Modeling Approach Research Articles

Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling.

In this paper, we propose FGPR: a Federated Gaussian process ( GP) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local computations. Notably, the resulting global model excels in personalization as FGPR jointly learns a shared prior across all devices. The predictive posterior is then obtained by exploiting this shared prior and conditioning on local data, which encodes personalized features from a specific dataset. Theoretically, we show that FGPR converges to a critical point of the full log-marginal likelihood function, subject to statistical errors. This result offers standalone value as it brings federated learning theoretical results to correlated paradigms. Through extensive case studies on several regression tasks, we show that FGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.

Streamflow prediction in ungauged basins: How dissimilar are drainage basins?

Hydrological processes leading to flow in river channels are extremely complex, which is why we cannot use any fundamental law to fully explain river flow dynamics at basin scale. The general approach for hydrological modelling considers that every catchment’s response to precipitation input is unique, which means different catchments respond differently to the same precipitation input. In other words, river basins are sufficiently dissimilar to warrant having hydrological models with multiple free-parameters for predicting discharge. It is assumed that free-parameters are related to static catchment characteristics. However, past efforts to relate model parameters with observable catchment characteristics have not been very successful. We thus need a framework to quantify catchment dissimilarities exhibited by catchment characteristics because there is no need of free-parameters if we decide to ignore dissimilarities. Thus, relative role of climate and catchment characteristics in streamflow response of the basin needs to be quantified. In this study, we have considered daily data from 3534 gauging stations spread across the globe to compare HBV, a widely used hydrological model, with Dynamic Budyko (DB) model, a recently proposed calibration-free model. The HBV model assumes that both climate and catchment characteristics are important in modeling streamflow response, whereas a calibration-free DB model essentially assumes that streamflow response is largely governed by climate. The performance of HBV run with regionalized parameters and DB in terms of median coefficient of determination (R2) and weighted R2 (WR2) are (0.43, 0.45) and (0.24, 0.27), respectively. The corresponding values of R2 and WR2 from quantile-quantile comparison are (0.94, 0.94) and (0.60, 0.59). Results here seem to support of idea of developing calibration-free models by appreciating similarities between basins, particularly if the objectives are prediction in ungauged basins and assessment of climate change impact on streamflow.

Managing deepsea oil spills through a systematic modeling approach

Offshore oil exploration and production in deepwater are associated with environmental risks to marine ecosystems. This research introduces DWOSM (Deep Water Oil Spill Model), a three-dimensional Lagrangian model, which is developed to simulate the transport and fate of oil spills resulting from subsea blowouts. DWOSM comprises three interconnected modules: DWOSM-DSD, which predicts the oil droplet size distribution from a blowout release; DWOSM-NearField, simulating plume dynamics and tracking oil droplets within the plume region; and DWOSM-FarField, modeling the evolution of dispersed oil beyond the near-field. Compared to other oil spill models, this integrated approach improves the transition between near and far fields using a near-field particle tracking algorithm. It also employs the thermodynamic models to enable the prediction of oil properties under varying deep water pressure and temperature. To gauge the reliability and efficacy of DWOSM, a hypothetical case situated within a North American context is employed for model testing. The DWOSM and its each module are juxtaposed with other established oil spill models. The outcomes indicate that DWOSM yields comparable results to these models by providing reasonable predictions of a deepwater blowout. The model's verification through case scenario testing and comparison underscores its potential as a decision tool for assessing and managing the potential environmental impacts of offshore petroleum activities.

Naturally felt emotions, work engagement: the moderating role of perceived organizational support

The main purpose of this study is to investigate whether or not emotional labor strategy (i.e., naturally felt emotions) significantly predicts work engagement (i.e., vigor, dedication, absorption) among academicians in a higher education setting. It also evaluates the role of perceived organizational support (POS) as the moderating variable between naturally felt emotions (NFE) and work engagement. This study employs a survey questionnaire, and data collected from private universities in Central Java, Indonesia. To analyze the data and test the proposed model, the partial least squares structural equation modelling approach was employed. The findings demonstrate that: i) NFE are significantly and positively related to all work engagement dimensions; and ii) POS is proven insignificant to moderate the relationship between NFE and work engagement dimensions. The results of this study complement the literature by addressing associations between NFE and work-related outcomes (i.e., work engagement), especially in the Indonesian academic context. In addition, this study also confirms the insignificant role of POS which is based on norms of mutuality between the organization and employee. Finally, the study offers recommendations and conclusions to achieve long-term objectives for higher education institutions.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Teamwork self-efficacy and employee satisfaction: a sequential framework to build employee cohesion

