Level Simulation Research Articles

A hybrid coupling model of groundwater level simulation considering hydrogeological parameter: a case study of Nantong City in Eastern China

Abstract Groundwater level dynamic monitoring data have the characteristics of spatio-temporal non-smoothness and strong spatio-temporal correlation. However, the current groundwater level prediction model is insufficient to consider the spatio-temporal factors of the groundwater level and the autocorrelation of spatio-temporal series, particularly the lack of consideration of hydrogeological conditions in the actual study area. Thus, this study constructed a model based on the hydrogeological conditions and the spatio-temporal characteristics of the dynamic monitoring data of groundwater in the porous confined aquifer III in Nantong, the northern wing of the Yangtze River Delta, China. The spatial autocorrelation coefficient of the hydrogeology important parameter, permeability coefficient K, is used to optimize the distance weighting coefficient of monitoring wells obtained by the K-nearest neighbor (KNN) algorithm and then reconstruct the spatio-temporal dataset and long short-term memory (LSTM) network. A spatio-temporal groundwater level prediction model LSTM-K-KNN that introduces the spatial autocorrelation of hydrogeological parameters was constructed. The reliability and accuracy of LSTM-K-KNN, LSTM, autoregressive integrated moving average (ARIMA) model, and support vector machine (SVM) were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of LSTM-K-KNN is 19.86, 43.64, and 52.38% higher than that of the other single prediction models (LSTM, ARIMA, and SVM).

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

Impact of Hydrological Changes on Wetland Landscape Dynamics and Implications for Ecohydrological Restoration in Honghe National Nature Reserve, Northeast China

Marsh wetlands are significant ecosystems located between land and water bodies which can both protect species diversity and provide habitats. Changes in the hydrological situation of marsh wetlands as a result of climate change and human activities have led to the degradation of wetland landscapes. Taking the Honghe National Nature Reserve (HNNR) in Heilongjiang Province, China, as an example, this paper gathered information on the reserve’s wetland landscape before and after dam construction. The information was obtained using field survey data and a random forest classification method based on Landsat data powered by the Google Earth Engine (GEE) cloud platform. Then, on the basis of the water level data and the digital elevation model, the wetland landscape dynamics of HNNR under three water level conditions were simulated. The findings were as follows: (1) From 1998 to 2008, the area of marsh and meadow had a downward trend, while the area of forest, farmland and water showed a gradual and upward trend; a marked rise in the area of marsh and a continued rise in the area of forest, farmland and water, and a sharp decline in the area of meadow during 2008 to 2018 was observed. (2) There was a significant increase in the area of marsh under the 20 and 40 cm water level simulation scenarios, with a decrease in the number of patches, and an increase in the aggregation index with rising water levels; in contrast, when the water level rose to 60 cm, the area of marsh and the number of patches decreased, but the aggregation index continued to increase. (3) The correlation between wetland landscape and the water level was a nonlinear one. The area of marsh increased and then decreased with increasing water level, reaching a maximum at the 40 cm water level; therefore, 40 cm was the optimal water level regulation scenario. Hydrological processes are the most fundamental ecological processes in marsh wetlands. Understanding the scientific pattern of the spatial pattern characteristics of species as a function of water level environment is important for scientifically guiding the restoration of marsh vegetation.

Combining process-based model and machine learning to predict hydrological regimes in floodplain wetlands under climate change

Climate change has remarkable effects on the hydrological regime in floodplain wetlands. However, the hydrodynamic simulation under climate change conditions suffers from the uncertainty of the downstream water level boundary. In this study, an effective approach was developed by integrating an advanced machine learning algorithm and a process-based watershed hydrological model to predict the lake level under climate change. The developed approach was validated in a vital conservation wetland in the Ramsar Convention, the Poyang Lake floodplain wetland in China and utilized to simulate the hydrological regime in climate change. We found that the Long Short–term Memory (LSTM) performed relatively well in the simulation of water level, with coefficients of determination (R2) of 0.94 and 0.83 in the training and testing periods, respectively. The mean cumulative flooding time in the future period was slightly less than that in the baseline period, with a decreased range of 1.01 ∼ 12.05 days/year. The highest and mean water levels rose significantly in the wet season, with a risen range of 1.01 ∼ 3.3 and 1.56 ∼ 2.44 m, respectively, and the appearance of extreme flood events became more frequent. The maximum, minimum, and mean inflows for Poyang Lake in the dry season exhibited a remarkable reduction trend, especially for the minimum inflow, with a reduced range of 59.37%∼79.22%, indicating that more serious extreme drought events would appear in Poyang Lake. The developed approach has good performance for water level simulation and extreme runoff prediction, thus providing a viable way for future water resources management in floodplain wetlands.

