Method For Models Research Articles

Predicting real-time roadway pluvial flood risk: A hybrid machine learning approach coupling a graph-based flood spreading model, historical vulnerabilities, and Waze data

Urban pluvial flash flooding (PFF), driven by extreme weather and urban expansion, introduces complex challenges that arise from the dynamic interaction of rainfall hazard, road vulnerability, and traffic exposure. These three critical components must be interconnected to provide a comprehensive prediction of roadway PFF risk. Our integrated approach combines historical data and real-time Waze flood alerts using a simplified physics-based PFF model and hybrid machine learning methods to predict flash flooding risk at the road segment scale. In a Dallas case study with four intersections, we trained multiple models with data from 15 storms and tested on 5 storms. The XGBoost method excels in test precision, while a Random Forest model offers better recall, and both methods outperform Support Vector Machines (SVM). The choice between models depends on factors such as negative class (prediction of unflooded areas) uncertainty and false positive cost (i.e., predicting no flooding incorrectly). For the case study, our approach could boost flood awareness, enhance safety, and improve urban flood management by correctly predicting 73% of risk observations during the test storm events.

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

A Three-Grid High-Order Immersed Finite Element Method for the Analysis of CAD Models

The automated finite element analysis of complex CAD models using boundary-fitted meshes is rife with difficulties. Immersed finite element methods are intrinsically more robust but usually less accurate. In this work, we introduce an efficient, robust, high-order immersed finite element method for complex CAD models. Our approach relies on three adaptive structured grids: a geometry grid for representing the implicit geometry, a finite element grid for discretising physical fields and a quadrature grid for evaluating the finite element integrals. The geometry grid is a sparse VDB (Volumetric Dynamic B+ tree) grid that is highly refined close to physical domain boundaries. The finite element grid consists of a forest of octree grids distributed over several processors, and the quadrature grid in each finite element cell is an octree grid constructed in a bottom-up fashion. The resolution of the quadrature grid ensures that finite element integrals are evaluated with sufficient accuracy and that any sub-grid geometric features, like small holes or corners, are resolved up to a desired resolution. The conceptual simplicity and modularity of our approach make it possible to reuse open-source libraries, i.e. openVDB and p4est for implementing the geometry and finite element grids, respectively, and BDDCML for iteratively solving the discrete systems of equations in parallel using domain decomposition. We demonstrate the efficiency and robustness of the proposed approach by solving the Poisson equation on domains described by complex CAD models and discretised with tens of millions of degrees of freedom. The solution field is discretised using linear and quadratic Lagrange basis functions.

Online modeling method for composite load model including EVs and battery storage based on measurement data

Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time-varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PurposeModel-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.MethodsPediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.ResultsTen pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.ConclusionBased on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Cardiovascular risk factors and aortic valve stenosis: towards 10-year absolute risk charts for primary prevention.

Due to aging populations the incidence of aortic valve stenosis (AVS) is increasing steeply. Since no medical therapy is available but only surgical interventions, it is highly warranted to identify modifiable risk factors for early prevention. The aim of the study was to investigate the associations of cardiovascular risk factors with AVS and to create 10-year absolute risk scores for use in primary prevention. In the Copenhagen General Population Study (N=93,979) lifestyle data, biochemical measures, and confounders were assessed at baseline. Risk factors with the strongest association with aortic valve stenosis from Cox regression analyses were included in ten-year risk prediction models. Ten-year absolute risk scores were conducted using the method of Fine-Gray proportional sub-hazards models, accounting for competing events. 1,132 individuals developed AVS during follow-up. Of well-known cardiovascular risk factors, those that associated with AVS included increasing levels of remnant cholesterol, triglycerides, lipoprotein(a), systolic blood pressure, and body mass index, low adherence to Danish dietary guidelines, current smoking, high alcohol consumption, lipid-lowering therapy and diabetes mellitus. Ten-year absolute risk scores increased when compiling the most important risk factors for AVS; age, sex, body mass index, systolic blood pressure, lipoprotein(a), and diabetes. Ten-year absolute risk increased from <1% to 19%. The presence of cardiovascular risk factors is associated with AVS, supporting that this disease, at least partly, may be modifiable through lifestyle changes. Risk charts combining cardiovascular risk factors have the potential to identify high-risk individuals, offering opportunities for preventive strategies. (Word count 245).

