Scaling Trust in Industrial Semantic Ecosystems: An Integrated Validation Framework for Interoperable Modeling and Tagging

ABSTRACTInundation dynamics in lowland rivers are an outcome of a complex interaction between hydrologic and geomorphic factors. The co‐occurrence of river flow and tidal surge in coastal regions further complicates these interactions. The extent to which these factors mediate inundation mechanisms in regulated lowland river reaches remains challenging and has been poorly understood. This study presents a novel framework that examines the combined effect of flood drivers (riverine and high tide) and geomorphic factors in determining the inundation mechanism along the Lower Tapi Basin (LTB) in western India through an integration of hydrological, tidal, hydrodynamic, and geomorphic datasets. Using variable infiltration capacity (VIC) and Hydrologic Engineering Center River Analysis System (2D HEC‐RAS) with geomorphic characteristics of river channels, we explore how spatial inundation dynamics vary across different valley settings under different flood return periods. Our integrated modelling framework accurately simulates daily streamflow and flood inundation. Incorporation of multidisciplinary data through the integrated modelling framework has advanced our understanding of flood inundation dynamics in lowland rivers. We have further shown that the model uncertainty can be reduced by incorporating field data, bathymetric measurements and rule curve of reservoir operation in the model domain. Although this modelling framework has been developed on a specific lowland reaches of the Tapi River, it can be further extended to any lowland rivers in similar settings. Such multidisciplinary models will help to analyse river response to future projected climate and sea level change.

Semantic links in integrated modelling frameworks

Multi-level system modelling of the resource-food-bioenergy nexus in the global south

The concept of sustainable development is now valued to be fundamental to most issues concerning human activities for development. As sustainable development requires balancing conflicting requirements, rational decision making is necessary to achieve sustainability. Simulation models to assess and forecast sustainability can aid rational decision making. In the context of human settlements, human-nature interactions need to be considered in an integrated manner to build appropriate simulation models. The integrated model framework proposed here serves as a base for such simulation models. The model framework is based on the premise that a community's attitude and its living environment determine human settlement sustainability. A generic model design comprising fundamental components of the proposed sustainability assessment and forecasting model framework is also presented in this paper. Discussion on relevant implementation issues including a simulation system and key limitations of the framework are also presented. This framework can be adopted for development and implementation of a simulation system for sustainability assessment and forecasting studies.

Ensuring consistent glycosylation-associated quality of therapeutic monoclonal antibodies (mAbs) has become a priority in pharmaceutical bioprocessing given that the distribution and composition of the carbohydrates (glycans) bound to these molecules determines their therapeutic efficacy and immunogenicity. However, the interaction between bioprocess conditions, cellular metabolism and the intracellular process of glycosylation remains to be fully understood. To gain further insight into these interactions, we present a novel integrated modelling platform that links dynamic variations in mAb glycosylation with cellular secretory capacity. Two alternative mechanistic representations of how mAb specific productivity (qp ) influences glycosylation are compared. In the first, mAb glycosylation is modulated by the linear velocity with which secretory cargo traverses the Golgi apparatus. In the second, glycosylation is influenced by variations in Golgi volume. Within our modelling framework, both mechanisms accurately reproduce experimentally-observed dynamic changes in mAb glycosylation. In addition, an optimisation-based strategy has been developed to estimate the concentration of glycosylation enzymes required to minimise mAb glycoform variability. Our results suggest that the availability of glycosylation machinery relative to cellular secretory capacity may play a crucial role in mAb glycosylation. In the future, the modelling framework presented here may aid in selecting and engineering cell lines that ensure consistent mAb glycosylatio.

The demand for projections of the future distribution of biodiversity has triggered an upsurge in modelling at the crossroads between ecology and evolution. Despite the enthusiasm around these so-called biodiversity models, most approaches are still criticised for not integrating key processes known to shape species ranges and community structure. Developing an integrative modelling framework for biodiversity distribution promises to improve the reliability of predictions and to give a better understanding of the eco-evolutionary dynamics of species and communities under changing environments. In this article, we briefly review some eco-evolutionary processes and interplays among them, which are essential to provide reliable projections of species distributions and community structure. We identify gaps in theory, quantitative knowledge and data availability hampering the development of an integrated modelling framework. We argue that model development relying on a strong theoretical foundation is essential to inspire new models, manage complexity and maintain tractability. We support our argument with an example of a novel integrated model for species distribution modelling, derived from metapopulation theory, which accounts for abiotic constraints, dispersal, biotic interactions and evolution under changing environmental conditions. We hope such a perspective will motivate exciting and novel research, and challenge others to improve on our proposed approach.

