Modeling Approaches Research Articles

Dynamic modeling and analysis of compressed air energy storage for multi-scenario regulation requirements

Compressed air energy storage (CAES) technology has received widespread attention due to its advantages of large scale, low cost and less pollution. However, only mechanical and thermal dynamics are considered in the current dynamic models of the CAES system. The modeling approaches are relatively homogeneous. CAES power stations have gradually increased the demand for auxiliary services such as frequency modulation mode and voltage regulation mode in addition to the peak regulation mode. Therefore, it is urgent to pay attention to the dynamic response characteristics at shorter time scales. Due to the lack of an accurate and complete dynamic model of the CAES system for multi-scenario adaptation requirements, the development of system optimization design and control technology has been severely limited. The paper establishes a dynamic model of advanced adiabatic compressed air energy storage (AA-CAES) considering multi-timescale dynamic characteristics, interaction of variable operating conditions and multivariate coordinated control. The simulation data is compared with the measured data of the peak regulation, frequency regulation and voltage regulation scenarios of the Jintan Salt Cave CAES (JTSC-CAES). The results show that the deviation of the dynamic model is <7 %, which verifies the validity and accuracy of the proposed AA-CAES dynamic model. The established model and research results provide the theoretical model foundation for dynamic characteristic research, system design and optimization control of the JTSC-CAES.

Turbulent diffusion and air pollution: A comprehensive review of mechanisms, impacts, and modeling approaches

Turbulent diffusion is often a desirable phenomenon in many contexts, but it also presents significant challenges, particularly in the realm of air pollution. Air pollution, a universally recognized and frowned upon issue, degrades air quality and poses serious long-term health risks. Turbulent flows, characterized by chaotic and random fluid motions, give rise to turbulent diffusion, a process that acts as a transport mechanism for dispersing pollutants in the atmosphere. Despite its inherent complexities, the study of turbulent diffusion has advanced significantly, building on foundational concepts like Osborne Reynolds' decomposition in the 1850s and extending to Kolmogorov's length scale framework. These developments have enabled a deeper understanding of turbulent diffusion and its wide-ranging applications across various fields as new methods are continuously employed to broaden the scope of research and application.

Editor’s Note: Special Issue on Computer Vision Approaches for Animal Tracking and Modeling 2023

Editor’s Note: Special Issue on Computer Vision Approaches for Animal Tracking and Modeling 2023

Abstract PR001: A comparison of modeling approaches to assess the interplay of social, behavioral, and environmental factors on lung cancer racial disparities

Abstract PR001: A comparison of modeling approaches to assess the interplay of social, behavioral, and environmental factors on lung cancer racial disparities

Comprehensive Review of Traffic Modeling: Towards Autonomous Vehicles

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling.

No-tillage farming enhances widespread nitrate leaching in the US Midwest

Abstract Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture.

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

Contextualized Metabolic Modelling Revealed Factors Affecting Isoflavone Accumulation in Soybean Seeds.

Isoflavones, secondary metabolites with numerous health benefits, are predominantly found in legume seeds, especially soybean; however, their contents in domesticated soybean seeds are highly variable. Wild soybeans are known for higher seed isoflavone contents than cultivars. Here we used experimental and modelling approaches on wild soybean (W05) and cultivated soybean (C08) to delineate factors influencing isoflavone accumulation. We found imported nutrients were converted into storage compounds, with isoflavone accumulation in W05 seeds being faster than in C08 ones. The isoflavone accumulation during seed development was simulated using context-specific cotyledon metabolic models of four developmental stages on cultivar C08, and the metabolic burden imposed by increasing biomass was evaluated. Trade-off analyses between biomass and isoflavone suggest that high biomass requirement in cultivars could limit the reallocation of resources for secondary metabolite production. Isoflavone production in mature seeds was also influenced by biomass compositions. Seeds with higher carbohydrate contents favour isoflavone production, while those with highest protein and oil contents had lowest isoflavone contents. Although seeds could synthesize isoflavones on their own, the predicted fluxes from biosynthesis alone were lower than the empirical levels. Shadow price analyses indicated that isoflavone accumulation depended on both intrinsic biosynthesis and direct contribution from the plant.