Application of Macro Element Method (MEM) for faster automotive crash safety design during concept stage

The non-linear nature of vehicle crash safety simulations using the Finite Element Method (FEM) usually requires a significant investment in computational time and resources, in addition to the requirement of full CAD (Computer Aided Design) data availability, which is challenging to obtain during the concept stage. The current work explores the usage of Macro Element Method (MEM) based on simplified Super Beam Element (SBE), for which the shape functions are developed based on experimental observations rather than algebraic equations. MEM crash analysis run time doesn’t exceed several seconds and is one of the underutilised analytical models which can be used to quickly explore new design directions during the early concept stage of the vehicle and has higher potential to speed up the product development process. In this paper, initially, a calibration study was done to validate the MEM at a component level to compare the results with FEM and experimental values. FEM and MEM simulation models were developed and evaluated for a metallic thin-walled square frusta with varying semi-apical angles subjected to axial compression. The results were compared, and it was found that MEM results show more than 97% correlation compared to FEM and experimental results. Subsequently, a design modification was introduced in the component and the results show close degree of correlation between MEM and FEM. Similar process was followed for full vehicle level simulations, such as full width frontal crash, side pole crash analyses to understand the robustness of the method in applying it to product development environment.

Rechargeable Multifunctional Anti-Bacterial AEMs for Electrodialysis: Improving Anti-Biological Performance via Synergistic Antibacterial Mechanism.

Constructing a functional layer on the surface of commercial membrane (as a substrate) to inhibit the formation of biofilms is an efficient strategy to prepare an antibacterial anion exchange membrane (AEM). Herein, a rechargeable multifunctional anti-biological system is reported by utilizing the mussel-inspired L-dopa connection function on commercial AEMs. Cobalt nanoparticles (Co NPs) and N-chloramine compounds are deposited on the AEM surface by a two-step modification procedure. The anti-biofouling abilities of the membranes are qualitatively and quantitatively analyzed by adopting common Gram-negative (E. coli) and Gram-positive (S. aureus & Bacillus) bacteria as model biofouling organisms. The optimized membrane exhibits a high stability concerning the NaCl solution separation performance within 240 min. Meantime, the mechanism of the anti-adhesion is un-veiled at an atomic level and molecular dynamics (MD) simulation are conducted to measure the interaction, adsorption energy and average loading by using lipopolysaccharide (LPS) of E. coli. In view of the superior performance of antibacterial surfaces, it is believed that this work could provide a valuable guideline for the design of membrane materials with resistance to biological contamination.

Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China

Water level prediction in large dammed rivers is an important task for flood control, hydropower generation, and ecological protection. The variations of water levels in large rivers are traditionally simulated based on hydrological models. Recently, most studies have begun applying deep learning (DL) models as an alternative method for forecasting the dynamics of water levels. However, it is still challenging to directly apply DL to the simultaneous prediction of water levels across multiple sites. This study attempts to develop a hybrid framework by combining the Physical-based Hydrological model (PHM) and Long Short-Term Memory (LSTM). This study hypothesizes that our hybrid model can enhance the predictive accuracy of water levels in large rivers, because it considers the temporal-spatial information of mainstream-tributaries relationships. The effectiveness of the proposed model (PHM-BP-LSTM) is evaluated using the daily water levels from 2012 to 2018 in the Three Gorges Reservoir (TGR), China. Firstly, we use a hydrological model to produce a large amount of water level data to solve the limited training data set. Then, we use the Back Propagation (BP) neural network to capture the mainstream-tributaries relationship. The future changes in water levels in the different mainstream stations are simultaneously predicted by the LSTM model. We reveal that our hybrid model yields satisfactory accuracy for daily water level simulations at fourteen mainstream stations of the TGR. We further demonstrate the proposed model outperforms the traditional machine learning methods in different prediction scenarios (one-day-ahead, three-day-ahead, seven-day-ahead), with RMSE values ranging from 0.793 m to 1.918 m, MAE values ranging from 0.489 m to 1.321 m, and the average relative errors at each mainstream station are controlled below 4%. Overall, our PHM-BP-LSTM, combining physical process and deep learning, can be viewed as a potentially useful approach for water level prediction in the TGR, and possibly for the rapid forecast of changes in water levels in other large rivers.

