Modelling Of Region Research Articles

A framework to assess suitability of global digital elevation models for hydrodynamic modelling in data scarce regions

With increase in availability of global Digital Elevation Models (DEMs) that are derived from distinctive techniques with varying characteristics, it is essentially required to assess their accuracies prior to use them in the flood inundation modelling, especially in urban catchments. In the present study, firstly, the vertical accuracy of the global DEMs is assessed using datasets of three independent Ground Control Point (GCP) measurements while accounting for the spatial and temporal variabilities with respect to DEM data acquisitions. Next, we investigate their suitability for flood inundation modelling by developing hydrodynamic models. The accuracy of global DEMs, viz. SRTM, MERIT, AW3D30, COPDEM30, TanDEM-X 12, TanDEM-X 90, FABDEM, and CartoDEM are assessed for Lower Tapi Basin (LTB), India using ICESat-1 and ICESat-2 laser altimetry, and GNSS surveyed locations. The potential of TanDEM-X 12 DEM for deriving Digital Terrain Model (DTM) from the Digital Surface Model (DSM) has been explored by adopting Simple Morphological Filter (SMRF) and void-filling approaches. Finally, a comprehensive performance evaluation of these DEMs that includes DSMs and DTMs in flood inundation modelling is carried out by simulating the severe fluvial flood event of magnitude 25,768 m3/sec that occurred in the year 2006 in LTB through 1D-2D coupled Hydro-Dynamic (HD) models using HEC-RAS software. Prior to development of HD models using global DEMs and DTMs as terrain layer, a benchmark model has been simulated for the same event with terrain layer as topographic surveyed contours of the basin. The least flood depth error has been observed for the FABDEM model (RMSE: 1.59 m), followed by the model with terrain topography as the derived “TanDEM-X 12 DTM-AMP-HEM-EVF” (RMSE: 1.88 m). On the other hand, the HD model based on TanDEM-X 12 DTM AMP-HEM-EVF has been able to simulate flood inundation extent better than the FABDEM model for the same flood event in the basin with Critical Success Index (CSI) scores 0.91 and 0.81 respectively. The TanDEM-X 12 DTM-AMP-HEM-EVF terrain-based model indicated consistently superior results in capturing flood inundation extent, leaving behind all the DSMs whose CSI scores are found to be less than 50 %. The global DEMs that are inherently DSMs in nature are unsuitable for flood inundation modelling as they fail to capture the flood pathways in urban topographies with precision. From the systematic framework followed in the present study, it is inferred that the processed TanDEM-X 12 DEM for the removal of artifacts could be a successful replacement to obtain bare-earth topography. In the absence of TanDEM-X 12 DEM, the freely available FABDEM can be used as an alternative source for terrain layer in flood modelling as its results are comparable to the derived terrain topography of TanDEM-X 12 DEM. The study is one of the first to assess the performance of FABDEM in flood modelling of coastal urban floodplain.

Early Diagnosing and Transformation Prediction of Alzheimer's Disease Using Multi-Scaled Self-Attention Network on Structural MRI Images with Occlusion Sensitivity Analysis.

Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability.

Net evolution of subglacial sediment transport in the Quebec–Labrador sector of the Laurentide Ice Sheet

The Laurentide Ice Sheet's (LIS) interior had a dynamic polythermal base, but the spatiotemporal variations of subglacial processes related to ice divide migration and other transient changes remain largely unknown, limiting our understanding of regional glacial dynamics. Previous studies focused on the regional glacial landform record, while ice sheet models lacked detailed parameterization within these regions, leading to an overestimation of cold-based subglacial conditions' extent and duration. In this study, glacial sediment dispersal patterns as identified by heavy minerals, clasts, and multivariate statistics of till matrix geochemistry were used to assess ice sheet dynamics within the Quebec–Labrador sector of the LIS. The earliest ice-flow phase produced and transported till across the study area (>175 km). However, major oxide data from till matrix geochemistry show a correlation with underlying bedrock, and this relationship is relatively common in areas of thin till cover and resistant bedrock lithologies. These results suggest a switch from an early phase of widespread erosion and long, sustained sediment transport to one of more limited erosion, perhaps abrasion dominant and shorter transport. Till compositional data and related dispersal patterns add supporting evidence to earlier ice sheet reconstructions based on ice-flow indicators and 10Be data together suggesting a transition from widespread uniform warm-based conditions during the earliest ice flow, followed by the development of an ice divide, its migration, and more sporadic warm-based conditions. Consequently, a thorough understanding of ice-flow history is essential for ice sheet modelling and future mineral exploration programs in inner ice sheet regions of the LIS.