An integrated experiment-modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks.

Abstract Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramafic rocks. However, unlike saline aquifers and depleted oil and gas reservoirs historically considered for GCS, these formations can have extremely low porosities and permeabilities, limiting storage volumes and reactive mineral surfaces to the pre-existing fracture network. As a result, coupling between geochemical interactions and the fracture network evolution is a critical component of long-term, sustainable carbon storage. In this paper, we summarize recent advances in integrating experimental and modeling approaches to determine the first order processes for carbon mineralization in a fractured mafic/ultramafic rock system. We observe the critical role of fracture aperture, flow, and surface characteristics in controlling the quantity, identity, and morphology of secondary precipitates and present where the influence of these factors can be reflected in newly developed thermo-hydro-mechanical-chemical models. Our findings provide a roadmap for future work on carbon mineralization, as we present the most important system components and key challenges that we are overcoming to enable GCS in mafic and ultramafic rocks.

Stochastic Block Smooth Graphon Model

In this article, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are generally used for clustering networks into groups of actors with homogeneous connectivity behavior. Smooth graphon models, on the other hand, assume that all nodes can be arranged on a one-dimensional scale such that closeness implies a similar behavior in connectivity. Both frameworks belong to the class of node-specific latent variable models, entailing a natural relationship. While these two modeling concepts have developed independently, this article proposes their generalization toward stochastic block smooth graphon models. This combined approach enables to exploit the advantages of both worlds. Employing concepts of the EM-type algorithm allows to develop an appropriate estimation routine, where MCMC techniques are used to accomplish the E-step. Simulations and real-world applications support the practicability of our novel method and demonstrate its advantages. The article is accompanied by supplementary material covering details about computation and implementation.

Building performance modelling approaches for a detached vertical green trellis: A case study in a tropical climate

Passive strategies involving greenery significantly increase energy performance in buildings and comfortable microclimate conditions. However, few studies model and simulate their effect on buildings’ energy performance. Thus, this work assesses modelling approaches for conducting building performance simulations where detached vertical green trellises (DVGT) are included. The DVGT characteristics are modelled by: (i) large solid component blocks and (ii) small opaque solid component blocks to form a grid. A building with glazed façades is evaluated through dynamic simulation under the tropical climate of Panama City, using DesignBuilder. Parametric analysis is performed to study the impact of the trellis configuration on the performance in reducing the annual cooling, lighting, and total electricity consumption. A cost-effective evaluation is also conducted based on the net present value for each trellis configuration. Results showed strong agreement with previous studies reporting significant cooling needs reduction while increasing lighting needs and promising return periods. This concludes that the correct optical and radiative properties of the vegetation layer that are wanted to be modelled in a detached vertical trellis are crucial.

DivIVA controls the dynamics of septum splitting and cell elongation in Streptococcus pneumoniae.

Bacterial shape and division rely on the dynamics of cell wall assembly, which involves regulated synthesis and cleavage of the peptidoglycan. In ovococci, these processes are coordinated within an annular mid-cell region with nanometric dimensions. More precisely, the cross-wall synthesized by the divisome is split to generate a lateral wall, whose expansion is insured by the insertion of the so-called peripheral peptidoglycan by the elongasome. Septum cleavage and peripheral peptidoglycan synthesis are, thus, crucial remodeling events for ovococcal cell division and elongation. The structural DivIVA protein has long been known as a major regulator of these processes, but its mode of action remains unknown. Here, we integrate click chemistry-based peptidoglycan labeling, direct stochastic optical reconstruction microscopy, and in silico modeling, as well as epifluorescence and stimulated emission depletion microscopy to investigate the role of DivIVA in Streptococcus pneumoniae cell morphogenesis. Our work reveals two distinct phases of peptidoglycan remodeling during the cell cycle that are differentially controlled by DivIVA. In particular, we show that DivIVA ensures homogeneous septum cleavage and peripheral peptidoglycan synthesis around the division site and their maintenance throughout the cell cycle. Our data additionally suggest that DivIVA impacts the contribution of the elongasome and class A penicillin-binding proteins to cell elongation. We also report the position of DivIVA on either side of the septum, consistent with its known affinity for negatively curved membranes. Finally, we take the opportunity provided by these new observations to propose hypotheses for the mechanism of action of this key morphogenetic protein.IMPORTANCEThis study sheds light on fundamental processes governing bacterial growth and division, using integrated click chemistry, advanced microscopy, and computational modeling approaches. It addresses cell wall synthesis mechanisms in the opportunistic human pathogen Streptococcus pneumoniae, responsible for a range of illnesses (otitis, pneumonia, meningitis, septicemia) and for one million deaths every year worldwide. This bacterium belongs to the morphological group of ovococci, which includes many streptococcal and enterococcal pathogens. In this study, we have dissected the function of DivIVA, which is a structural protein involved in cell division, morphogenesis, and chromosome partitioning in Gram-positive bacteria. This work unveils the role of DivIVA in the orchestration of cell division and elongation along the pneumococcal cell cycle. It not only enhances our understanding of how ovoid bacteria proliferate but also offers the opportunity to consider how DivIVA might serve as a scaffold and sensor for particular membrane regions, thereby participating in various cell cycle processes.

Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.

Real-Time Exposure to Negative News Media and Suicidal Ideation Intensity Among LGBTQ+ Young Adults

With a recent surge in anti-lesbian, gay, bisexual, transgender, or queer (LGBTQ+) policies and associated news and media coverage, there is a need to study the association of LGBTQ+ focused news or media exposure and short-term changes in suicidal ideation (SI) among LGBTQ+ youth. To examine within-person direct and indirect associations between exposure to general and LGBTQ+ negative news or media and SI intensity through expectations of rejection. This intensive longitudinal cohort study used a smartphone-based ecological momentary assessment (EMA) protocol wherein participants responded to EMAs 3 times per day for 28 consecutive days. Young adults aged 18 to 24 years who self-identified as LGBTQ+, resided in Tennessee, and had past-year SI and at least mild depression (defined as a score ≥5 on the Patient Health Questionnaire-9) were eligible for inclusion. Participants were recruited through social media advertisements, LGBTQ+ community organizations, and mental and behavioral health clinics. Participants were recruited between March 30, 2023, and August 23, 2023, and data analyses were conducted from August 28, 2023, through April 20, 2024. At each EMA, participants reported on recent exposure to negative news or media. A 3-level categorical exposure variable denoted (1) no news or media exposure (reference); (2) exposure to general negative news or media; and (3) exposure to LGBTQ+ negative news or media. Each EMA also assessed expectations of rejection due to LGBTQ+ identity on a scale of 0 (not at all) through 10 (very much). Three primary outcomes assessed current (ie, "right now") intensity of active SI, passive SI, and self-harm ideation, each measured on a scale from 0 (not at all) to 10 (very strong). Multilevel modeling approaches were used to account for the hierarchical structure of EMA data, with assessments (level 1) nested within people (level 2). Linear mixed models and multilevel mediation models were used to examine within-person associations between exposure to negative news or media and the 3 primary outcomes, as well as the mediating role of expectations of rejection. Of 31 total participants, 22 were assigned female sex at birth (71%), and 16 (52%) self-identified as transgender or gender diverse. The median (IQR) participant age was 21 (18-22) years, and a total of 2189 EMAs were completed, with a median (range) compliance of 90.5% (41.7%-100%). At the within-person level, recent exposure to LGBTQ+ negative news or media was significantly associated with increased active SI (estimate [b], 0.14; 95% CI, 0.04-0.25; P = .009), passive SI (b, 0.23; 95% CI, 0.04-0.41; P = .02), and self-harm ideation (b, 0.13; 95% CI, 0.02-0.23; P = .02). No statistically significant associations were detected for exposure to general negative news or media. In multilevel mediation models, heightened expectations of rejection explained some of the total effect of exposure to LGBTQ+ negative news or media on active SI (23%) and passive SI (37%). This intensive longitudinal cohort study found that SI intensity modestly increased in the hours immediately following exposure to LGBTQ+ negative news or media among LGBTQ+ young adults. These findings have timely implications for research and intervention, particularly within sociopolitical and geographic contexts where news or media coverage about LGBTQ+ topics is intensified.