To Lie or Not to Lie in Non-Cooperative User-Centric Networks: Is Lying Worth It?

This paper presents a load-aware network selection model intended to help users to determine whether or not to connect to a macro cell (MC) or a WiFi access point (AP) in non-cooperative user-centric networks. The problem is formulated as a game theoretic model in which users selfishly maximize their throughput. Unlike in most existing work, we do not assume that users have complete information about the other users’ dynamics, which makes it more realistic in a communication network with distributed users. Then, because the network selection decision depends crucially on truthful reporting of channel states by the users, we explore the idea of non-cooperative users sending signals that are likely to induce the scheduler to behave in a manner beneficial to them. We provide five procedures which consist of introducing hierarchy among the users reflecting their channel quality and dividing them into groups interfering with each other, but not within themselves. Having done this, we allow them to sequentially choose their preferred network. We also propose a solution to compel users to reveal the truthful signals to the macro eNodeB (MeNb) by designing an additional immunity parameter mainly meant to keep lying users from harming truthful users. Particularly noteworthy is the fact that the additional immunity parameter does not only decrease the gain of liars, but it further improves the overall system performance. We provide extensive system level simulation results comparing our procedures between themselves and with traditional schemes. It is shown that the proposed solutions outperform classical approaches in almost every respect.

Hardware/Software Co-Design of Cryptographic SoC Based on RISC-V Virtual Prototype

Embedded hardware accelerator with limited resources is increasingly employed in security areas. To accelerate system-on-chip (SoC) design, an efficient HW/SW co-design approach and validation platform become extremely important. The Electronic System Level Simulator (ESL) based on SystemC is the primary solution for fast hardware modeling and verification. However, most existing simulators cannot achieve a better trade-off between accuracy and performance, and none of the specific ESL simulators are proposed for cryptographic SoCs. To this end, this brief proposes a virtual prototype (VP) with integrated cryptographic accelerators for a cryptographic SoC based on RISC-V to accelerate the functional and performance simulation of the SoC. The VP is designed as an extensible and configurable platform dedicated to cryptographic SoC using an efficient HW/SW co-design approach. To accurately emulate real hardware, the flexible AHB-TLM interface and core timing model are presented. Compared to the RTL simulation, our custom VP performs about 10-450 times faster than the RTL simulation, and the simulation error is only about 4%. Our code is available at https://github.com/LX-IC/VP.

Location-based Power Control Mechanism for D2D Communication Underlaying a Cellular System

The paper presents a location-based approach to controlling the power of device-to-device (D2D) underlay of a frequency reuse-1 cellular system. The system allows for direct communication to share uplink resources with cellular users. As a result, both D2D and cellular users are experiencing additional interferences in the system. By controlling the output power of the devices, these interferences can be mitigated and the performance of the network can be improved in terms of better spectral and energy efficiency. The proposed location-based target signal-to-interference ratio power control scheme for D2D communications utilizes information about users' locations to estimate the interference level experienced by the receiver of the direct link. Based on this estimation, an appropriate transmit power can be determined. The performance of the proposed power control solution is investigated via system level simulations.

