Advanced Modeling Approaches Research Articles

Corrosion-related parameter estimation for RC structures using UKF-based Bayesian nonlinear finite element model updating with seismic data

Model updating based on field measurements (e.g., from ambient vibration) has proven to be an efficient approach for assessing the condition of reinforced concrete (RC) structures. Despite existing studies in the literature, no effort has been devoted to corrosion-related parameter estimation for RC structures based on Bayesian nonlinear finite element model updating (FEMU) with seismic data. To bridge this gap, this research work extends the application of nonlinear FEMU to corroded RC structures and provides a methodology to estimate the corrosion-related parameters from seismic measurement of RC structures with high nonlinearity. The essential idea of this approach is to integrate recursive Bayesian inference (i.e., unscented Kalman filter) with advanced finite element (FE) modeling of corroded RC structures to estimate corrosion-affected properties. The advanced FE modeling approach used in this study is an efficient modeling strategy that accounts for different aspects of the corrosion, particularly corroded bond-slip in RC structures. The capability of the approach in evaluating the corrosion-affected model parameters is examined by 13 case studies using an example RC column with simulated seismic data due to lack of real-word measurements. These study cases consider different corrosion levels, measurement noise levels, corroded features, seismic intensity levels, and types of measurement. The successful estimation of the corrosion-affected properties for the corroded column under various scenarios indicates that the proposed methodology can be employed to identify the unknown corroded properties of RC structures using recorded seismic response data. The updated FE model of a corroded RC structure can potentially be utilized for reliable seismic performance assessment, which assists in future decision-making for repair or retrofit of existing structures.

Three‐Dimensional Thermoporoelastic Modeling of Hydrofracturing and Fluid Circulation in Hot Dry Rock

AbstractGeothermal energy, featured as a renewable low‐carbon energy resource, exhibits great potential in mitigating global warming. However, efficient mining of geothermal energy from hot dry rock remains challenging due to the lack of a thermoporoelastic modeling approach that allows for integrated simulation of hydrofracturing and fluid circulation, and poor understanding of the mechanisms of hydraulic stimulations. To conquer these challenges, we propose an advanced modeling approach, which enables us to deal with the interplay between fluid transport, heat transfer, rock deformation, and three‐dimensional fractures' growth and interaction. Based on the thermoporoelastic model, a refined investigation is performed to determine the hydrofracturing and fluid circulation processes, using data collected from the EGS Collab Experiment 1. This meso‐scale testbed involves creating major hydraulic fractures, accompanied by numerous secondary hydroshearing fractures, hydraulically connecting two horizontal wellbores to build a fluid circulation system. All wellbores in the testbed were heavily instrumented with monitoring devices, offering high‐quality data to properly constrain the geomechanical model. Our study provides insights into: (a) how the EGS Collab testbed responds to propagating fractures with strong stress shadowing and long‐term injection of chilled water; (b) why the parallel pattern of two hydraulic fractures with a distance much smaller than the fracture dimension forms during multi‐staged hydraulic stimulations; and (c) what the mechanisms are that determine hydrofracturing performance. Overall, this work not only contributes to a novel modeling approach for integrated simulation of hydrofracturing and fluid circulation, but advances our understanding of creating enhanced geothermal systems with interconnected fracture networks.

From Protein Sequence to Structure: The Next Frontier in Cross-Species Extrapolation for Chemical Safety Evaluations.

Computational screening for potentially bioactive molecules using advanced molecular modeling approaches including molecular docking and molecular dynamic simulation is mainstream in certain fields like drug discovery. Significant advances in computationally predicting protein structures from sequence information have also expanded the availability of structures for nonmodel species. Therefore, the objective of the present study was to develop an analysis pipeline to harness the power of these bioinformatics approaches for cross-species extrapolation for evaluating chemical safety. The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool compares protein-sequence similarity across species for conservation of known chemical targets, providing an initial line of evidence for extrapolation of toxicity knowledge. However, with the development of structural models from tools like the Iterative Threading ASSEmbly Refinement (ITASSER), analyses of protein structural conservation can be included to add further lines of evidence and generate protein models across species. Models generated through such a pipeline could then be used for advanced molecular modeling approaches in the context of species extrapolation. Two case examples illustrating this pipeline from SeqAPASS sequences to I-TASSER-generated protein structures were created for human liver fatty acid-binding protein (LFABP) and androgen receptor (AR). Ninety-nine LFABP and 268 AR protein models representing diverse species were generated and analyzed for conservation using template modeling (TM)-align. The results from the structural comparisons were in line with the sequence-based SeqAPASS workflow, adding further evidence of LFABL and AR conservation across vertebrate species. The present study lays the foundation for expanding the capabilities of the web-based SeqAPASS tool to include structural comparisons for species extrapolation, facilitating more rapid and efficient toxicological assessments among species with limited or no existing toxicity data. Environ Toxicol Chem 2023;42:463-474. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A Review of Characterization and Modelling Approaches for Sheet Metal Forming of Lightweight Metallic Materials.