Exploring an integrated method for measuring the relative spatial equity in public facilities in the context of urban parks

As biodiversity continues to decline under the combined pressures of climate change and urbanization, there is an urgent need for comprehensive risk assessment. Given the close interconnection between biodiversity and ecosystem functions, two major risk factors have been identified: (1) habitat loss (destruction) and (2) habitat gain expansion (disturbance). To address these challenges, this study proposes an integrated approach for biodiversity risk assessment that spatially and quantitatively evaluates risks from a holistic perspective. Specifically, an integrative modeling framework (BIOMOD2–LCM–MigClim) was developed to predict biodiversity patterns under the simultaneous impacts of climate change and urbanization. In parallel, novel indices, the Persistence Index (PI) and Disturbance Index (DI), were designed to assess biodiversity risks from multidimensional temporal and spatial perspectives. Based on the biodiversity risk types defined as Risk 1 (habitat loss; climate change risk species) and Risk 2 (habitat gain–expansion; invasive species), the study derived spatial distribution patterns and conducted a time series analysis of risk characteristics. Results highlight the dynamic ecological interactions in competitive areas, where Risk 2 advances by intruding into Risk 1 zones, thereby accelerating Risk 1 retraction. These findings confirm the need for integrated methodologies to capture shifting biodiversity responses and provide robust decision support for fundamental biodiversity policies.

In Chap. 9, an integrative modeling framework was proposed for modeling open agent systems. In Chap. 6, we advocated an ORGANISED framework to undertake the organizational abstraction of an agent organization in terms of organizational metaphor. Collections of visual model-building blocks were subsequently specified and defined in Chap. 7 for the concrete, graphic analysis of an open agent organization. Chap. 8 presented a system of formal representation of the ORGANISED framework through temporal logics. This body of work constitutes the organization-oriented analysis system of open agent systems in terms of organizational metaphor.

An Integrated Modeling Framework in Projections of Hydrological Extremes

This study uses an integrated modeling framework that couples the dynamics of hydrology, soil thermal regime, and ecosystem carbon and nitrogen to quantify the long‐term peat carbon accumulation in Alaska during the Holocene. Modeled hydrology, soil thermal regime, carbon pools and fluxes, and methane emissions are evaluated using observation data at several peatland sites in Minnesota, Alaska, and Canada. The model is then applied for a 10,000 year (15 ka to 5 ka; 1 ka = 1000 cal years before present) simulation at four peatland sites. We find that model simulations match the observed carbon accumulation rates at fen sites during the Holocene (R2 = 0.88, 0.87, 0.38, and −0.05 using comparisons in 500 year bins). The simulated (2.04 m) and observed peat depths (on average 1.98 m) were also compared well (R2 = 0.91). The early Holocene carbon accumulation rates, especially during the Holocene thermal maximum (HTM) (35.9 g C m− 2 yr− 1), are estimated up to 6 times higher than the rest of the Holocene (6.5 g C m− 2 yr− 1). Our analysis suggests that high summer temperature and the lengthened growing season resulted from the elevated insolation seasonality, along with wetter‐than‐before conditions might be major factors causing the rapid carbon accumulation in Alaska during the HTM. Our sensitivity tests indicate that, apart from climate, initial water table depth and vegetation canopy are major drivers to the estimated peat carbon accumulation. When the modeling framework is evaluated for various peatland types in the Arctic, it can quantify peatland carbon accumulation at regional scales.

Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States

Rapid urbanization has subjected the megacities of developing countries to various environmental stresses. Delhi, a major Indian megacity, faces increasing urban stress leading to reduction in air quality and visibility. These challenges necessitate an integrated modeling framework to mitigate adverse environmental impacts on public health. Therefore, we have developed an advanced version of the high-resolution Delhi Model with Chemistry and aerosol framework (DM-Chem) at the National Centre for Medium Range Weather Forecasting (NCMRWF) under the Weather and Climate Science for Service Partnership India (WCSSP India) project. This collaborative initiative between India and the United Kingdom aims to provide real-time forecasts of visibility and fine particulate matter (PM 2.5 ) for Delhi and neighboring regions during the winter season. The DM-Chem framework is unique due to its detailed urban canopy scheme and realistic aerosol representation, making it well suited for city-scale forecasts. It is designed to predict extreme fog and pollution events in the winter season. Here, we discuss the major physical parameterization improvements for the model, along with its skill and deficiencies in predicting extreme events. Notably, irrigation effects on surrounding agricultural areas have significantly improved fog and visibility forecasts, but have degraded the wind forecasts. We demonstrate the applicability of this modeling framework to study aerosol–radiation interaction during fog holes and discuss its potential to be applied or adapted to other megacities worldwide. Significance Statement The capital city Delhi and, in general, many metropolitan cities of India often report high levels of particulate matter pollution especially during wintertime, often accompanied by episodes of dense fog. Air quality and visibility forecasting for an urban region is dependent upon many key model inputs other than emissions and meteorology. Realistic representation of the (i) city’s local urban morphology, (ii) surface aerosols, and (iii) soil conditions impacted by irrigation activities has been found to significantly influence air quality and visibility prediction in Delhi and surrounding areas. This work aimed at providing a reliable air quality and visibility forecast for Delhi, which is crucial to minimize both health and economic losses.