Groundwater Level Prediction with Deep Learning Methods

The development of civilization and the preservation of environmental ecosystems are strongly dependent on water resources. Typically, an insufficient supply of surface water resources for domestic, industrial, and agricultural needs is supplemented with groundwater resources. However, groundwater is a natural resource that must accumulate over many years and cannot be recovered after a short period of recharge. Therefore, the long-term management of groundwater resources is an important issue for sustainable development. The accurate prediction of groundwater levels is the first step in evaluating total water resources and their allocation. However, in the process of data collection, data may be lost due to various factors. Filling in missing data is a main problem that any research field must address. It is well known that to maintain data integrity, one effective approach is missing value imputation (MVI). In addition, it has been demonstrated that machine learning may be a better tool. Therefore, the main purpose of this study was to utilize a generative adversarial network (GAN) that consists of a generative model and a discriminative model for imputation. Although the GAN could not capture the groundwater level endpoints in every section, the overall simulation performance was still excellent to some extent. Our results show that the GAN can improve the accuracy of water resource evaluations. In the current study, two interdisciplinary deep learning methods, univariate and Seq2val (sequence-to-value), were used for groundwater level estimation. In addition to addressing the significance of the parameter conditions, the advantages and disadvantages of these two models in hydrological simulations were also discussed and compared. Regarding parameter selection, the simulation results for univariate analysis were better than those for Seq2val analysis. Finally, univariate was employed to examine the limits of the models in long-term water level simulations. Our results suggest that the accuracy of CNNs is better, while LSTM is better for the simulation of multistep prediction. Therefore, the interdisciplinary deep learning approach may be beneficial for providing a better evaluation of water resources.

Projection of Sea Level Change in the South China Sea Based on Dynamical Downscaling

The projection of future sea level change is usually based on the global climate models (GCMs); however, due to the low spatial resolution of the GCMs, the ability to reproduce the spatial heterogeneity of sea level is limited. In order to improve the sea level simulation capability in the South China Sea (SCS), a high-resolution ocean model has been established by using the dynamic downscaling technology. By evaluating and testing 20 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), average results of seven models were selected as the forcing condition of the high-resolution ocean model. The ocean model conducted the historical (1980~2014) and future (2015~2100) simulation under three scenarios of Shared Socio-economic Pathways (SSP1–2.6, SSP2–4.5 and SSP5–8.5). The selected average results of seven models in CMIP6 are better than any of them individually. The downscaled dynamic ocean model provides fruitful spatial characteristics of the sea level change, with a decrease in the dynamic sea level (DSL) in the central and southeastern parts of the SCS, and with a significant increase in the coastal DSL. The local steric sea level (SSL) is dominated by the local thermosteric sea level (TSSL), and the changes of local TSSL more than half of the sea level rise in SCS, indicate the magnitude of total sea level rise is dominated by local TSSL. But the spatial variation in total sea level is dominated by the spatial variation in DSL. Compared with CMIP5, the rise magnitude of the DSL and the local TSSL have been increased under the CMIP6 scenarios. The dynamic downscaling of sea level reveals more spatial details, provides more reliable projection of future sea level under the background of global warming, and can provide a new reference for coastal areas in the SCS to cope with the increasing risk of extreme water level disasters in the future.

Multiscale thermomechanical assessment of silicon carbide-based nanocomposites in solar energy harvesting applications

Increasing the operating temperature of concentrating solar power plants beyond 650 °C is crucial for increasing their efficiency and energy storage capability, and thus pushing their market penetration. However, one of the main drawbacks that arise with an increase in temperature is material degradation. Ceramic materials are an interesting alternative to nickel–chromium superalloys due to their high thermal resistance and low expansion capabilities. In this work, we characterize the thermomechanical behavior of a silicon carbide-based ceramic material for receiver tubes up to 750 °C. For this purpose, we develop a multiscale simulation framework, combining studies at different scales of observation (i.e., material, and component) using different simulation tools. At the material scale, we generate virtual microstructures that are subject to thermomechanical tests using the lattice model. The material properties obtained at this scale of observation are subsequently used at component level simulations, which are carried out by means of the finite element method. This allows the exploration of new materials designed in silico and the prediction of their performance in solar energy applications. In this work, we use our methodology to assess the thermoelastic behavior of SiSiC tubes at high temperatures and obtain temperature-dependent material properties. At the material level, we report elastic moduli in the range of 250–500 MPa and coefficients of thermal expansion in the range of 6–12·10−6 1/°C, under operation conditions. Regarding the component, we found that SiSiC tubes with a thickness-to-diameter ratio of 0.15 or larger are safe at operating temperatures up to 750 °C.