Lightweight sheet metals are attractive for aerospace and automotive applications due to their exceptional properties, such as low density and high strength. Sheet metal forming (SMF) is a key technology to manufacturing lightweight thin-walled complex-shaped components. With the development of SMF, numerical simulation and theoretical modelling are promoted to enhance the performance of new SMF technologies. Thus, it is extraordinarily valuable to present a comprehensive review of historical development in SMF followed by state-of-the-art advanced characterization and modelling approaches for lightweight metallic materials. First, the importance of lightweight materials and their relationship with SMF followed by the historical development of SMF are reviewed. Then, the progress of advanced finite element technologies for simulating metal forming with lightweight alloys is covered. The constitutive modelling of lightweight alloys with an explanation of state-of-the-art advanced characterization to identify the constitutive parameters are presented. Then, the formability of sheet metals with major influencing factors, the techniques for measuring surface strains in SMF and the experimental and modelling approaches for determining the formability limits are clarified. Finally, the review is concluded by affording discussion of the present and future trends which may be used in SMF for lightweight metallic materials.

Advanced Modelling and Simulation of Intermetallic Reinforced Composites for Structural and Functional Applications

In recent years, intermetallic reinforced composites (IRCs) have garnered significant attention due to their exceptional mechanical properties, corrosion resistance, and high-temperature stability, making them ideal candidates for both structural and functional applications. This research paper presents an advanced modelling and simulation approach to understand the microstructural evolution, mechanical behaviour, and functional properties of IRCs. Utilizing a combination of finite element analysis (FEA), molecular dynamics (MD), and phase-field modelling, the study offers a comprehensive insight into the intricate interplay between the matrix, reinforcement, and the resultant composite behaviour. The developed models accurately predict the stress-strain response, thermal conductivity, and fatigue life of the IRCs under various loading and environmental conditions. Furthermore, the simulations provide a detailed understanding of the mechanisms governing crack initiation and propagation in these composites. The outcomes of this research not only pave the way for optimizing the design and processing parameters of IRCs but also underscore the potential of these materials in aerospace, automotive, and energy sectors. The findings presented herein serve as a foundational reference for researchers and engineers aiming to harness the full potential of intermetallic reinforced composites in advanced engineering applications.

From Rockfall Observation to Operational Solutions: Nearly 20 years of Cryo-gravitational Hazard Studies in Mont-Blanc Massif

The enhanced rockfall frequency (V > 100 m3) first noticed in the European Alps, has motivated investigations on high mountain rock walls and rock wall permafrost since the 2000s. Mont-Blanc massif (MBM) has become a pilot study area thanks to the wealth of data and knowledge on rockfall and permafrost dynamics acquired through successive research projects. Statistical analysis of rockfall distribution (> 1300 events inventoried between 2007 and 2021) shows that they are related to permafrost distribution and increasing atmospheric temperature, opening promising perspective for developing forecasting tools to support mountain practitioners in their risk mitigation strategy. However, understanding how rock wall destabilizations develop is challenged by the variety of potentially involved thermo-hydro-mechanical processes. To face these challenges, research currently carried out in the MBM focuses on water infiltration and circulation processes in frozen rock walls by combining advanced numerical modelling approaches and ad hoc field surveys. The coupling of models simulating heat and water transport with geoelectrical imaging methods could allow assessment of ground water/ice distributions and contents that is highly relevant for geotechnical purposes. Concurrently, the combination of acquired data and knowledge in integrative approaches of landscape changes and hazards involving permafrost degradation, glacier retreat, possible lake formation and rock wall destabilization appears as an essential basis for land-planning concerns. Finally, thermal and hydrological interactions between rock wall permafrost and ice bodies nestled on rock faces (ice aprons and hanging glaciers) is another direction of research to be pursued with cross-disciplinary implications.