Assessment of the Relationship Between Anterior Teeth Colour and Skin Colour by assessing different measurement tools in Adult Patients: A Systematic Review

Background: Previous research conducted since 1968 have extensively examined the correlation between tooth color and skin color. The purpose of these studies was to determine whether skin color could effectively influence dentists in choosing tooth color. The objective of this systematic review is to examine the methodologies employed in assessing the relationship between tooth and skin color, and to identify the most precise and dependable approaches for modeling and evaluating this relationship. The systematic review was registered in the PROSPERO (CRD42022356022). This systematic review was done on four databases using PRISMA 2009 search strategy, and selected studies were chosen according to PICO model. JBI Critical Appraisal Checklist was used for quality assessment. Out of 1611 articles that were found in the databases, 10 articles matched the criteria and were selected for this systematic review study. Three main methods were found in the 10 studies: conventional methods, digital methods, conventional-digital (mixed) methods. Digital methods were found to be the more reliable methods for color evaluation. Specific settings should be applied during digital color measuring methods. Conventional and digital assessment methods showed either a negative or a positive significant association between the color of teeth and skin. However, the combined method (digital for tooth and visual for skin) did not show any such relation. It is imperative to conduct and apply research in Artificial Intelligence to forecast tooth color based on knowledge of skin color.

Progress in the Study of Animal Models of Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) has recently been proposed as an alternative term to NAFLD. MASLD is a globally recognized chronic liver disease that poses significant health concerns and is frequently associated with obesity, insulin resistance, and hyperlipidemia. To better understand its pathogenesis and to develop effective treatments, it is essential to establish suitable animal models. Therefore, attempts have been made to establish modelling approaches that are highly similar to human diet, physiology, and pathology to better replicate disease progression. Here, we reviewed the pathogenesis of MASLD disease and summarised the used animal models of MASLD in the last 7 years through the PubMed database. In addition, we have summarised the commonly used animal models of MASLD and describe the advantages and disadvantages of various models of MASLD induction, including genetic models, diet, and chemically induced models, to provide directions for research on the pathogenesis and treatment of MASLD.

A mini-review for identifying future directions in modelling heating values for sustainable waste management.

Global estimations suggest energy content within municipal solid waste (MSW) is underutilized, compromising efforts to reduce fossil CO2 emissions and missing the opportunities for pursuing circular economy in energy consumption. The energy content of the MSW, represented by heating values (HVs), is a major determinant for the suitability of incinerating the waste for energy and managing waste flows. Literature reveals limitations in traditional statistical HV modelling approaches, which assume a linear and additive relationship between physiochemical properties of MSW samples and their HVs, as well as overlook the impact of non-combustible substances in MSW mixtures on energy harvest. Artificial intelligence (AI)-based models show promise but pose challenges in interpretation based on established combustion theories. From the variable selection perspectives, using MSW physical composition categories as explanatory variables neglects intra-category variations in energy contents while applying environmental or socio-economic factors emerges to address waste composition changes as society develops. The article contributes by showing to professionals and modellers that leveraging AI technology and incorporating societal and environmental factors are meaningful directions for advancing HV prediction in waste management. These approaches promise more precise evaluations of incinerating waste for energy and enhancing sustainable waste management practices.

Uncovering mechanisms behind Chennai's deluges during north-east monsoon season 2015: An observational and modeling analysis