APPLICATION OF PINN AND THE METHOD OF DIFFERENTIAL CONSTRUCTION OF ADAPTIVE ONE-DIMENSIONAL COMPUTATIONAL GRIDS

This paper presents an approach to constructing adaptive one-dimensional computational grids using the Beltrami equation and Physics-Informed Neural Networks (PINNs). The main focus is on exploring the potential for precise control of grid node density through the control function ω(s), which allows the grid to adapt to the local features of the problem. The Beltrami equation used, being a key component of the method, regulates the distribution of nodes by modifying the function’s derivatives depending on the values of the control function. The effectiveness of this approach is demonstrated through examples involving one and two regions of node clustering. The results showed that the PINN method combined with the Beltrami equation allows for the creation of computational grids with a high degree of adaptation to given conditions, providing detailed modeling in critical regions. This approach has advantages over traditional numerical methods, as integrating physical laws in the grid construction process minimizes numerical errors and improves modeling accuracy. The use of neural networks offers flexibility in model tuning and the ability to account for complex nonlinear dependencies. The discussion of the results highlights the potential of using PINNs for adaptive grid construction in various fields requiring precise and efficient modeling. In conclusion, this study confirms that the combination of the Beltrami equation and PINNs is a powerful tool for adaptive grid construction, opening new possibilities for numerical modeling of complex physical processes.

Modern Regionalism in France

This paper offers a comprehensive overview of modern regionalism in France, with a particular focus on the territorial administrative organization and its far-reaching effects on cultural, political, and economic regionalism. Drawing from the principles underlying the French regionalization process, we emphasize the decentralization initiatives aimed at empowering local authorities and nurturing regional identities. Our analysis traces the evolution of regionalism in France, from a historical era of centralization to the current state of territorial administrative organization, highlighting the multi-dimensional nature of regional development. Furthermore, we explore the cultural, political, and economic facets of regionalism, illuminating their interconnectedness and their impact on regional dynamics. By delving into the French regionalization model and its outcomes, this paper aims to enhance our comprehension of modern regionalism in France and its broader implications for governance and socio-economic development.

Pluvial Flood Risk Assessment in Urban Areas: A Case Study for the Archaeological Site of the Roman Agora, Athens

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

Regionalization of pancreatic surgery in California: Benefits for preventing postoperative deaths and reducing healthcare costs

IntroductionPancreatic cancer (PC) surgery has been associated with improved outcomes and value when performed at high-volume centers (HVC; ≥20 surgeries annually) compared to low-volume centers (LVC). Some have used these differences to suggest that regionalization of PC surgery would optimize patient outcomes and expenditures. MethodsA Markov model was created to evaluate 30-day mortality, 30-day complications, and 30-day costs. The differences in these outcome measures between the current and future states were measured to assess the population-level benefits of regionalization. A sensitivity analysis was performed to evaluate the impact of variations of input variables in the model. ResultsAmong 5958 new cases of pancreatic cancer in California in 2021, a total of 2443 cases (41 %) would be resectable; among patients with resectable PC, a total of 977 (40 %) patients would undergo surgery. In aggregate, HVC and LVC 30-day postoperative complications occurred in 364 patients, 30-day mortality in 35 patients, and healthcare costs expended managing complications were $6,120,660. In the predictive model of complete regionalization to only HVC in California, an estimated 29 fewer complications, 17 fewer deaths, and a cost savings of $487,635 per year would occur. Conclusions and relevancePancreatic cancer (PC) surgery has been associated with improved outcomes and value when performed at high-volume centers (HVC; ≥20 surgeries annually) compared to low-volume centers (LVC). Complete regionalization of pancreatic cancer surgery predicted benefits in mortality, complications and cost, though implementing this strategy at a population-level may require investment of resources and redesigning care delivery models.

