Integrated Modeling Framework Research Articles

Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance

Abstract Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. While current acoustic data models provide information on the presence/absence of individual species, new approaches are needed to monitor population abundance, ideally across large spatio‐temporal regions. We present an integrated modelling framework that combines high‐quality but temporally sparse bird point count survey data with acoustic recordings. Our models account for imperfect detection in both data types and false positive errors in the acoustic data. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modelling framework in a case study to estimate abundance of the Eastern Wood‐Pewee (Contopus virens) in Vermont, USA. The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Improved estimates are obtained across a wide range of scenarios, with the largest gains occurring when detection probability for the point count data is low. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood‐Pewee in this region. Our integrated modelling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio‐temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.

Evaluation of Combination Strategies for the A2AR Inhibitor AZD4635 Across Tumor Microenvironment Conditions via a Systems Pharmacology Model.

BackgroundAdenosine receptor type 2 (A2AR) inhibitor, AZD4635, has been shown to reduce immunosuppressive adenosine effects within the tumor microenvironment (TME) and to enhance the efficacy of checkpoint inhibitors across various syngeneic models. This study aims at investigating anti-tumor activity of AZD4635 alone and in combination with an anti-PD-L1-specific antibody (anti-PD-L1 mAb) across various TME conditions and at identifying, via mathematical quantitative modeling, a therapeutic combination strategy to further improve treatment efficacy.MethodsThe model is represented by a set of ordinary differential equations capturing: 1) antigen-dependent T cell migration into the tumor, with subsequent proliferation and differentiation into effector T cells (Teff), leading to tumor cell lysis; 2) downregulation of processes mediated by A2AR or PD-L1, as well as other immunosuppressive mechanisms; 3) A2AR and PD-L1 inhibition by, respectively, AZD4635 and anti-PD-L1 mAb. Tumor size dynamics data from CT26, MC38, and MCA205 syngeneic mice treated with vehicle, anti-PD-L1 mAb, AZD4635, or their combination were used to inform model parameters. Between-animal and between-study variabilities (BAV, BSV) in treatment efficacy were quantified using a non-linear mixed-effects methodology.ResultsThe model reproduced individual and cohort trends in tumor size dynamics for all considered treatment regimens and experiments. BSV and BAV were explained by variability in T cell-to-immunosuppressive cell (ISC) ratio; BSV was additionally driven by differences in intratumoral adenosine content across the syngeneic models. Model sensitivity analysis and model-based preclinical study simulations revealed therapeutic options enabling a potential increase in AZD4635-driven efficacy; e.g., adoptive cell transfer or treatments affecting adenosine-independent immunosuppressive pathways.ConclusionsThe proposed integrative modeling framework quantitatively characterized the mechanistic activity of AZD4635 and its potential added efficacy in therapy combinations, across various immune conditions prevailing in the TME. Such a model may enable further investigations, via simulations, of mechanisms of tumor resistance to treatment and of AZD4635 combination optimization strategies.

An integrated modeling framework for active traffic management and its applications in the Washington, DC area

Developing appropriate modeling and simulation tools with the capability of analyzing traffic patterns, travel demand, and traveling/driving behavior responses at the regional level is important for the evaluation of active traffic management (ATM) and real-time transportation systems management and operations (TSM&O). In this paper, an integrated travel behavior and dynamic traffic assignment modeling tool, AgBM-DTALite, has been developed and adapted for real-time and dynamic analysis of ATM and TSM&O. The ATM algorithms are tightly integrated into the system, revising the control rates on a minute-by-minute basis. This tool also successfully incorporates travel demand and behavioral sensitivity into the traffic control and operations. The model is applied in a large-scale network to evaluate several ATM strategies, including variable speed limit (VSL) and ramp metering in the Washington, DC area. Significant departure time behavioral changes are found in response to ATM and the subsequent changes in traffic conditions. The impact on traffic dynamics is also quantified using various performance metrics.

Assessing biophysical and socio-economic impacts of climate change on regional avian biodiversity

Climate change threatens biodiversity directly by influencing biophysical variables that drive species’ geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia and we find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts. However, by translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global.