Model-based process optimization of black soldier fly egg production.

Black soldier fly (BSF) larvae (Hermetia illucens) serve as a valuable protein source for animal feed. Limiting factors in the industrial rearing of BSF are the reproduction process and egg output. Studies indicate the potential to shorten preoviposition time and increase egg output through better utilization of environmental variables, such as temperature and light, in industrial settings. Excessive stimulation, however, can lead to stress, elevated production costs, and reduced egg numbers, emphasizing the need for a delicate balance. This study addresses these challenges by investigating controlled manipulation of environmental variables to stimulate mating and enhance egg production, thereby developing a comprehensive model encompassing the adult fly life cycle, mating, and egg production. Model parameters were fitted using literature data, and the model's plausibility was tested through simulations. Using the model and optimal control methods, the calculated dynamic trajectories for environmental variables when compared to the standard approach in a constant environment demonstrated higher output and shorter production cycles at reasonable energy costs. Applications for this model-based optimization are demonstrated for various scenarios, highlighting the practical utility and versatility of the developed model. This study contributes valuable insights for improving rearing practices of BSF through environmental stimulation, offering potential advancements in egg production efficiency and overall sustainability.

A modular decentralized control system of multi-connected processes in the presence of structural disturbances

The object of research is digital systems of decentralized control of quasi-stationary multi-connected processes taking into account structural disturbances. It was determined that the promising development of decentralized control of multi-connected processes is research related to considering the possibility of its application for real situations of partial disruption of interconnections between subsystems in multidimensional stochastic systems. Such studies are not sufficiently developed today, therefore the topic of the proposed work should be considered relevant from the theoretical and applied points of view. The problem of decentralized management of multi-connected stochastic objects is formalized, taking into account the internal relationships between their local subsystems of the system. A method of decentralization of a multi-connected system is proposed, the advantage of which is the use of simple computational procedures of transformation and permutation of matrix elements of the global model. The concept of c-robustness of a multi-connected dynamic system is introduced, which guarantees its asymptotic stability and suboptimality in the event of structural disturbances. Methods of robust decentralized control are proposed for cases of explicit and implicit tasks of interconnections between local subsystems. The proposed methods have a certain advantage in comparison with the method of local control coordination, which consists in increasing the efficiency of decentralized control with a guaranteed level of suboptimality. Experimental modeling of the considered method for models of multi-connected chemical-technological systems describing the relationship between the input and output parameters of the processes was carried out. During the simulation, the global system was decomposed into 4 local subsystems. Modeling of the task of decentralized management was also carried out according to the well-known method of coordination, based on finding the values of uncertain multipliers. A comparative analysis of the results obtained using this standard method and the proposed robust methods shows the superiority of the latter.

Benchmarking the accuracy of higher-order particle methods in geodynamic models of transient flow

Abstract. Numerical models are a powerful tool for investigating the dynamic processes in the interior of the Earth and other planets, but the reliability and predictive power of these discretized models depends on the numerical method as well as an accurate representation of material properties in space and time. In the specific context of geodynamic models, particle methods have been applied extensively because of their suitability for advection-dominated processes and have been used in applications such as tracking the composition of solid rock and melt in the Earth's mantle, fluids in lithospheric- and crustal-scale models, light elements in the liquid core, and deformation properties like accumulated finite strain or mineral grain size, along with many applications outside the Earth sciences. There have been significant benchmarking efforts to measure the accuracy and convergence behavior of particle methods, but these efforts have largely been limited to instantaneous solutions, or time-dependent models without analytical solutions. As a consequence, there is little understanding about the interplay of particle advection errors and errors introduced in the solution of the underlying transient, nonlinear flow equations. To address these limitations, we present two new dynamic benchmarks for transient Stokes flow with analytical solutions that allow us to quantify the accuracy of various advection methods in nonlinear flow. We use these benchmarks to measure the accuracy of our particle algorithm as implemented in the ASPECT geodynamic modeling software against commonly employed field methods and analytical solutions. In particular, we quantify if an algorithm that is higher-order accurate in time will allow for better overall model accuracy and verify that our algorithm reaches its intended optimal convergence rate. We then document that the observed increased accuracy of higher-order algorithms matters for geodynamic applications with an example of modeling small-scale convection underneath an oceanic plate and show that the predicted place and time of onset of small-scale convection depends significantly on the chosen particle advection method. Descriptions and implementations of our benchmarks are openly available and can be used to verify other advection algorithms. The availability of accurate, scalable, and efficient particle methods as part of the widely used open-source code ASPECT will allow geodynamicists to investigate complex time-dependent geodynamic processes such as elastic deformation, anisotropic fabric development, melt generation and migration, and grain damage.