Drier August and colder September slow down the delaying trend of leaf senescence in herbaceous plants on the Qinghai–Tibetan Plateau

Plant phenological shifts on the Qinghai–Tibetan Plateau (QTP) have gained considerable attention over the last few decades. However, temporal changes in plant autumn phenology and the main driving factors remain uncertain. Most previous studies used satellite-derived phenological transition dates and climatic statistics during the preseason, which have relatively large uncertainties and may mask some important climate change characteristics at the intra-annual scale, thus affecting exploration of the underlying phenological change causes. This study collected 1685 phenological records at 27 ground stations on the QTP during 1983–2017. Temporal change trends and break points in leaf senescence date (LSD) of 23 herbaceous species were assessed using least squares regression, a meta-analysis procedure, and the Pettitt test. The main drivers and causes were investigated through correlation analysis and contribution calculation based on LSD observations and monthly climatic data. Results showed that, LSD of QTP herbaceous plants was significantly delayed at a rate of 4.45 days/decade during 1983–2017. Break points were concentrated during 1999–2003, with an overall mean in 2001. After 2001, the delay trend in LSD decreased, falling from 5.26 days/decade to 2.54 days/decade. Air temperature and precipitation were the most important climatic factors that showed closer and more extensive correlations with LSD and greater contributions to the inter-annual variations in LSD. August and September were the most critical period during which climatic factors had higher contributions to the LSD shifts. However, August was drier, with precipitation significantly decreasing and temperature increasing, and September was colder after 2001. Therefore, the declining trend in LSD may be attributed to the drier August and colder September. This study has not only provided reliable field evidence on temporal changes in autumn phenology on the QTP, but has also provided valuable insights into autumn phenological modelling and regional carbon cycling in alpine regions.

Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

AbstractAccurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent (SWE) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

PRUSSIC

We present deep ALMA Band 3 observations of the HCN, HCO+, and HNC(4–3) emission in SDP.81, a well-studied z = 3.042, strongly lensed galaxy. These lines trace the high-density gas, which remains almost entirely unexplored in z ≥ 1 galaxies. Additionally, these dense-gas tracers are potentially powerful diagnostics of the mechanical heating of the interstellar medium. While the HCN(4–3) and HNC(4–3) lines are not detected, the HCO+(4–3) emission is clearly detected and resolved. This is the third detection of this line in a high-redshift star-forming galaxy. We find an unusually high HCO+/HCN intensity ratio of ≥2.2. Based on the modelling of the photodissociation region, the most likely explanation for the elevated HCO+/HCN ratio is that SDP.81 has low mechanical heating, making up less than 10% of the total energy budget, along with a sub-solar metallicity of Z ≈ 0.5 Z⊙. While such conditions might not be representative of the general population of high-redshift dusty galaxies, a lower-than-solar metallicity might significantly impact gas masses inferred from CO observations. In addition, we report the detection of CO(0–1) absorption from the foreground lensing galaxy and CO(1–0) emission from a massive companion to the lensing galaxy, approximately 50 kpc to the south-east.

Interaction of focused recharge and deep groundwater discharge near a wetland: A study in the Ordos Basin, China

Groundwater mounds and depressions induced by rainfall and evapotranspiration near wetlands/lakes have been widely observed, but their effects on groundwater flow field are poorly known. Here, a sandy hillslope near a wetland in the Ordos Plateau, China, which has a semi-arid climate, is selected for investigation. After identifying temporary groundwater mounds/depressions based on three water level observation wells, a HYDRUS-2D model is setup to obtain the highly transient daily flow field. The variable thickness of unsaturated zone near the wetland, which controls soil water content and unsaturated hydraulic conductivity, is found to be responsible for groundwater mounds resulting from focused recharge and groundwater depressions resulting from nonuniform evapotranspiration. On nonrainy days, evapotranspiration in the lowland is supported by flowpaths from regional groundwater discharge, and in the slope is supported by a flow cell from the upland to the upper part of the slope, which causes water table recession in the upland. On rainy days, focused recharge in the lowland leads to two temporary local flow cells, which break the regional groundwater discharge flowpaths into two temporary flow cells. The two temporary flow cells toward the upland contribute to water table rises in the uplandMoreover, we find focused recharge in rainy days does not change the overall direction of flowpaths controlled by regional groundwater discharge and strong evapotranspiration, but only increase the tortuosity of flowpaths. By coupling daily atmospheric conditions and regional groundwater discharge, this study has implications for groundwater recharge estimation and future groundwater modelling in arid regions.

