anti-Money Laundering (AML) laws, and the use of financial intelligence units. However, the existing money laundering system is complex, making it difficult to detect and prevent money laundering activities. Many current systems rely on outdated technology and manual processes, which can be time-consuming and prone to error. Therefore, there is a need for effective detection and prevention systems that can identify suspicious transactions and patterns of behavior. This project aims to prevent and detect money laundering activities by identifying suspicious transactions and monitoring the movement of funds through the financial system. The proposed system uses Long Short-Term Memory (LSTM) to detect and prevent money laundering activities. By analyzing the data in a time-series format, LSTM can identify unusual patterns of transactions and flag them for further investigation. LSTM can also predict future trends in financial data, allowing for the detection and prevention of potential money laundering activities before they occur. The proposed transactional network and behavior analysis system provides a more efficient and accurate method for identifying potential money laundering activities. This system can ultimately lead to a more effective and efficient anti-money laundering system. Keywords: Anti-Money laundering, Long short-term memory, Legitimate funds, Transaction network, behavior analysis, Time series format

Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture

Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people’s behavior during field labor. In this study, we propose a field manual labor behavior recognition network based on an enhanced SlowFast architecture. The main work includes the following aspects: first, we constructed a field manual labor behavior dataset containing 433,500 fast-track frames and 8670 key frames based on the captured video data, and labeled it in detail; this includes 9832 labeled frames. This dataset provides a solid foundation for subsequent studies. Second, we improved the slow branch of the SlowFast network by introducing the combined CA (Channel Attention) attention module. Third, we enhanced the fast branch of the SlowFast network by introducing the ACTION hybrid attention module. The experimental results show that the recognition accuracy of the improved SlowFast network model with the integration of the two attention modules increases by 7.08%. This implies that the improved network model can more accurately locate and identify manual labor behavior in the field, providing a more effective method for problem solving.

Ultrafast oscillation in a field emission-driven miniaturized gaseous diode

We report an ultrafast oscillation (up to ∼104 GHz) in the early stage of field emission-driven microdischarges. Spatiotemporal behaviors of electron density, space charge density, and electric field, exhibiting high-frequency oscillations, are demonstrated based on first-principle particle-in-cell/Monte Carlo collision simulations. Intermittent electron emission fluxes are identified from the electron phase space distributions whereas the ions are rather non-oscillatory in the transient timescale. The mechanisms of oscillation with growing amplitude are found to be related to the rapid modification of the field emission current affected by the space charge electric field, which is also accompanied by the fast response of the electron transport and ionization in a dynamic double-layer sheath. Further, a transport equation for emission current is solved to estimate the oscillation frequency, which agrees well with the simulation results. This study provides a more precise understanding of the formation of the field emission-driven microdischarge, which informs the design and optimization of miniaturized gaseous electronic devices.

Study of a cannibalistic prey–predator model with Allee effect in prey under the presence of diffusion

In this study, we have investigated the temporal and spatio-temporal behavior of a prey–predator model with weak Allee effect in prey and the quality of being cannibalistic in a specialist predator. The parameters responsible for the Allee effect and cannibalism impact both the existence and stability of coexistence steady states of the temporal system. The temporal system exhibits various kinds of local bifurcations such as saddle–node, Hopf, Generalized Hopf (Bautin), Bogdanov–Takens, and global bifurcation like homoclinic, saddle–node bifurcation of limit cycles. For the model with self-diffusion, we establish the non-negativity and prior bounds of the solution. Subsequently, we derive the theoretical conditions in which self-diffusion leads to the destabilization of the interior equilibrium. Additionally, we explore the conditions under which cross-diffusion induces the Turing-instability where self-diffusion fails to do so. Further, we present different kinds of stationary and dynamic patterns on varying the values of diffusion coefficients to depict the spatio-temporal model’s rich dynamics. It has been found that the addition of self and cross-diffusion in a prey–predator model with the Allee effect in prey and cannibalistic predator play essential roles in comprehending the pattern formation of a distributed population model. It is expected that the comprehensive mathematical analysis and extensive numerical simulations used in investigating the global dynamics of the proposed model can facilitate researchers in studying the temporal and spatial aspects of prey–predator models in more significant detail.