An optical scheme of on-chip matrixing by phase-change based tunable weighting of photonic tensor unit

In this paper, we propose an optical scheme of on-chip matrixing for matrix-vector multiplications (MVMs) by configuring each matrix element into a photonic tensor processing unit (TPU) with wavelength division multiplexing and the actively tunable weighting for scalar multiplication. The low loss chalcogenide phase change material of Ge2Sb2Se4Te (GSST) is employed and modeled with intermediate states for multilevel tunable weighting of each TPU. The dynamic electro-thermal process of GSST phase transition using ITO for electrical heating is simulated and well confirms the switchable weighting of TPUs. Simulation results reveal that a 7 V voltage pulse of 500 ns duration followed by a 9 V voltage pulse of 1500 ns duration and another pulse of 14 V voltage and 500 ns duration can set data value of ‘0’ and ‘1’ for each TPU. Taking a set of incident light with varied wavelengths and powers as input vectors, the quantized MVM outputs of photocurrents with varied amplitude are obtained after photoelectric conversion. Finally, the photonic integrated circuit level simulations by Lumerical INTERCONNECT perfectly confirm our scheme of on-chip matrixing for optical MVMs and computing.

MVNOCoreSim: A Digital Twin for Virtualized IoT-Centric Mobile Core Networks

The rapid growth of connected devices has led to the implementation of IoT-centric platforms designed to provide connectivity for machine-to-machine type communication. Specifically, mobile virtual network operators (MVNOs) provide global service for IoT use cases by leveraging roaming in readily deployed physical networks. This global deployment of connected devices poses a significant challenge regarding the operation of centralized core networks with respect to dimensioning, scaling as well as system survivability in case of signaling incidents. Overload control mechanisms for IoT mobile networks offer a proactive solution for mitigating excessive signaling traffic from IoT devices in mobile core networks. However, global scaling and a heterogeneous composition of devices present network operators with additional challenges. To address these challenges, this work presents a detailed, protocol level simulation framework of a real-world IoT MVNO core network. We present both models for the expected IoT signaling load and the processing of signaling messages based on measurements in a live, production environment as well as a dedicated testbed. Finally, we present a case study on various overload mechanisms and identify critical performance characteristics to compare their performance. The results of this study categorize overload control mechanisms and shed light on the necessity for MVNOs to deal with the upcoming IoT traffic by defining appropriate overload control mechanisms in mobile core networks to ensure network survivability under unforeseen conditions.

Empiric dosing strategies to predict lamotrigine concentrations during pregnancy.

Maintaining seizure control with lamotrigine is complicated by altered pharmacokinetics and existence of subpopulations in whom clearance increases or remains constant during pregnancy. Our objective was to characterize the potential for particular dosing scenarios to lead to increased seizure risk or toxicity. Lamotrigine pharmacokinetic parameters obtained from our previous study were applied to a one-compartment model structure with subpopulations (75:25%) exhibiting different clearance changes. A single-patient simulation was conducted with typical pharmacokinetic parameter values from each subpopulation. Population-level simulations (N = 48,000) included six dosing scenarios and considered four preconception doses using the R package mrgsolve (Metrum Research Group). Thresholds for efficacy and toxicity were selected as drug concentration that are 65% lower than preconception concentrations and doubling of preconception concentrations, respectively. Individual simulation results demonstrated that without dose increases, concentrations fell below 0.65 at 6-8 weeks in the high clearance change (HC) subpopulation, depending on preconception clearance. While no simulated dosing regimen allowed all women in both subpopulations to maintain preconception concentrations, some regimens provided a more balanced risk profile than others. Predicted concentrations suggested potential increased seizure risk for 7%-100% of women in the HC group depending on preconception dose and subpopulation. Additionally, in 63% of dosing scenarios for women with low clearance change (LC), there was an increased risk of toxicity (34%-100% of women). A substantial percentage of simulated individuals had concentrations low enough to potentially increase seizure risk or high enough to create toxicity. Early clearance changes indicate possible subpopulation categorization if therapeutic drug monitoring is conducted in the first trimester. An arbitrary "one-size-fits-all" philosophy may not work well for lamotrigine dosing adjustments during pregnancy and reinforces the need for therapeutic drug monitoring until a patient is determined to be in the LC or HC group.