The occupational psychological health of the employees is affected by the behavior of their peers in the workplace. To reduce psychological stress and improve the well-being of employees, organizations are focusing on developing coherence through various human resources practices. In the current business scenario, it is crucial to address psychological stress by building employee cohesion. The aim of this study is to analyze how employees’ teamwork self-efficacy influences group cohesion through employee satisfaction. This study followed a quantitative approach to test the data, which comprises 228 information technology (IT) professionals, working in top IT companies in India. This study used a partial least squares structural equation modeling approach to test the hypothesis. The results revealed that employees’ teamwork self-efficacy is not directly influencing employee cohesion, whereas it has a significant positive influence on an employee’s cohesion only with the mediation effect of an employee’s satisfaction. Predominantly, an employee’s extrinsic satisfaction has a greater influence on the aforesaid relationship than an employee’s intrinsic satisfaction. The results of this study suggest human resources development practitioners place more emphasis on delivering motivational benefits and providing collaboration-oriented interventions that strengthen teamwork self-efficacy, since these factors may directly affect employee cohesion given that a large portion of the respondents were young adults.

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Development Secured Blockchain Based on Agricultural Food Supply Chain in Management System

Abstract: Blockchain, smart contracts, and the Internet of Things (IoT) are essential technologies for the business process reengineering of supply chains in the era of Industry 4.0. The agricultural food supply chain is one of the research areas where these disruptive technologies can play a crucial role in automating business processes, providing real-time goods monitoring, and securing transactions. With the help of blockchain, smart contracts, and the IoT, a product’s health and environment can be monitored throughout the supply chain. In this study, we have critically examined the relevance of these technologies through various activities of the agriculture supply chain using the approach of Business Process Modeling (BPM). Agriculture is a big part of the economy of any country because it helps feed the entire population. It connects and communicates with all of the related industries. If the agriculture base is strong, it is generally regarded as a socially and politically stable society. Many modern farms make use of cutting-edge technology and sciatic and technological ideas. The maximization of the profits relies on some farmers’ vegetables and fruits with chemicals. Chemical fertilizers, insecticides, and other compounds are used in several plants and fruits.

The mediating effect of sleep duration on metabolic syndrome severity in adults: a structural equation modeling approach

BackgroundMetabolic syndrome (MetS) is associated to sleep duration. It is crucial to identify factors that disrupt sleep regulation. The study aimed to assess the indirect effect of risk factors related to MetS severity through sleep duration by utilizing a structural equation model (SEM).MethodsThe study involving 3,935 adults from the baseline data of the Ravansar Non-Communicable Disease (RaNCD) cohort study. MetS severity scores were the outcome variables. SEM was employed to explore the relationships, utilizing IBM SPSS and AMOS version 23.ResultsThe mean MetS severity score was higher in women compared to men (0.25 vs. 0.16, P = 0.003). In men, socioeconomic status (SES) has a positive direct effect (β = 0.048) and a negative indirect effect (β=-0.006) on MetS severity. Increased physical activity is directly (β=-0.036) and indirectly (β=-0.093) associated with reducing MetS severity. Nap duration is directly linked to an increase (β = 0.072) but has an indirect effect (β=-0.008) in decreasing MetS severity. In women, SES has a direct (β=-0.020) and indirect (β=-0.001) inverse relationship with MetS severity. Increased physical activity is directly (β=-0.048) and indirectly (β=-0.036) associated with decreasing MetS severity in women. Nap duration is directly associated with an increase in MetS severity (β=-0.018) but indirectly contributes to its reduction (β=-0.002). Sleep duration not only directly affects MetS severity but is also influenced by age, SES, physical activity, obesity and nap duration.ConclusionPhysical activity, SES, and nap duration directly and indirectly effect the MetS severity. Sleep duration was recognized as a mediating variable that supports the indirect effects.