An integrative production-delivery system with mobile additive manufacturing and truck-drone delivery considering optimal printing sequence and preferred delivery time window

ABSTRACT Mobile additive manufacturing (MAM) involves deploying 3D printing in moving vehicles and has recently garnered significant attention from academia and industry. However, no prior work integrates MAM with truck-drone delivery systems to support daily operations, despite the benefits to supply chain management. This paper fills this gap by integrating these technologies, considering customer delivery time windows and optimal printing sequences. A mixed-integer linear programming model is proposed to establish this innovative mechanism. Both exact (CPLEX) and heuristic (Lagrangian relaxation algorithm) approaches are utilized and compared to solve the model. Computational studies validate the model and solution methods across various problem sizes (small, medium, and large), showing the superior performance of Lagrangian relaxation in optimality gap and CPU time. The model is then applied in a case study using the Lagrangian relaxation algorithm. Results indicate that drone flight range and printing job complexity significantly impact cost performance and on-time delivery. Beyond achieving timely and cost-effective customer satisfaction, this system offers insights into supply chain resilience, enabling companies to enhance service quality and reduce inventory dependence through on-demand spare part production via MAM technology.

Exploration and Research on the Project-Driven Teaching Mode of Building Materials and Testing Experimental Course

The project-driven experimental course teaching model focuses on the cultivation of students’ practical and innovative abilities, with projects as the core. This study takes the course “Building Materials and Testing” as an example to explore and evaluate the effectiveness of specific implementation methods of the project-driven experimental course teaching model. Through practical case analysis, it was found that this teaching model effectively enhanced the student’s practical hands-on abilities, innovative thinking abilities, and teamwork abilities. In this teaching model, students are not just passive recipients of knowledge, but actively learn by participating in project practice. They need to apply the knowledge they have learned to solve practical problems and cooperate with team members to complete project tasks. This learning approach makes students more proactive and positive, cultivating their practical hands-on abilities and teamwork abilities. With this, the teaching of experimental courses in architectural majors can refer to and draw lessons from the project-driven experimental course teaching model. However, teachers need to pay attention to solving potential problems when implementing this teaching model to ensure optimal teaching outcomes.

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

Utilizing Geological and Geoelectrical methods in a GIS-based DRASTIC model of the Probable Groundwater Vulnerability in the Southern Nigerian Raffia Metropolis of Ikot Ekpene and its Surroundings

The city of Ikot Ekpene is facing a significant issue with groundwater contamination. This is due to the rising amount of home and industrial waste caused by the increasing human population and various business operations in the area. This study seeks to evaluate the susceptibility of groundwater in the metropolis of Ikot Ekpene and its surrounding areas in southern Nigeria. It will utilize integrated geoelectrical and geological approaches within the GIS-based DRASTIC model. A total of twenty vertical electrical soundings (VES) were conducted in the region using the Schlumberger array. Based on the geological drilling data, the interpretation of the VES data suggests that the area consists of 3-4 geoelectric layers. The lithological succession in the area exhibits a range of sediment types, including fine and coarse sand as well as gravelly sands. Additionally, there are localized occurrences of clay intercalations. The third geoelectric layer, located at a depth of 9.0–86.6 m, is the primary aquifer that can be utilized in the region. The DRASTIC model included seven environmental parameters, including depth to the water table, net recharge, aquifer medium, topography, impact of vadose zone, and hydraulic conductivity, with the purpose of conducting a vulnerability assessment. The assessment of groundwater vulnerability ratings (GVR) reveals that 75% of the research region is classified as high vulnerability, 20% as moderate vulnerability, and the remaining 5% as low vulnerability. The studied area is predominantly characterized by moderate to high Groundwater Vulnerability Rating (GVR), which is likely attributed to the generally gentle topography and the presence of highly permeable geomaterials in the upper levels of the water table.