Pop-guide: Population modeling guidance, use, interpretation, and development for ecological risk assessment.

The assimilation of population models into ecological risk assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated modeling framework and decision guide to aid the development of population models with respect to ERA objectives and data availability. In the framework, the trade-offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The decision guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the decision guide and modeling framework into a comprehensive approach, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment (Pop-GUIDE), for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop-GUIDE, assessors are guided through the trade-offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop-GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. Integr Environ Assess Manag 2021;17:767-784. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Exploring regional land use dynamics under shared socioeconomic pathways: A case study in Inner Mongolia, China

Land use change scenarios can contribute to the development of improved policies to tackle climate change. In this study, we developed an integrated modeling framework to simulate land use dynamics in Inner Mongolia, China under the Shared Socio-economic Pathways (SSPs) during 2015–2050. A computable general equilibrium of land use change model was developed to simulate the regional socio-economic effects on future land use demand. The Dynamics of Land System model was applied to simulate the spatial patterns of land use change. Land use change varied considerably among SSPs. Increased agricultural productivity reduced the demand for cultivated land. Increased livestock system efficiency and decreased animal product consumption reduced grassland demand, benefiting natural grassland recovery. Built-up land increased in all SSPs, with positive relation to GDP growth. Under SSP1, forest area, grassland, and built-up land increased by 19940 km² (12.05%), 9882 km² (1.88%), and 20844 km² (129.19%), respectively, while cultivated land decreased by 10995 km² (−9.80%). Grassland decreased considerably under SSP3 and SSP4, and distributed discretely, mainly concentrated on Xilin Gol League and northwestern Ulanqab. Built-up land sprawled considerably under SSP1 and SSP5, and was mainly concentrated in eastern Tongliao and northern Ordos. This integrated modeling framework provides an effective and reliable way to simulate regional land use dynamics under a range of alternative future pathways.

Fostering local crop-livestock integration via legume exchanges using an innovative integrated assessment and modelling approach based on the MAELIA platform

CONTEXTCrop diversification is now well-recognised as a strong lever to address the environmental challenges currently faced by agriculture. Connecting and fostering local exchanges between specialised arable crop and livestock farms can support the diversification of crops and relocate animal feeding with local protein (vs. soya supplements). However, the trade-offs and synergies between individual and collective objectives and performances generated by this system are still largely unknown. Innovative tools that consider the spatiotemporal heterogeneity underpinning the daily functioning of farms are needed to explore the implications of exchange scenarios. OBJECTIVESTo assess self-sufficiency, sustainability and vulnerability at the farm, group (arable vs. livestock) and territorial levels considering the baseline situation and scenarios that increase synergy between arable and livestock farms. METHODSWe demonstrated the utility of using MAELIA, a spatial agent-based integrated modelling framework, to support iterative design and assessment of self-sufficiency, sustainability and vulnerability of such scenarios. MAELIA was applied to model a collective of five arable and two livestock farmers in western France. In a participatory approach, scientists, agricultural advisers and the farmers co-designed three possible scenarios of legume exchanges. RESULTS AND CONCLUSIONSOnly the most ambitious scenario based on strong collaboration allowed farmers to reach local protein self-sufficiency while reducing the variability in five of the seven indicators used to assess farm performances. Meeting livestock farms' demand for legumes had a positive influence on socio-economic performance at the territorial level, including an increase in the mean gross margin (of 71 €/ha; 4% higher), decrease in the use of nitrogen fertiliser (of ca. 21 kg N/ha; 11% lower) and decrease in labour time (of ca. 12 min/ha; 5% lower). No major trade-offs between self-sufficiency and vulnerability were observed. However, there were distinct individual performances related to the degree of changes imposed on each farm. SIGNIFICANCEWe show that by reducing the dependence to external inputs, these systems are promising alternatives to an environmentally sustainable, resilient and economically viable agroecological transition. The development of dedicated institutional support for direct bilateral or multilateral agreements, and specific financial and technical support would encourage farmers to join such initiatives and redesign their farming systems.