Soft-Error-Aware Radiation-Hardened Ge-DLTFET-Based SRAM Cell Design

In this paper, a soft-error-aware radiation-hardened 6T SRAM cell has been implemented using germanium-based dopingless tunnel FET (Ge DLTFET). In a circuit level simulation, the device-circuit co-design approach is used. Semiconductor devices are very prone to the radiation environment; hence, finding out the solution to the problem became a necessity for the designers. Single event upset (SEU), also known as soft error, is one of the most frequent issues to tackle in semiconductor devices. To mitigate the effect of soft error due to single-event upset, the radiation-hardening-by-design (RHBD) technique has been employed for Ge DLTFET-based SRAM cells. This technique uses RC feedback paths between the two cross-coupled inverters of an SRAM cell. The soft-error sensitivity is estimated for a conventional and RHBD-based SRAM cell design. It is found that the RHBD-based SRAM cell design is more efficient to mitigate the soft-error effect in comparison to the conventional design. The delay and stability parameters, obtained from the N-curve, of the Ge DLTFET-based SRAM cell performs better than the conventional Si TFET-based SRAM cell. There is an improvement of 305x & 850x in the static power noise margin and write trip power values of the Ge DLTFET SRAM cell with respect to the conventional Si TFET SRAM cell.

A reduced-order electrochemical model for analyzing temperature distributions in a tubular solid oxide fuel cell stack

It is challenging to improve the computational efficiency of temperature predictions in tubular solid oxide fuel cell (SOFC) stacks, since the complex flow field usually leads to non-uniform heat generations and heat dissipations. In this study, we have developed a reduced-order electrochemical model for fast predicting the temperature distributions in a kilowatt-class tubular solid oxide fuel cell stack. This model can explicitly express the current density as a function of operating potential, concentration, and temperature, which can significantly reduce the computational cost of stack level simulations. The influence of flow characteristics on temperature distributions in the tubular SOFC stack is examined and analyzed. We show that the vortices formed by flow separations could affect temperature uniformity among all cells. At a low inlet air velocity, few vortices are formed, and the convective heat transfer is too weak to dissipate the heat generations from electrochemical reactions. As the air velocity is increased, the number and strength of vortices are both increased. These vortices enable the air stream to flow spirally and enhance the local convective heat transfer. We also demonstrate that as the air inlet velocity is increased from 1 m/s to 4 m/s, the temperature uniformity among tubular solid oxide fuel cells first decreases and then increases, but the output power is reduced from 2.19 kW to 1.78 kW due to the decrease in operating temperatures. The inlet air velocity should be carefully selected to balance the electrical and thermal performance of the SOFC stack.

Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization

The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.

Discussion of “The Asymmetric Impact of COVID-19: A Novel Approach to Quantifying Financial Distress across Industries”

The COVID-19 pandemic crisis and the associated lockdown measures have exerted significantly adverse effects on corporate sectors globally. Archanskaia et al. (2023) provide a novel empirical strategy to timely assess corporate financial distress in the EU. The contribution is two-fold. First, this paper’s notion of financial distress considers both the equity position and corporate indebtedness. Second, the methodology proposed in this paper allows the authors to estimate corporate financial distress in the EU at a highly granular level and link micro-level simulations to sectoral macroeconomic outcomes. The methodology employed by Archanskaia et al. (2023) consists of three steps. First, the authors apply a nowcasting model to acquire monthly industrial turnover data. Second, they feed the obtained monthly industrial turnover into a profit-generating process via an accounting identity to estimate monthly firm profits at the firm level. Third, the authors use the estimated firm profits with a snapshot of information on pre-existing liquid assets to deduce the firm-level liquidity needs and the depletion of equity through the focus period during COVID-19. These estimated results on firm equity position and indebtedness enable the authors to quantity corporate financial distress in the EU via various angles (e.g., country-level heterogeneity, industry heterogeneity, and the targeting of COVID support policies). The primary advantage of this approach is that it deals with large datasets at the granular level and produces firm-level results almost in real-time. Therefore, it can help policymaking track the effects of crises over time. However, one can quickly critique this three-step approach for its susceptibility to the usual Lucas critique. That said, since the objective here is to estimate firm-level financial distress, a large structural model being more or less aggregate in nature, though able to mitigate the Lucas critique concern, will encounter significant challenges in estimating firm-level results with the requisite level of granularity offered by the available data. Therefore, I broadly concur with the authors’ position that ‘the specific contribution of this paper consists in striking a better balance between the need to carry out a multi-country evaluation of the pandemic’s effects on industrial activity in a strongly integrated region like the EU and the difficulty of capturing time, industry, and country variation in turnover with sufficient granularity.’