The present study delves into the underlying processes responsible for the Chennai deluge during 9th, 15th of November, and 1st December, 2015 by employing both observational data and modeling approaches. The Chennai rainfall, as observed from the GPM satellite data, was substantially higher than the accumulated normal of ∼79 cm for the October-December period. These extreme events coincided with the strongest El Niño event of the century, which persisted from 2014 to early 2016. Further, it is found that the anomalies in Sea Surface Temperature (SST) during this period were more than 1° K above the climatological value and prevailing strong low-level easterly waves over the Indian Oceanic region aided the intensification of previously developing synoptic systems. Soil moisture analysis indicated saturation values nearing 70 %, resulting in increased surface runoff during rainfall events in the backdrop of rapid urban expansion from 1995 to 2015 and aggravated water logging issues. Calculation of thermodynamic indices revealed favourable conditions for the development and intensification of severe convective systems, leading to the catastrophic rainfall events over the Chennai region. A high resolution regional model NCUM (resolution ∼1.5 Km) was utilized to simulate various synoptic features and dynamics of the event over Chennai. Moisture transport at 700 hPa and integrated precipitable water up to 300 hPa were examined, revealing a strong convergence of moisture along the Chennai coast for all cases, with high values of precipitable water observed. Simulations of 3-hourly accumulated rainfall from model generally align with corresponding GPM satellite estimates, despite the model tending to underestimate the intensity of rainfall in all cases. The model simulated location specific rainfall is reasonably well matched with the in-situ observations around Chennai region. However, the model is underestimated the peak rainfall while compare with the observations in all the cases. Further, it successfully depicts the dynamics and structure of extreme rainfall events, including key features such as wind patterns and moisture convergence, demonstrating its utility for forecasting extreme weather events in the Chennai region.

Applying the water quality indices and geographic information system approach to assessing the suitability of groundwater quality for drinking and irrigation purposes in the semi-arid region of Tebessa-Ain Chabro, Northeastern Algeria

Assessing the groundwater quality is important for the efficient exploitation of water resources in semi-arid areas. The study area in northeast Algeria mostly depends on groundwater as its main source of water, and the quality of groundwater is becoming important due to the increasing need for freshwater. The hydrochemical characteristics and water quality of groundwater in the Tebessa-Ain Chabro were assessed using water quality indices, geochemical modeling, and multivariate statistical approaches. The study discovered that the groundwater samples could be classified into four distinct water groups using hierarchical cluster analysis in Q mode (HCA) based on their electrical conductivity. We identified three forms of water: mixed (Ca2+–Mg2+–Cl−), Na+–K+–HCO3−, and Ca2+–Cl−. According to the water quality assessment, only 38% of the samples were deemed suitable for human consumption, while 34% were categorized as poor water, 10% as extremely poor, and 17% as unsafe for drinking. The irrigation water quality index identified four classifications: low, moderate, high, and severe restriction, with corresponding percentages of 31%, 31%, 7%, and 31%. The nitrate pollution index (NPI) showed that 48% of samples fell into the moderate pollution class. Human activity, such as sewage infiltration and waste disposal in open areas, was the cause of this nitrate contamination. The saturation index values showed that groundwater was less saturated in halite and sylvite and more saturated in aragonite, calcite, dolomite, anhydrite, gypsum, and hydroxyapatite. The Tebessa region’s groundwater’s hydrochemical properties and water quality have been assessed using multivariate statistical techniques, geochemical modeling, and water quality indexes. The study’s conclusions can provide a foundation for upcoming research evaluating the region’s groundwater quality.

Video Anomaly Detection via self-supervised and spatio-temporal proxy tasks learning

Video Anomaly Detection (VAD) aims to identify events in videos that deviate from typical patterns. Given the scarcity of anomalous samples, previous research has primarily focused on learning regular patterns from datasets exclusively containing normal behaviors, and treating deviations from these patterns as anomalies. However, most of these methods are constrained by coarse-grained modeling approaches that renders them incapable of learning highly-discriminative features, which are necessary to effectively distinguish between the subtle differences between normal and abnormal behaviors. To better capture these features, we propose an innovative method. Initially, pseudo-anomalous samples for appearance and motion are generated through geometric transformations (2D rotations) and the scrambling of video sequences. Subsequently, a dual-branch network featuring spatio-temporal decoupling is proposed, in which the spatial and temporal branches each handle a specific proxy task. These tasks are designed to distinguish between normal and pseudo-anomalous samples, involving operations such as predicting patch-based 2D rotation angles and classifying video frame triplets as total-anomaly, left-anomaly, right-anomaly, and non-anomaly. Our approach employs an end-to-end training methodology, without relying on pre-trained models (except for the object detector). Evaluations on the UCSD Ped2, Avenue, and ShanghaiTech datasets show that our method achieved AUC scores of 99.1%, 91.9%, and 81.1%, respectively, demonstrating its effectiveness. The code is publicly accessible at the following link: https://spatio-temporal-tasks.