Integrated modelling framework for enhancement history matching in fluvial channel sandstone reservoirs

Abstract Modelling lithofacies and petrophysical properties are the challenging processes at the beginning of exploration and production of hydrocarbon reservoirs. However, the limited amount of well data and core data are the main issues facing conventional modelling processes. In this study, Artificial Neural Network (ANN), Sequential Gaussian Simulation (SGS), Co-kriging and object-based modelling (OBM) were integrated as the enhancement framework for lithofacies and petrophysical properties modelling in the fluvial channel sandstone reservoir. In the OBM, multiple fluvial channels were generated in the lithofacies model. The result of this model represented all the characteristic of the fluvial channel reservoir. The model was then distributed with channels, crevasse, and leeves depositional facies with background shale. Multiple geological realizations were made and cross-validation to select the most suitable lithofacies distribution. This model was cross-validated by modelling the porosity and permeability properties using Sequential Gaussian Simulation. Thereafter, the modelling process continued with Artificial Neural Network. Petrophysical properties (mainly porosity and permeability) were predicted by training various seismic attributes and well log data using the ANN. Applying the co-kriging algorithm, the predicted ANN model was integrated with OBM simulated lithofacies model to preserve the fluvial features of the geological system. To achieve full field history matching, the final geological model was upscaled to serve as input data in dynamic history matching. An excellent and nearly perfect history matching with a least mismatch was obtained between the measurement and simulated bottom hole pressure from well test and production history. The results indicated that an efficient integrated workflow of ANN and other geostatistical approaches are imperative to attaining an excellent history matching.

Influence of energy deficiency on the subcellular processes of Substantia Nigra Pars Compacta cell for understanding Parkinsonian neurodegeneration

Parkinson’s disease (PD) is the second most prominent neurodegenerative disease around the world. Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that “Energy deficiency at a sub-cellular/cellular/systems level can be a common underlying cause for SNc cell loss in PD.” Here, we propose a comprehensive computational model of SNc cell, which helps us to understand the pathophysiology of neurodegeneration at the subcellular level in PD. The aim of the study is to see how deficits in the supply of energy substrates (glucose and oxygen) lead to a deficit in adenosine triphosphate (ATP). The study also aims to show that deficits in ATP are the common factor underlying the molecular-level pathological changes, including alpha-synuclein aggregation, reactive oxygen species formation, calcium elevation, and dopamine dysfunction. The model suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modeling framework to understand the neurodegenerative processes underlying PD.

Integrated modeling method of Cyber physical system based on extended AADL

The concept and connotation of CPS are analyzed firstly in the paper, then the challenges of system modeling brought by heterogeneous feature of the CPS system are pointed out. How to describe the CPS model are studied. We propose a description method of CPS integrated modeling framework based on extended AADL (architecture analysis and design language); and realize the unified description of the computing entity, the physical entity and interactive entity through extending the standard and appendix of AADL.

Social Vulnerability and Power Loss Mitigation: A Case Study of Puerto Rico

The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures --- such as power grids --- as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. In this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-changing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening.

Global integrated modeling framework of riverine dissolved inorganic nitrogen with seasonal variation

Global integrated modeling framework of riverine dissolved inorganic nitrogen with seasonal variation

Grain growth prediction in selective electron beam melting of Ti-6Al-4V with a cellular automaton method

An integrated modeling framework coupling the discrete element method for powder spreading, finite volume method for powder bed melting, and an extended cellular automaton method for grain structure evolution during solidification is proposed. In this framework, the initial grain structure of both the substrate and metal powders can be taken into account and used to capture epitaxial and competitive grain growth. The framework is used to provide an in-depth understanding of microstructure development in Ti-6Al-4V during the selective electron beam melting process. The complex process of grain growth during deposition of multiple tracks and multiple layers is modeled through an analysis restarting scheme. The epitaxial growth of grains from pre-existing grains, in particular the grains of partially melted powders, is reproduced. The mechanism of microstructure development within the overlap region of consecutive tracks and layers for various scan strategies is revealed. The simulation results are in qualitative agreement with experimental observation in the literature. The proposed modeling framework is a powerful tool to guide optimal process parameters that lead to designed, site-specific microstructure control and therefore to tailored mechanical properties of parts fabricated by the powder bed fusion additive manufacturing process.