Comparison of outcomes between off-pump and on-pump coronary artery bypass graft surgery using collaborative targeted maximum likelihood estimation

There are some discrepancies about the superiority of the off-pump coronary artery bypass grafting (CABG) surgery over the conventional cardiopulmonary bypass (on-pump). The aim of this study was estimating risk ratio of mortality in the off-pump coronary bypass compared with the on-pump using a causal model known as collaborative targeted maximum likelihood estimation (C-TMLE). The data of the Tehran Heart Cohort study from 2007 to 2020 was used. A collaborative targeted maximum likelihood estimation and targeted maximum likelihood estimation, and propensity score (PS) adjustment methods were used to estimate causal risk ratio adjusting for the minimum sufficient set of confounders, and the results were compared. Among 24,883 participants (73.6% male), 5566 patients died during an average of 8.2 years of follow-up. The risk ratio estimates (95% confidence intervals) by unadjusted log-binomial regression model, PS adjustment, TMLE, and C-TMLE methods were 0.86 (0.78–0.95), 0.88 (0.80–0.97), 0.88 (0.80–0.97), and 0.87(0.85–0.89), respectively. This study provides evidence for a protective effect of off-pump surgery on mortality risk for up to 8 years in diabetic and non-diabetic patients.

Comprehensive analysis of the bZIP gene family in ‘Duli’ pear (Pyrus betulifolia Bunge): Insights for research on abiotic stress, especially drought, in pear

Plants are unavoidably subjected to various abiotic stresses, such as drought, salt, and cold, during their growth and development. Drought, accompanied by water shortages in North China, has become an environmental factor seriously affecting agriculture production. Basic leucine zipper (bZIP) is one of the most diverse transcription factor families in plants. It participates in the regulation of many physiological processes and plays an important role in plants’ response to abiotic stress. In this study, for the first time, 88 bZIP family members (PbbZIPs) in ‘Duli’ pear (Pyrus betulifolia Bunge) were identified using both hidden Markov model (HMM) and basic local alignment search tool (BLAST) methods. A phylogenetic tree of all the PbbZIPs members was constructed. The physicochemical properties, subcellular localisation, chromosomal location, gene structure, conserved motifs and domains, secondary and tertiary structures, and cis-acting promoter elements of the 88 family members were comprehensively analysed. The results indicated that PbbZIPs may play relevant roles in stress response in ‘Duli’. Therefore, we determined the expressions of all PbbZIPs in ‘Duli’ seedlings under drought, salt, and cold conditions, and in seedlings treated with stress-related hormones such as abscisic acid (ABA), salicylic acid (SA), methyl jasmonate (MeJA), and melatonin (MT). PbbZIPs responsive to various abiotic stresses and hormones were screened. In addition, we determined the expression patterns of 12 PbbZIP genes that strongly responded to both drought and ABA in ‘Duli’ tissue culture plants treated with different concentrations of PEG4000 at different time points. Finally, we selected three key genes (PbbZIP65, PbbZIP77 and PbbZIP81) that may play a major role in drought resistance in ‘Duli’, and preliminarily identified their function through the VIGS method, finding that PbbZIP77 could positively regulate the drought resistance of ‘Duli’ seedlings, while PbbZIP65/81 could negatively regulate the drought resistance of ‘Duli’ seedlings. This study not only broadens our knowledge about the bZIP gene family in ‘Duli’ pear, but also provides notable guidance for further functional research on PbbZIPs in abiotic stress, especially drought resistance, in the pear plant.