SSA2py: A High-Performance Python Implementation of the Source-Scanning Algorithm for Spatiotemporal Seismic Source Imaging

Abstract This article introduces the first version of SSA2py (v.1.0)—an open-source package designed to implement the source-scanning algorithm (SSA). SSA2py is a Python-based, high-performance-oriented package that incorporates the SSA method, which has been effectively applied to numerous earthquakes for imaging the spatiotemporal behavior of the seismic source. The software supports a wide range of data and metadata resources. These include the International Federation of Digital Seismograph Networks Web Services, the SeedLink protocol, and others, ensuring optimal access to waveforms and station metadata. Furthermore, the code may evaluate the quality of accessible waveforms using signal analysis methods, allowing for the most appropriate data selection. The SSA method has been computationally optimized using multiprocessing techniques for efficient central processing unit and graphic processing units executions, enabling considerably accelerated computational processes even for large-scale grid searches. The program is also designed to provide statistical and methodological uncertainties for the executed cases through jackknife, bootstrap, and backprojection array response function tests. After appropriate tuning by the user, SSA2py can be used for detailed earthquake source studies that backprojection technique typically serves as a complementary output to the source inversion result or as a near-real-time tool for successful and quick identification of the style and complexity of the earthquake rupture. With a wide and flexible configuration, the user has complete control over all calculating aspects of SSA2py. This article provides a detailed description of the structure and capabilities of this new package, and its reliability is demonstrated through targeted applications to the 2004 Mw 6.0 Parkfield and 2019 Mw 7.1 Ridgecrest earthquakes. Furthermore, the computational efficiency of SSA2py is validated through rigorous performance tests.

Hidden Follower Detection: How Is the Gaze-Spacing Pattern Embodied in Frequency Domain?

Spatiotemporal social behavior analysis is a technique that studies the social behavior patterns of objects and estimates their risks based on their trajectories. In social public scenarios such as train stations, hidden following behavior has become one of the most challenging issues due to its probability of evolving into violent events, which is more than 25%. In recent years, research on hidden following detection (HFD) has focused on differences in time series between hidden followers and normal pedestrians under two temporal characteristics: gaze and spatial distance. However, the time-domain representation for time series is irreversible and usually causes the loss of critical information. In this paper, we deeply study the expression efficiency of time/frequency domain features of time series, by exploring the recovery mechanism of features to source time series, we establish a fidelity estimation method for feature expression and a selection model for frequency-domain features based on the signal-to-distortion ratio (SDR). Experimental results demonstrate the feature fidelity of time series and HFD performance are positively correlated, and the fidelity of frequency-domain features and HFD performance are significantly better than the time-domain features. On both real and simulated datasets, the accuracy of the proposed method is increased by 3%, and the gaze-only module is improved by 10%. Related research has explored new methods for optimal feature selection based on fidelity, new patterns for efficient feature expression of hidden following behavior, and the mechanism of multimodal collaborative identification.

Recreational use of protected areas: spatiotemporal insights from the Wikiloc mobile app

ABSTRACT Managing the rising number of visitors to protected areas (PAs) without compromising their environmental goals is a critical challenge that requires a complete understanding of how and when visitors use these spaces. Georeferenced data from fitness-tracking apps can provide detailed information about the visitor’s spatiotemporal behaviour inside PAs, but its potential is still underexplored. This study uses georeferenced data, from Wikiloc, to analyse the visitors’ spatiotemporal behaviour in Alvão Natural Park, Portugal. Our findings show that: 52% of the visitors’ use is concentrated around the Fisgas de Ermelo Waterfall; photos’ hotspots coincide with the waterfalls and viewpoints facing them; 2% of the routes crossed a zone where recreational use is illegal; Spring (26%) and April (11%) receive the most visitors; 52% of routes are done on the weekends; 66% of visitors are inside the park during noon; start/end places are located near parking spaces; the most frequent times to start a route are between 08:00-13:00 hrs, ending between 13-18:00 hrs. A new method was developed to calculate daily visitor numbers without photo concentration bias. These results offer valuable insights that can aid in PAs’ environmental and recreational use management by quickly providing detailed and low-cost visitor monitoring data.

Membrane Catalyzed Formation of Nucleotide Clusters and Their Role in the Origins of Life: Insights from Molecular Simulations and Lattice Modeling.