ANTECEDENTS, OUTCOMES, AND CORRELATES OF METABOLIC HEALTH IN OLDER AGE: A MULTIDISCIPLINARY PERSPECTIVE

Abstract Metabolic syndrome (MetS) is a cluster of risk factors for cardiovascular disease associated with reduced physical fitness, higher disease burden, and impaired cognitive functions in late life. Thus investigating antecedents, outcomes and correlates of metabolic health is a priority in order to promote healthier lifestyles and successful aging. This symposium compiles four empirical interdisciplinary studies that examine the role of metabolic health/risk on different timescales. Employing advanced modeling approaches to data obtained in large-scale studies, these reports will shed light on potential antecedents, correlates, and consequences thereof. First, Kalyani and colleagues will report data from the SPRING (Study of Physical Resilience IN Geriatrics) study on the interrelation between metabolic syndrome, glucose intolerance, and physical resilience.Second, Buchmann and colleagues examine the association of the metabolic syndrome and Lipoprotein(a) finding that hormonal aspects and in particular menopausal alterations seem to moderate theassociation between MetS and Lp(a). Third, Demuth and colleagues show how metabolic health is related to specific DNA methylation age acceleration as derived from five different epigenetic clocks, namely PhenoAge, and GrimAge.Fourth, Duezel and colleagues examine the neural correlates of metabolic risk in a sample of older adults finding that lower metabolic risk is linked to greater GMI in the prefrontal cortex. Conjointly, findings demonstrate the multifaceted nature of metabolic risk across adulthood and across different timescales. Walston will critically discuss the contributions from an aging perspective and discuss implications for future research.

Mapping under-five child malaria risk that accounts for environmental and climatic factors to aid malaria preventive and control efforts in Ghana: Bayesian geospatial and interactive web-based mapping methods

BackgroundUnder-five child malaria is one of the leading causes of morbidity and mortality globally, especially among sub-Saharan African countries like Ghana. In Ghana, malaria is responsible for about 20,000 deaths in children annually of which 25% are those aged < 5 years. To provide opportunities for efficient malaria surveillance and targeted control efforts amidst limited public health resources, the study produced high resolution interactive web-based spatial maps that characterized geographical differences in malaria risk and identified high burden communities.MethodsThis modelling and web-based mapping study utilized data from the 2019 Malaria Indicators Survey (MIS) of the Demographic and Health Survey Program. A novel and advanced Bayesian geospatial modelling and mapping approaches were utilized to examine predictors and geographical differences in under-five malaria. The model was validated via a cross-validation approach. The study produced an interactive web-based visualization map of the malaria risk by mapping the predicted malaria prevalence at both sampled and unsampled locations.ResultsIn 2019, 718 (25%) of 2867 under-five children surveyed had malaria. Substantial geographical differences in under-five malaria risk were observed. ITN coverage (log-odds 4.5643, 95% credible interval = 2.4086–6.8874), travel time (log-odds 0.0057, 95% credible interval = 0.0017–0.0099) and aridity (log-odds = 0.0600, credible interval = 0.0079–0.1167) were predictive of under-five malaria in the spatial model. The overall predicted national malaria prevalence was 16.3% (standard error (SE) 8.9%) with a range of 0.7% to 51.4% in the spatial model with covariates and prevalence of 28.0% (SE 13.9%) with a range of 2.4 to 67.2% in the spatial model without covariates. Residing in parts of Central and Bono East regions was associated with the highest risk of under-five malaria after adjusting for the selected covariates.ConclusionThe high-resolution interactive web-based predictive maps can be used as an effective tool in the identification of communities that require urgent and targeted interventions by programme managers and implementers. This is key as part of an overall strategy in reducing the under-five malaria burden and its associated morbidity and mortality in a country with limited public health resources where universal intervention is practically impossible.

Spatial variation of the determinants affecting urban land value in Thiruvananthapuram, India

PurposeLand value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.Design/methodology/approachThe global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.FindingsGlobal regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.Originality/valueThis study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

COVID-19 pandemic and unemployment rate prediction for developing countries of Asia: A hybrid approach.