ABSTRACTTwo modelling approaches are presented in this article for spatial and temporal analysis of water resources risk. Major sources of uncertainty in water resources management are spatial and temporal variability. Spatial variability occurs when values fluctuate with the location of an area and temporal variability occurs when values fluctuate with time. System dynamics (SD) simulation and hydrodynamic modelling are presented in this article as tools for modelling the dynamic characteristics of flood risk and its spatial variability. The first modelling framework presents SD simulation coupled with 3D fuzzy set theory. Whereas the second modelling framework presents hydrodynamic modelling coupled with 3D fuzzy set theory. The two integrated modelling frameworks are illustrated and compared using the Red River flood of 1997 (Manitoba, Canada) as a case study. For the 1997 Red River case study, SD simulation proved to be efficient modelling approach for capturing the feedback-based dynamic processes occurring in the watershed. However, the SD simulation has limited capacity for adequately representing spatial processes. The SD simulation coupled with 3D fuzzy set theory and geographic information system-based spatial analysis tool provides an integrated approach capable of modelling the spatial and temporal variability of flood risk. With respect to the topography of Red River basin, the hydrodynamic modelling coupled with 3D fuzzy set theory proved to be an accurate approach for predicting spatial and temporal variation of water surface elevation, velocity, flux, etc. The integrated approach demonstrates high accuracy in predicting spatial and temporal variability of flood risk.

<p>Agricultural use of pesticides helps control a range of pests and diseases that threaten crops, thereby avoiding yield losses and improving the quality of the food produced. However, pesticides applied on agricultural fields dissipate with time. The export of pesticides and their transformation products after application from the agricultural fields threatens the water quality of aquatic systems in many world regions.</p><p>Climate change is further expected to intensify pest pressures and potential pesticide use by affecting agriculture in many ways. Changing climatic conditions can increase pesticide leaching due to increased and frequent rainfall, higher degradation rates, or higher temperatures or soil moisture contents. The indirect effects are changes in land use, the timing of crop cultivation, selection of other crop types, new pests and changed pest behaviour, etc. Additionally, several socio-economic factors influence pesticide use at the farm and national level, including regulation and legislation, economy, technology and crop characteristics. In order to better understand the pesticide risk to surface waters in the future, we aim to understand the influence of both climate and socio-economic change on pesticide use and fate.</p><p>Various catchment-scale models are available to assess pesticides and their impacts on water bodies. However, most modelling approaches solely concentrate on the total amount or concentration of pesticide exported from a catchment and do not necessarily analyse the future change of pesticide and transformation products. We propose an integrated modelling framework to answer the research questions: What are the current significant climate and socio-economic drivers influencing pesticide use and emissions? How can climate change influence pesticide and transformation products emission trends? How will socio-economic change influence pesticide emissions?</p><p>The integrated modelling framework helps to include adapting agricultural production to climatic (e.g., temperature, precipitation) and socio-economic drivers (e.g., land use, crop type, pesticide regulation) and quantifying pesticide emissions with the Zin-AgriTRA pesticide fate model. The ZIN-AgriTra is a catchment scale reactive transport model which can simulate agrochemical and transformation products exported from agricultural catchments. We use the Eur-Agri-SSP scenarios that extend and enrich the basic Shared Socio-economic Pathways with a regional and sectoral component on agriculture to explain the socio-economic change and climate projections for Representative concentration pathways to adopt climate change scenarios.</p><p>The integrated modelling framework links the future scenario results from independent, standalone models that present crop rotation, land use, pesticide regulation and climate to the pesticide fate model (Zin-AgriTRA). The framework is applied to an agricultural catchment in Burgenland, Austria, to quantify pesticide pollution under future climate and socio-economic change up to 2050.</p><p> </p>