Dynamic interconnections between career engagement and perceived employability among recent graduates: a latent change score modeling approach

PurposeThe purpose of this paper is to examine career engagement and perceived employability during the school-to-work transition. We studied within-person changes in career engagement and perceived employability in the transition from higher education to the labor market. We investigated their dynamic reciprocal relationship to unravel whether career engagement or perceived employability is the leading indicator in the relationship in view of providing adequate support for students during the school-to-work transition.Design/methodology/approachWe conducted latent change score (LCS) analyses on a three-wave sample of 701 graduates in Flanders (Belgium). We collected data in July (right before graduation), November and May. LCS is a novel method allowing to simultaneously test change and reciprocal relationships.FindingsOur findings demonstrated how both career engagement and perceived employability changed (within-person) non-linearly during the school-to-work transition. As for their relationship, we found that perceived employability is the driving force in the relationship. Perceived employability fueled subsequent positive changes in career engagement, whereas career engagement did not lead to subsequent changes in perceived employability.Originality/valueOur study connects the career development and the graduate employability literature, and examines the school-to-work transition from preparation for the labor market to ten months after graduation. We also make an important methodological contribution, demonstrating the added value of LCS for studying employability in higher education. Our findings provide insights in how higher education institutions may support students in the school-to-work transition.

An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies

Research on the influence of flexible wheelset rotation effect on wheel rail contact force

PurposeUnder the high-speed operating conditions, the effects of wheelset elastic deformation on the wheel rail dynamic forces will become more notable compared to the low-speed condition. In order to meet different analysis requirements and selecting appropriate models to analyzing the wheel rail interaction, it is crucial to understand the influence of wheelset flexibility on the wheel-rail dynamics under different speeds and track excitations condition.Design/methodology/approachThe wheel rail contact points solving method and vehicle dynamics equations considering wheelset flexibility in the trajectory body coordinate system were investigated in this paper. As for the wheel-rail contact forces, which is a particular force element in vehicle multibody system, a method for calculating the Jacobian matrix of the wheel-rail contact force is proposed to better couple the wheel-rail contact force calculation with the vehicle dynamics response calculation. Based on the flexible wheelset modeling approach in this paper, two vehicle dynamic models considering the wheelset as both elastic and rigid bodies are established, two kinds of track excitations, namely normal measured track irregularities and short-wave irregularities are used, wheel-rail geometric contact characteristic and wheel-rail contact forces in both time and frequency domains are compared with the two models in order to study the influence of flexible wheelset rotation effect on wheel rail contact force.FindingsUnder normal track irregularity excitations, the amplitudes of vertical, longitudinal and lateral forces computed by the flexible wheelset model are smaller than those of the rigid wheelset model, and the virtual penetration and equivalent contact patch are also slightly smaller. For the flexible wheelset model, the wheel rail longitudinal and lateral creepages will also decrease. The higher the vehicle speed, the larger the differences in wheel-rail forces computed by the flexible and rigid wheelset model. Under track short-wave irregularity excitations, the vertical force amplitude computed by the flexible wheelset is also smaller than that of the rigid wheelset. However, unlike the excitation case of measured track irregularity, under short-wave excitations, for the speed within the range of 200 to 350 km/h, the difference in the amplitude of the vertical force between the flexible and rigid wheelset models gradually decreases as the speed increase. This is partly due to the contribution of wheelset’s elastic vibration under short-wave excitations. For low-frequency wheel-rail force analysis problems at speeds of 350 km/h and above, as well as high-frequency wheel-rail interaction analysis problems under various speed conditions, the flexible wheelset model will give results agrees better with the reality.Originality/valueThis study provides reference for the modeling method of the flexible wheelset and the coupling method of wheel-rail contact force to the vehicle multibody dynamics system. Furthermore, by comparative research, the influence of wheelset flexibility and rotation on wheel-rail dynamic behavior are obtained, which is useful to the application scope of rigid and flexible wheelset models.