Unemployment is an essential problem for developing countries, which has a direct and major role in economy of a country. Understanding the pattrens of unemployment rate is critical now a days and has drawn attention of researcher from all fields of study across the globe. As unemployment plays an important role in the planning of a country's monetary progress for policymakers and researcher. Determining the unemployment rate efficiently required an advance modeling approach. Recently,numerous studies have relied on traditional testing methods to estimate the unemployment rate. Unemployment is usually nonstationary in nature. As a result, demonstrating them using traditional methods will lead to unpredictable results. It needs a hybrid approach to deal with the prediction of unemployment rate in order to deal with the issue associated with traditional techniques. This research primary goal is to examine the effect of the Covid-19 pandemic on the unemployment rate in selected countries of Asia through advanced hybrid modeling approach, using unemployment data of seven developing countries of Asian: Iran, Sri Lanka; Bangladesh; Pakistan; Indonesia; China; and India,and compare the results with conventional modeling approaches. Finding shows that the hybrid ARIMA-ARNN model outperformed over its competitors for Asia developing economies. In addition, the best fitted model was utilised to predict five years ahead unemployment rate. According to the findings, unemployment will rise significantly in developing economies in the next years, and this will have a particularly severe impact on the region's economies that aren't yet developed.

Association of Cerebrovascular Reactivity and Cognitive Performance in a Racially Diverse Cohort

AbstractBackgroundCerebrovascular reactivity (CVR) to increased carbon dioxide (CO2) reflects cerebral microvascular function and is impaired in hypertension and stroke. Limited studies have looked at its relationship with cognitive performance in African Americans and Whites.MethodsWe used data from the Brain‐Stress Hypertension and Aging Program cohort (B‐SHARP) consisting of racially diverse individuals with normal cognition or mild cognitive impairment (MCI). CVR was measured using advanced signal modeling approach developed by BH/IH during blood oxygenation level dependent (BOLD) resting state ‐fMRI. Participants underwent cognitive assessment using global and domain‐specific tests such as Montreal Cognitive Assessment (MoCA), Boston Naming Test (BNT), Hopkins Verbal Learning Test‐Revised (HVLT‐R), and Trail Making Test. We used multivariate regression models to assess the relationship of CVR and cognitive domains and tested interaction of CVR with race.ResultsSuccessful CVR data were completed on 219 participants with mean age= 65.3 years (SD 7.8), 46% African American and 65% women. An increased resting whole brain CVR was associated with a higher MoCA score (β = 6.46, SE 2.43, p‐value 0.008), higher BNT score (β =2.15, SE 0.93 p‐value 0.02), and shorter time on Trail B‐A, (β= ‐113.4, SE 42.0, p‐value 0.007) after adjusting for mean arterial pressure. After adjusting for age, sex, and race, increased resting whole brain CVR was associated with higher MoCA (β =4.4, SE 2.26, p‐value 0.05), BNT score (β =1.46, SE 0.89, p‐value 0.1) and Trail B‐A score (β =‐86.7, SE 40.3, p‐value 0.03). There was no association with delayed memory based on HVLT‐R. The results were similar for individuals with normal cognition or MCI. There was a significant interaction by race (p&lt;0.01) such that whole brain CVR was associated with better performance on all cognitive tests for African Americans but not for Whites.ConclusionsHigher CVR, reflected by resting state BOLD‐fMRI, was associated with higher overall cognitive status and specific domains including language and executive functioning in individuals with normal cognition or MCI. The differential effect in African Americans vs Whites could suggest a greater contribution of microvascular dysfunction for the higher prevalence of cognitive impairment in African Americans.

Three-Dimensional Modeling of Soil-Structure Interaction for a Bridge Founded on Caissons under Seismic Conditions

In recent years, the urgent need to increase the safety standards of viaducts and bridges—under static and dynamic loading conditions—has required the development of advanced modeling approaches able to accurately predict the expected behavior of such infrastructures in a reliable manner. This paper presents a comparison between the adoption of a simplified modeling approach, widely used in the current practice, where the response of the structural system neglects the effects of the soil-structure interaction (SSI) phenomenon (considering the base of the structure fixed at the ground surface) and a rigorous modeling approach that considers the full 3D problem with all the components of the system (superstructure, foundation, and soil), through a finite element model. The pier of a real-world viaduct in central Italy was considered, with the aim of starting from a specific case study with foundation characteristics that are frequently found in viaducts in Italy, to obtain results that can be generalized to a wide range of similar types. Its behavior was evaluated both in the dynamic range of small oscillations and in the field of the seismic response to low and strong motion events. The results show that, in terms of seismic demand, the fixed-based model appears more conservative, but it significantly underestimates both elastic and residual displacements and rotations

Asia-Pacific ICEMR: Understanding Malaria Transmission to Accelerate Malaria Elimination in the Asia Pacific Region.