Effects of Total Body Irradiation on Hematopoietic Cell Transplantation Outcomes in Pediatric Acute Myeloid Leukemia with Prior Central Nervous System Involvement

The implications of previous central nervous system (CNS) involvement in children with acute myeloid leukemia (AML) undergoing hematopoietic cell transplantation (HCT) remain inadequately understood. Patients with CNS disease require more upfront CNS-directed intrathecal therapy, but little is known about whether transplant conditioning regimens should be intensified or if previous CNS involvement impacts post-HCT outcomes. While total body irradiation (TBI) remains standard for pediatric acute lymphoblastic leukemia myeloablative conditioning, it has been largely replaced with chemotherapy-only myeloablation in pediatric AML, primarily due to toxicity and late effects associated with TBI. In the setting of previous CNS involvement, it has been suggested that TBI-based myeloablation may have advantages due to superior CNS tissue penetration and thus decreased rates of AML relapse post-HCT. We analyzed a publicly available dataset derived from the Center for International Blood and Marrow Transplantation Research (CIBMTR) registry to characterize the impact of TBI in HCT preparative regimens in pediatric AML patients with a history of CNS involvement. The study dataset was obtained from the CIBMTR data repository. The study cohort included patients aged ≤21 years who underwent initial allogeneic HCT with myeloablative conditioning for de novo AML in the first or second complete remission (CR) between 2008 and 2016, who provided consent for research. Patients with mismatched related donor transplants and noncalcineurin inhibitor graft-versus-host disease (GVHD) prophylaxis were excluded. The dataset was further modified by excluding patients with missing disease site data or those with non-CNS extramedullary disease. Patients were categorized as CNS-positive or -negative AML (AML-CNS(+) and AML-CNS(-), respectively) based on the disease status at diagnosis. The Cox regression model and Fine-Grey methods were employed to delineate the effects of TBI and CNS disease on key HCT outcomes. The study cohort comprised 550 pediatric AML patients, of which 25% (n = 136) were AML-CNS(+). CNS involvement was more prevalent in patients aged 0 to 3 years, patients who were in the second CR, and those with a mismatched unrelated donor or umbilical cord blood. AML-CNS(+) patients demonstrated a lower relapse rate (hazard ratio: 0.50, 95% confidence interval: 0.33 to 0.76) compared to AML-CNS(-) patients, with comparable disease-free survival (DFS) and overall survival (OS) (P = .10 and 0.20, respectively) in the two cohorts. The entire TBI-treated cohort showed an association with increased risks of grade 2 to 4 acute GVHD, bloodstream infections, and endocrine dysfunction. TBI use within the AML-CNS(+) cohort was associated with a lower relapse rate but increased risks of nonrelapse mortality and a trend of higher grade 3 to 4 acute GVHD. In this population-based analysis of pediatric patients with de novo AML undergoing HCT, TBI-based conditioning regimens did not confer an advantage in DFS or OS compared to non-TBI regimens, irrespective of CNS disease status. However, TBI use was associated with increased risks of short- and long-term comorbidities. These findings underscore the need for careful consideration of TBI in pediatric AML.

Complex fracture description and quasi-elastic energy development mathematical model for shale oil reservoirs

Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is ​​constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Study of Light Pollution Risk Evaluation Model

The invention and widespread use of artificial light are important progress in human history, but the light pollution formed by its excessive or poor use is a serious danger to biodiversity and ecosystems, etc. Behind the bright lights of the busy city, it is also a metaphor for the bright starry sky far away from us. Though using the theory and methods of risk identification and evaluation hierarchical model, and unconfirmed measurement model, the index system and the measuring method on the risk level of light pollution are established, the Light pollution risk evaluation model is formed, and they are applied to interpret their results for the four diverse types of locations. The order of the ratings is urban communities, suburban communities, rural communities, and protected land according to high to low. They meet the results from the literature, which confirms the reasonableness of the risk level measures developed. Our work provides a certain reference for the research and treatment of light pollution.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.