One of the mysteries in studying the molecular "Origin of Life" is the emergence of RNA and RNA-based life forms, where nonenzymatic polymerization of nucleotides is a crucial hypothesis in formation of large RNA chains. The nonenzymatic polymerization can be mediated by various environmental settings, such as cycles of hydration and dehydration, temperature variations, and proximity to a variety of organizing matrices, such as clay, salt, fatty acids, lipid membrane, and mineral surface. In this work, we explore the influence of different phases of the lipid membrane toward nucleotide organization and polymerization in a simulated prebiotic setting. Our molecular simulations quantify the localization propensity of a mononucleotide, uridine monophosphate (UMP), in distinct membrane settings. We perform all-atom molecular dynamics (MD) simulations to estimate the role of the monophasic and biphasic membranes in modifying the behavior of UMPs localization and their clustering mechanism. Based on the interaction energy of mononucleotides with the membrane and their diffusion profile from our MD calculations, we developed a lattice-based model to explore the thermodynamic limits of the observations made from the MD simulations. The mathematical model substantiates our hypothesis that the lipid layers can act as unique substrates for "catalyzing" polymerization of mononucleotides due to the inherent spatiotemporal heterogeneity and phase change behavior.

Designing outdoor emergency rescue stations based on the spatiotemporal behavior of outdoor adventure tourists using GPS trajectory data

While outdoor travel has become an increasingly popular phenomenon, its high accident rate remains a significant concern. It is important to reasonably design emergency rescue stations based on the behavioral characteristics of outdoor adventure tourists. However, previous studies have mainly relied on static population information, which cannot capture adventure tourists’ spatiotemporal behaviors. To overcome this disadvantage, we obtained the GPS trajectory data of adventure tourists in Shenzhen from the “2bulu” platform, covering three types of travel groups: hiking, cycling, and driving. The seasonal characteristics, intra-day activities, and trajectory distributions of outdoor adventure tourists were analyzed. On this basis, we used a genetic algorithm to determine the optimal locations of rescue stations, taking into account both socioeconomic and natural conditions. The results show that outdoor adventure tourists have seasonal preferences, with the majority traveling during the summer and being most active between 5 and 9 a.m. The travel trajectories of these tourists exhibited a linear distribution pattern with several hotspots. In addition, the optimal rescue stations are mainly distributed at the junctions of districts, which can cover most outdoor scenic spots in Shenzhen. These findings could provide decision support for ensuring the safety of outdoor adventures.

Spatiotemporal Reaction Dynamics Control in Two‐Photon Polymerization for Enhancing Writing Characteristics

AbstractSince 2001, 3D microfabrication based on two‐photon polymerization (TPP) has drawn extensive attention and interest in biology, optics, photonics, material science, and high‐energy physics. The in‐volume fabrication capability due to the threshold behavior of two‐photon absorption enables TPP higher flexibility compared with other nanofabrication techniques. However, as determined by the in‐volume fabrication feature as well as various reaction dynamics, the writing characteristics of TPP, such as throughput, accuracy, surface quality, and fabrication capability, are still limited. Herein, a comprehensive study is performed on the spatiotemporal behavior of reaction dynamics during TPP fabrication, mainly focusing on spatiotemporal characteristics of radical diffusion, photothermal effect, microscale mechanics, and voxel stacking process. Based on the study, a nonsequential fabrication method is established to simultaneously improve key writing characteristics of TPP and realize sharp features, high speeds, large overhang structure, and smooth surfaces. The method established in this work can be applied to improve the performance of functional devices for various fields.

Optimized ST-Moran’s I Model for Characterizing the Dynamic Evolution of Terminal Airspace Congestion

This study aims to unveil the spatiotemporal evolution of congestion within terminal airspace, offering an in-depth analysis of congestion concerns to effectively utilize airspace resources and devise targeted control strategies, thereby enhancing airspace operation safety and efficiency. Initially, converting segment flow rates into equivalent speeds serves as a quantitative benchmark for operational status. Subsequently, an enhanced version of the ST-Moran’s I index model, specifically tailored to terminal airspace, is developed by incorporating improvements across spatial weight matrices, standard state parameters, and temporal dimensions. Validating this model with actual operational data from Chengdu’s terminal airspace, the research demonstrates significant advancements. Compared to conventional models, the proposed model enhances recognition rates for congestion in spatial and temporal dimensions by 62.5% and 43.61%, respectively. Congestion within terminal airspace predominantly occurs at the intersection of departure-climb and approach-departure segments, exhibiting evident spatiotemporal migration behavior. The proposed model accurately delineates the spatiotemporal characteristics of segment congestion, offering support for tailored congestion management strategies.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.