Gaining an in-depth understanding of malaria transmission requires integrated, multifaceted research approaches. The Asia-Pacific International Center of Excellence in Malaria Research (ICEMR) is applying specifically developed molecular and immunological assays, in-depth entomological assessments, and advanced statistical and mathematical modeling approaches to a rich series of longitudinal cohort and cross-sectional studies in Papua New Guinea and Cambodia. This is revealing both the essential contribution of forest-based transmission and the particular challenges posed by Plasmodium vivax to malaria elimination in Cambodia. In Papua New Guinea, these studies document the complex host-vector-parasite interactions that are underlying both the stunning reductions in malaria burden from 2006 to 2014 and the significant resurgence in transmission in 2016 to 2018. Here we describe the novel analytical, surveillance, molecular, and immunological tools that are being applied in our ongoing Asia-Pacific ICEMR research program.

Three‐dimensional fatigue crack growth simulation and fatigue life assessment based on finite element analysis

AbstractDamage tolerance design allows manufactures to assess fatigue crack propagation of components containing initial defects. A new modeling approach is proposed to carry out a systematic crack growth analysis based on an analytical method using reduced order models and three‐dimensional (3D) finite element (FE)‐based fatigue crack growth (FCG) analysis. 3D FE‐based FCG analysis is adopted as an advanced modeling approach for component geometries without any simplifications with respect to crack front shape or planarity of the crack path. The FE‐based FCG approach was verified and validated in two different stages: Ti‐6Al‐4V plate geometry with three different single elliptical crack configurations and Al 2024‐T3 specimens with two different multiple crack configurations. The analytical FCG approach was developed to estimate crack front evolution and crack growth life for three different elliptical cracks to verify the FE‐based approach. Experimental data of Al 2024‐T3 specimens with multi‐edge cracks was used to further validate the FE‐based FCG approach. The FE‐based FCG approach proved to be both accurate and powerful in assessing crack propagation life and crack paths of structural parts.

Advanced asteroseismic modelling: breaking the degeneracy between stellar mass and initial helium abundance

ABSTRACT Current stellar model predictions of adiabatic oscillation frequencies differ significantly from the corresponding observed frequencies due to the non-adiabatic and poorly understood near-surface layers of stars. However, certain combinations of frequencies – known as frequency ratios – are largely unaffected by the uncertain physical processes as they are mostly sensitive to the stellar core. Furthermore, the seismic signature of helium ionization provides envelope properties while being almost independent of the outermost layers. We have developed an advanced stellar modelling approach in which we complement frequency ratios with parameters of the helium ionization zone while taking into account all possible correlations to put the most stringent constraints on the stellar internal structure. We have tested the method using the Keplerbenchmark star 16 Cyg A and have investigated the potential of the helium glitch parameters to constrain the basic stellar properties in detail. It has been explicitly shown that the initial helium abundance and mixing-length parameters are well constrained within our framework, reducing systematic uncertainties on stellar mass and age arising for instance from the well-known anticorrelation between the mass and initial helium abundance. The modelling of six additional Kepler stars including 16 Cyg B reinforces the above findings and also confirms that our approach is mostly independent of model uncertainties associated with the near-surface layers. Our method is relatively computationally expensive, however, it provides stellar masses, radii and ages precisely in an automated manner, paving the way for analysing numerous stars observed in the future during the ESA PLATO mission.

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges.