Security Region Modeling and Fault Ride-Through Coordinative Control of VSC-HVDC Under Grid Fault

Security Region Modeling and Fault Ride-Through Coordinative Control of VSC-HVDC Under Grid Fault

Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions

Extreme hydrometeorological events such as the 2018 Vaia storm increasingly threaten alpine regions with multiple hazards often compounded and with cascading effects. Currently available risk assessment and prevention tools may therefore prove inadequate, particularly for transborder and vulnerable mountain areas, calling for comprehensive multi-hazard and transdisciplinary approaches. In particular, the exposed assets should not anymore be considered a sheer collection of static items, but the models should also reflect functional features. In this paper, we propose an integrated approach to multi-hazard exposure modelling including both static and functional components. The model is based on a homogeneous planar tessellation composed of hexagonal cells and a graph-like structure which describes the functional connections among the cells. To exemplify the methodology, a combination of static (buildings, protective forests), dynamic (population) and functional (road-based transport system) components has been considered together, targeting a ca. 10,000 km2 region across Italy and Austria. A cell-based aggregation at 250 m resolution and an innovative graph-based simplification allow for a good trade-off between the complexity of the model and its computational efficiency for risk-related applications. Furthermore, aggregation ensures protection of sensitive data at a scale still useful for civil protection. The resulting model can be used for different applications including scenario-based risk analysis and numeric simulation, probabilistic risk assessment, impact forecasting and early warning.

An equivalent winding thermal model considering fill factor and void ratio for multiphysics coupling analysis of permanent magnet linear motors

Accurately clarifying thermal characteristics and acquiring thermal limits of permanent magnet linear motors (PMLMs) is significant for system safety, which is often evaluated by computational thermal analysis. However, conventional thermal analysis methods are faced with the contradiction between the complexity of modeling and the accuracy of computation. To fix the problem, a new two-way multiphysics coupling method is developed by proposing an improved equivalent homogeneous winding thermal model. The energy consumption distribution is used as the heat source for thermal field calculation, and the material properties are determined by temperature in the electromagnetic field. To further reduce the complexity of winding region modeling and ensure the accuracy of calculation, an improved equivalent homogeneous winding thermal model is established by considering the fill factor and void ratio. The new coupling method is verified by temperature rise experiments on a single winding sample and a PMLM prototype. The error between the simulation and experiment of the single winding sample is less than 3 %, which indicates the high accuracy of the improved equivalent model. Meanwhile, the low error (less than 6 %) on the PMLM case indicates the high accuracy of the new multiphysics coupling method. Then, the temperature distribution of PMLM is analyzed, and the effect of the fill factor and void ratio on the temperature rise of PMLM is studied with the effect of the void ratio on the limiting temperature and continuous working time of PMLM discussed. It is demonstrated that both fill factor and void ratio affect the temperature rise of winding, and thus impact the temperature rise of the PMLM. The continuous working time decreases rapidly with the increase of the void ratio, and this effect will be more significant with a higher effective current. The results can provide references for improving the precision of temperature estimation and selecting a reasonable current density. This also demonstrates the high accuracy and efficiency of the new coupling method.

A new analytical model for force prediction in incremental sheet forming

A new analytical model for forming force prediction in the whole process of incremental sheet forming (ISF) is presented in this work. The modelling of contact region, thickness distribution and strain components are carried out as the basis for the prediction of vertical and horizontal forces. The effect of local contact behaviour and material deformation, as well as the behaviours of global bending, changes of deformation mode and contact condition are taken into account in the modelling of ISF force history. The force prediction model at both plane strain and biaxial tension condition areas is developed based on the differences in contact region and thickness distribution between these two different deformation states. Validated by ISF tests for a truncated cone and pyramid part with varying drawing angles, the prediction of force history captures well the fluctuation of different force components due to the change of deformation conditions and provides an insight into the material deformation mechanisms and the direct correlation to forming force characteristics of the whole ISF process. In a practical case, the model is successfully applied to the force prediction in ISF-based cranial plate manufacture. The model is also validated by a series of ISF tests with accurate force predictions of both the peak and stabilised forces.