A dynamic land use/land cover input helps in picturing the Sahelian paradox: Assessing variability and attribution of changes in surface runoff in a Sahelian watershed

In Sahelian landscapes, land use/land cover (LULC) dynamics and climate variability are already known to affect the water cycle. In its current practice however, hydrological modelling does not account for LULC changes. This issue pertains to rapidly evolving watersheds and might result in critical inaccuracies in the simulated processes. In this study, the Soil and Water Assessment Tool (SWAT) model was used to simulate surface runoff in the small Sahelian watershed of Tougou, which underwent significant LULC changes between 1952 and 2017. Based on rainfall/runoff data acquired from 2004 to 2018, the SWAT model was calibrated under two scenarios: a static land use scenario (SLU) using a single LULC map (in 1999) and a dynamic land use scenario (DLU) integrating 3 LULC maps (1999, 2009 and 2017). The DLU scenario estimated with higher accuracy surface runoff, deep aquifer infiltration and actual evapotranspiration processes. Based on the calibrated parameters, surface runoff was simulated during the historical period 1952–2003 under four scenarios with static LULC maps (in 1952, 1973, 1986 and 1999) opposed to a fifth scenario integrating these LULC maps dynamically. The DLU scenario was found to be more effective at picturing the so-called Sahelian paradox (i.e. the increase in surface runoff despite the decrease in rainfall), reported in the literature for small watersheds in the Sahel. The analysis of variability revealed that fluctuations in surface runoff were both influenced by rainfall and LULC changes. Furthermore, the isolated contributions of climate variability and LULC changes on surface runoff showed that LULC conditions played a dominant role (ηlulc = +393.1%) in the runoff increase over climate (ηcl = −297%) during the historical period. These results highlight the importance of accounting for LULC dynamics in hydrological modelling and advocate the development of integrated modelling frameworks for hydrologists and water resource managers.

Predicting weather-induced delays of high-speed rail and aviation in China

High-speed rail (HSR) has become a competitive mode with aviation for medium-distance intercity travel, given the massive deployment of the HSR infrastructure network in China. While the travel experience with both HSR and air has become more convenient, the systems’ operational reliability in terms of punctuality remains a key concern, especially during disruptive events, such as under severe weather conditions. Although previous studies have attempted to investigate the impact of severe weather events on the operational performance of transportation systems, there is still a lack of ability to forecast to what extent the performance of different transportation systems may vary under various conditions. This study develops an integrated modeling framework that allows us to predict the performance of weather-induced delays of different transportation systems, including HSR and aviation. By applying machine-learning methods to real-world transportation performance data, the study examines the robustness of the method, variations of data characteristics and the different applications of the predictive modeling system. Overall, the concept and modeling framework provide important implications for the improvement of transportation system resilience to various severe weather-related disruptions through the understanding of the impact and its predictability of the system performance.

Impacts of regional industrial electricity savings on the development of future coal capacity per electricity grid and related air pollution emissions – A case study for China

Moving to a sustainable industry and weaning electricity supply off coal are critical to mitigate ambient air pollution and climate change. This is particularly true in China which is globally the largest manufacturer and relies heavily on coal-fired electricity. Research that explores the linkages between industrial electricity use and the electricity supply sector to curb air pollution is limited. In this study, an integrated modeling framework is developed that quantifies the impact of industrial electricity savings on the evolution of the coal power plant fleet in China, and on air pollutants for the different power grids in the period 2016–2040. The framework includes a rich set of efficiency technologies and detailed unit-level information (geo-coordinates, thermal efficiency, environmental performance). We find that the reduced electricity load due to the industrial efficiency improvements can effectively scale down the coal power fleet, and most importantly allows closing the most polluting units. The potentials for electricity savings vary amongst the industrial sectors and provinces, resulting in significant heterogeneity of coal plant phaseout per power grid. Because energy-intensive industrial plants are mostly found in the North, Central and Northwest grids, these three grids provide 66% of the total displaced coal capacity. The closing of coal units leads to a variation in annual emission reductions per power grid of 13–85 kt-SO2, 19–129 kt-NOx, 3–17 kt-PM and 21–167 Mt-CO2, compared to business-as-usual emissions. The iron & steel, aluminium and chemical sectors, together contribute to 84% of the total electricity savings by 2040, and are thereby most important to target.