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

Human movement and environmental barriers shape the emergence of dengue

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

Second-order water wave properties over Roseau’s three-parameter bottom topography

Summary In his book Asymptotic Wave Theory (North-Holland 1976), Roseau develops the only known exact solution for linear wave propagation over a shoal. Remarkably, almost half a century later, the only properties of that solution which appear to have been used to validate more wide-ranging models are the limiting values of wave height in the far field. The primary aim of this work is to remedy this situation by providing a robust computational setting where the solution may be fully evaluated randomly both on the surface and the interior, speedily and to a high degree of accuracy. Such ‘benchmark’ solutions will facilitate a much more rigorous approach to validation of more general alternative approximation models. One such has some of its numerical results here examined and subsequently largely vindicated by the present calculations. Computation of the absolutely convergent solution integrals is supported by cubic spline approximation of an integrand component which satisfies a certain difference equation and the work is extended to cover bottom profiles with overhang including the extreme case where the overhang degenerates to a semi-infinite flat plate. The further development of solutions at second-order reveals details of mean Eulerian current and the attendant Stokes drift enabling mass transport computations; all illustrated through the entire depth with a range of contouring and arrows plots. The force field that drives a second harmonic component is calculated and shows excellent agreement with its equivalent in the more general modal expansion model applied to a non-identical but somewhat similar profile. This further validates the modelling approach and application described in that work.

Corn Acreage Intensification Levels in U.S. Corn Belt States

Abstract Crop rotations in the United States increasingly involve few crops dominated by corn frequently combined with soybeans. We assess factors tied to corn acreage intensification over the past two decades. Using state-level data of 11 U.S. Corn Belt states from 2000 to 2021, we applied a panel fixed effects instrumental variable modeling approach to investigate these linkages. Findings suggest Conservation Reserve Program acreage releases, crop prices, ethanol demand, farm size, productivity, and genetically modified varieties positively impact corn acreage intensity. These results imply crop planting decisions are complex and are not uniquely attributed to biofuel considerations.

Comparative Evaluation of Boron Sorption Dynamics on Zeolites in Irrigation Waters: An Isothermal Modeling Approach

Efficient boron removal from irrigation waters is crucial for sustainable agriculture, as elevated levels of boron can be toxic to many plants, limiting growth and crop productivity. In this context, the present study investigated the sorption equilibrium of boron using zeolites in two types of aqueous matrices: a synthetic solution containing only boron and natural irrigation waters. Through the application of various isothermal sorption models (Langmuir, Freundlich, Sips, Toth, Jovanovic, Temkin, Dubinin–Radushkevich, and Redlich–Peterson), the efficacy of zeolite for boron removal under controlled and real conditions was evaluated. The results indicated a notable difference in sorption behavior between the two matrices, reflecting the complexity and heterogeneity of interactions in the boron–zeolite system. In the synthetic solution, the Freundlich model provided the best fit (R2 = 0.9917), suggesting heterogeneous and multilayer sorption, while the Sips model showed high efficacy in describing the sorption in both matrices, evidencing its capability to capture the complex nature of the interaction between boron and zeolite under different environmental conditions. However, in natural irrigation waters, the Jovanovic model demonstrated the most accurate fit (R2 = 0.999), highlighting the importance of physical interactions in boron sorption. These findings underscore the significant influence of the water matrix on the efficacy of zeolite as a boron removal agent, emphasizing the need to consider the specific composition of irrigation water in the design of removal treatments. Additionally, the results stress the importance of selecting the appropriate isothermal model to predict boron sorption behavior, which is crucial for developing effective and sustainable treatment strategies. This study provides a basis for optimizing boron removal in various agricultural and industrial applications, contributing to the design of more efficient and specific water treatment processes.

Agricultural land use modeling and climate change adaptation: A reinforcement learning approach

AbstractThis paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling to investigate climate change adaptation strategies. More specifically, we develop and apply a computationally efficient machine learning approach based on reinforcement learning to simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers in Southeast Germany, our results show that a change in climate, market, and policy conditions shifts the spatial distribution of the uptake of agroforestry systems. Our modeling approach can be used to advance currently used models for ex ante policy analysis by upscaling existing knowledge about farmers behavioral characteristics and combine it with spatially explicit environmental and farm structural data. The approach presents a potential solution for researchers who aim to upscale information, potentially enriching and complementing existing land use modeling approaches.