Soil erosion in diverse agroecological regions of India: a comprehensive review of USLE-based modelling.

Soil erosion caused by water refers to the removal of topsoil by rainfall and runoff. Proper selection of an assessment method is crucial for quantifying the spatial variance of soil erosion. The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are widely used for modelling soil erosion. This study aimed to evaluate the effectiveness of the USLE-based soil erosion modelling in different agroecological regions of India, identify potential issues, and provide suggestions for future applications. The review revealed that little attention has been given to estimate soil erosion in high-priority land degradation regions of India. Additionally, many studies failed to thoroughly verify the authenticity of stated soil loss rates in their research regions either by overestimating or underestimating at least one of the five soil loss parameters. Furthermore, flaws in the application of methods to calculate these parameters leading to erroneous values were identified and suggestions for improvement were made. The USLE-based soil erosion modelling is an effective tool for quantifying soil erosion risk, but researchers should put emphasis on thoroughly verifying the methodologies adopted, unit conversions, and data availability for the estimation of soil loss parameters to improve the accuracy of their final results. This paper provides valuable insights to assist researchers in implementing USLE-based erosion models in diverse agroecological regions in India and elsewhere. However, for effective soil conservation and sustainable agriculture, further research is necessary to develop efficient techniques for using USLE-based soil erosion modelling to achieve a comprehensive understanding of erosion risk across different agroecological regions.

An Efficient Image Deblurring Network with a Hybrid Architecture.

Blurring is one of the main degradation factors in image degradation, so image deblurring is of great interest as a fundamental problem in low-level computer vision. Because of the limited receptive field, traditional CNNs lack global fuzzy region modeling, and do not make full use of rich context information between features. Recently, a transformer-based neural network structure has performed well in natural language tasks, inspiring rapid development in the field of defuzzification. Therefore, in this paper, a hybrid architecture based on CNN and transformers is used for image deblurring. Specifically, we first extract the shallow features of the blurred images using a cross-layer feature fusion block that emphasizes the contextual information of each feature extraction layer. Secondly, an efficient transformer module for extracting deep features is designed, which fully aggregates feature information at medium and long distances using vertical and horizontal intra- and inter-strip attention layers, and a dual gating mechanism is used as a feedforward neural network, which effectively reduces redundant features. Finally, the cross-layer feature fusion block is used to complement the feature information to obtain the deblurred image. Extensive experimental results on publicly available benchmark datasets GoPro, HIDE, and the real dataset RealBlur show that the proposed method outperforms the current mainstream deblurring algorithms and recovers the edge contours and texture details of the images more clearly.

Landuse-Landcover Mapping and Modelling in different Ecological regions of the Monsoon International School and Symposium – 2023

Landuse-Landcover Mapping and Modelling in different Ecological regions of the Monsoon International School and Symposium – 2023

River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models

River Flow Modeling in Semi-Arid and Humid Regions Using an Integrated Method Based on LARS-WG and LSTM Models

TBE in China

The first TBE patients in China were reported in 1943, and the TBEV was isolated from the brain tissues of 2 patients in 1944 by Japanese military scientists, and from patients and ticks (Ixodes persulcatus and Haemaphysalis concinna) in 1952 by Chinese researchers. The Far Eastern viral subtype (TBEV-FE) is the endemic subtype that has been isolated from all 3 known natural foci (northeastern China, western China, and southwestern China). Recently a new “Himalayan subtype” of the TBEV (TBEV-HIM) was isolated from wild rodent Marmoata himalayana in the Qinghai-Tibet Plateau. The main vector of the TBEV in China is I. persulcatus. One recent report suggests that the TBEV-SIB is prevalent in the Uygur region (North West China) but epidemiological modelling indicates that the TBEV may occur even widely all over China (Figure 3). Likely, the disease is often missed by clinicians due to a lack of the availability of specific diagnostic assays.