Informing groundwater policies in semi-arid agricultural production regions under stochastic climate scenario impacts

Region-specific groundwater policies are required to regulate groundwater extraction for agricultural irrigation and reduce climate change adaption externalities. We examine the semi-arid Seewinkel region in Austria and explore interactions between climatic, agronomic, hydrological, and socio-economic conditions and processes to provide policy advice. The assessment is conducted with a spatially explicit integrated modeling framework to analyze impacts on land and irrigation water use, land management, and net benefits of agricultural production. The model results show that with imposed groundwater restrictions for irrigation, land use shifts from irrigated vineyards to mostly rainfed cropland with declining regional net benefits of agricultural production. The direction of change is similar for a DRY, SIMILAR, and WET climate scenario, while the magnitude differs. We estimate that an increase of the marginal value of groundwater extraction for irrigation by 0.1 €/m3 results in an average decrease in groundwater extraction volumes by 17.2 Mm3 in DRY, 6.3 Mm3 in SIMILAR, and 6.4 Mm3 in WET. Furthermore, regional net benefits of agricultural production decrease by 3.4 M€ in DRY and SIMILAR, and by 1.6 M€ in WET, on average. Our assessment highlights that efficient groundwater policies can help to sustain groundwater availability in semi-arid regions, particularly under climate change.

A simple unit specific event based modeling framework for short term scheduling and heat integration of batch plants: Design and optimization of heat storage vessels

Energy conservation has been one of the prime research objectives of the global scientific community. Heat integration has been a promising intensification technique for energy conservation in chemical industries. In this work, a simple unit-specific event-based framework is proposed for short term scheduling and heat integration. Using the concept of active task, various modelling issues such as task alignments, energy balances, direct and indirect heat integrations have been handled precisely with minimum number of equations and variables. The effect of the amount of thermal fluid, initial temperature and number of storage vessels on profit is systematically analyzed. The accuracy of the proposed framework is demonstrated using three benchmark examples. The proposed model could effectively incorporate the direct and indirect heat integration and external utility usage. The computational results show that this integration leads to an optimum of two heat storage vessels as against three reported by Sebelebele and Majozi (2017).

Developing a hybrid modeling and multivariate analysis framework for storm surge and runoff interactions in urban coastal flooding

The formation of urban coastal flooding is mainly ruled by the interaction between rainfall-runoff and storm surge. This study aims to advance the understanding of coastal urban flood mechanism by developing an integrated modeling and multivariate analysis framework, which involves a hydrologic model (Storm Water Management Model (SWMM)), as the core model, coupled with a coastal hydrodynamic model (Delft3D). The uncertainty associated with the flood depth prediction by integrated models is analyzed using the multivariate Gaussian Copula. The performance of the integrated modeling framework is evaluated for the Chittagong City of Bangladesh, which has experienced extreme and frequent coastal urban floods. Results from modeling indicate that changes in the tidal phase of coastal urban flooding alter the flood’s duration and depth. The intensity of compound flooding is higher for the co-occurrence of rainfall and surge peaks than the occurrence of both events in succession. The average flood duration and depth can be increased by about 2.5 h and 0.24 m, respectively, during compound events. When the storm surge occurs during the transition phase, between high/low tides (2–4 h before peak low/high tide), the duration of flood extends due to longer surge duration (4–4.5 h). Finally, the multivariate Gaussian Copula model adjusts the integrated modeling outputs and enhances the skill to predict the inundation depth by 4.6–24.3%. The findings of this study are critical for a better understanding of coastal urban flood processes and enhancing the informed decision-making for emergency management and planning in low-lying coastal regions.

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.