Among the different thermo-chemical recycling routes for plastic waste valorization, gasification is one of the most promising, converting plastic waste into syngas (H2+CO) and energy in the presence of an oxygen-rich gas. Plastic waste gasification is associated with many different complexities due to the multi-scale nature of the process, the feedstock complexity (mixed polyolefins with different contaminations), intricate reaction mechanisms, plastic properties (melting behavior and molecular weight distribution), and complex transport phenomena in a multi-phase flow system. Hence, creating a reliable model calls for an extensive understanding of the phenomena at all scales, and more advanced modeling approaches than those applied today are required. Indeed, modeling of plastic waste gasification (PWG) is still in its infancy today. Our review paper shows that the thermophysical properties are rarely properly defined. Challenges in this regard together with possible methodologies to decently define these properties have been elaborated. The complexities regarding the kinetic modeling of gasification are numerous, compared to, e.g., plastic waste pyrolysis, or coal and biomass gasification, which are elaborated in this work along with the possible solutions to overcome them. Moreover, transport limitations and phase transformations, which affect the apparent kinetics of the process, are not usually considered, while it is demonstrated in this review that they are crucial in the robust prediction of the outcome. Hence, possible approaches in implementing available models to consider these limitations are suggested. Finally, the reactor-scale phenomena of PWG, which are more intricate than the similar processes—due to the presence of molten plastic—are usually simplified to the gas-solid systems, which can result in unreliable modeling frameworks. In this regard, an opportunity lies in the increased computational power that helps improve the model’s precision and allows us to include those complexities within the multi-scale PWG modeling. Using the more accurate modeling methodologies in combination with multi-scale modeling approaches will, in a decade, allow us to perform a rigorous optimization of the PWG process, improve existing and develop new gasifiers, and avoid fouling issues caused by tar.

Trends and Insights from Transportation Congestion Pricing Policy Research: A Bibliometric Analysis.

Toll-based congestion pricing (CP) policies are increasingly implemented globally for alleviating road traffic congestion. Several interconnected factors affecting or induced by CP implementation include air quality/emissions, travel time, and road user safety. We sought to examine and characterize research output and patterns across several domains (e.g., health, policy acceptability) surrounding toll-based CP policies, in order to identify where research has focused and where gaps exist. We conducted a structured review and identified 2333 relevant publications, using semi-supervised and machine learning strategies combined with manual review. Annual publication counts peaked in 2015 (n = 122). Themes identified from title and abstract terms included policy implementation characteristics, advanced transportation modeling methods and approaches, and public perception and acceptability. Authorship networks indicated a lack of interdisciplinary research. Country analyses identified the US, China, and the UK as the most frequently represented countries, and underrepresentation from low-income countries. Findings indicate that research focused on specific road user types (e.g., pedestrians) and safety impacts, and equity considerations were relatively sparse compared to other topics (e.g., policy economics, public perception). Additional research on these critical topics is necessary to ensure that such policies are designed to promote positive and equitable effects on road user health and safety.

Impact of river flow modification on wetland hydrological and morphological characters.

A good number of researchers investigated the impact of flow modification on hydrological, ecological, and geomorphological conditions in a river. A few works also focused on hydrological modification on wetland with some parameters but as far the knowledge is concerned, linking river flow modification to wetland hydrological and morphological transformation following an integrated modeling approach is often lacking. The current study aimed to explore the degree of hydrological alteration in the river and its effect on downstream riparian wetlands by adopting advanced modeling approaches. After damming, maximally 67 to 95% hydrological alteration was recorded for maximum, minimum, and average discharges. Wavelet transformation analysis figured out a strong power spectrum after 2012 (damming year). Due to attenuation of flow, the active inundation area was reduced by 66.2%. After damming, 524.03 km2 (48.9% of total pre-dam wetland) was completely obliterated. Hydrological strength (HS) modeling also reported areas under high HS declined by 14% after post-dam condition. Wetland hydrological security state (WSS) and HS matrix, a new approach, are used to explore wetland characteristics of inundation connectivity and hydrological security state. WSS was defined based on lateral hydrological connectivity. HS under critical and stress WWS zones deteriorated in the post-dam period. The morphological transformation was also well recognized showing an increase in area under the patch, edge, and a decrease in the area under the large core area. All these findings established a clear linkage between river flow modification and wetland transformation, and they provided a good clue for managing wetlands.

Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial

Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing an improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through the implementation of moderated non-linear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex model building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) and nitrogen dioxide (NO2) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM2.5 and PM10 were consistently reliable, but the EV for NO2 had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM2.5 and PM10 model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the “Growth trajectories and air pollution” (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.