Mixed Variables Research Articles

Lightning and radar measures of mixed phase updraft variability in tracked storms during the TRACER field campaign in Houston, Texas

Abstract Properties of 7488 thunderstorms are summarized for June-September 2022 during the TRACER field campaign Houston, Texas using polarimetric weather radar and VHF 3D lightning mapping array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day, and cells with lightning had stronger polarimetric columns. Track-level correlation of lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

Quantifying the Role of Ocean Dynamics in SST Variability across GCMs and Observations

Abstract Midlatitude SSTs forced by mesoscale oceanic processes can affect the large-scale atmosphere, pointing to the ocean’s crucial role outside the tropics. Previous studies have shown oceanic mesoscale processes’ effect on global and regional climate variability. This study quantifies the local contribution of ocean dynamics to mixed-layer temperature across the globe by directly estimating the ocean heat flux divergence resolved by state-of-the-art ocean reanalysis, eddy-resolving, and eddy-parameterized versions of two U.S. national climate models and indirectly from air–sea flux satellite-based estimates. Our results show that the eddy-resolving climate simulations resolve mixed-layer temperature variances that are larger and closer to those inferred from observations than both their eddy-parameterized counterparts and ECCO over much of the extratropics. The observations and the eddy-resolving models indicate a more significant role of ocean dynamics in the mixed-layer temperature variability than the surface fluxes over most extratropics compared to their eddy-parameterized versions. A frequency domain analysis shows that the better-resolved ocean mesoscale and thermal gradients enhance the variance over a time scale from 2 months to 30 years. Results show agreement in the ocean’s contribution among satellite-based estimates, ocean reanalysis products, and ocean eddy-resolving simulations. At the same time, differences emerge for ECCO and the eddy-parameterized models, suggesting that surface fluxes account for a larger fraction of the mixed-layer temperature variability in most of the extratropics.

Integrated optimization of ply number, layer thickness, and fiber angle for variable-stiffness composites using dynamic multi-fidelity surrogate model

To fully exploit the efficiency of variable-stiffness composite laminates with spatially varied fiber orientation angles, this paper aims at presenting a novel optimization framework for integrated design of ply number, layer thickness, and fiber angle. The optimization problem is innovatively formulated based on the definition of a ground laminate with redundant layers. The basic optimization idea is to seek both unnecessary and necessary layers in this ground laminate. For unnecessary layers, they can be removed and assigned with small-value ply thicknesses, while necessary layers are retained in the ground laminate and corresponding ply thicknesses and fiber angles are optimally determined using discrete and continuous variables, respectively. Since variable-stiffness composite laminates always require high-fidelity analysis models to accurately capture the spatial characteristics of varying fibers, this results in a time-consuming process. To alleviate this problem, a multi-fidelity surrogate model with an exponent-based comprehensive correction is originally proposed based on Gaussian process regression, generating an approximate problem to replace the original one. The genetic algorithm and sequential quadratic programming method are sequentially employed to solve this approximate problem with mixed design variables. The solution from this procedure is dynamically added to the sampling dataset to update the constructed surrogate model. Numerical benchmark problems and cases studies of a composite plate and a solar wing structure are addressed, demonstrating the efficacy of the newly proposed optimization strategy.

Model for Predicting Maize Crop Yield on Small Farms Using Clusterwise Linear Regression and GRASP

Planting a crop involves several key steps: resource assessment, crop selection, crop rotation, planting schedules, soil preparation, planting, care, and harvesting of crops. In this context, estimating the productivity of a crop based on available information, such as expected climatic conditions and agricultural practices, helps farmers reduce the uncertainty of their investment. In Colombia, maize is the fourth most important crop in the country. Significant efforts are required to improve productivity in traditional and technified production systems. In this sense, this research proposes and evaluates an approach called Clusterwise Linear Regression (CLR) to predict the crop maize yield in small farms, considering data on climate, soil, fertilization, and management practices, among others. To develop the CLR model, we conducted the following steps: data collection and preparation, clustering using k-means, cluster optimization with Greedy Random Adaptive Search Procedure (GRASP), and performance evaluation. The cluster optimization process allows the identification of clusters with similar characteristics and generates multiple linear regression models with mixed variables that explain the yield of the farms on each cluster. The Simulated Multiple Start Annealing (MSSA) metaheuristics were also evaluated, but the results of GRASP were the best. The results indicate that the proposed CLR approach is more effective than the linear and nonlinear algorithms mentioned in the literature, such as multiple lasso linear regression, random forests, XGBoost, and support vector machines. These algorithms achieved an accuracy of 70%. However, with the new CLR model, a significantly improved accuracy of 87% was achieved with test data. The clusters’ studies revealed key factors affecting crop yield, such as fertilization, drainage, and soil type. This transparency is a benefit over black-box models, which can be harder to interpret. This advancement can allow farmers to make better decisions about the management of their crops.

Mixed-variable graphical modeling framework towards risk prediction of hospital-acquired pressure injury in spinal cord injury individuals

Developing machine learning (ML) methods for healthcare predictive modeling requires absolute explainability and transparency to build trust and accountability. Graphical models (GM) are key tools for this but face challenges like small sample sizes, mixed variables, and latent confounders. This paper presents a novel learning framework addressing these challenges by integrating latent variables using fast causal inference (FCI), accommodating mixed variables with predictive permutation conditional independence tests (PPCIT), and employing a systematic graphical embedding approach leveraging expert knowledge. This method ensures a transparent model structure and an explainable feature selection and modeling approach, achieving competitive prediction performance. For real-world validation, data of hospital-acquired pressure injuries (HAPI) among individuals with spinal cord injury (SCI) were used, where the approach achieved a balanced accuracy of 0.941 and an AUC of 0.983, outperforming most benchmarks. The PPCIT method also demonstrated superior accuracy and scalability over other benchmarks in causal discovery validation on synthetic datasets that closely resemble our real dataset. This holistic framework effectively addresses the challenges of mixed variables and explainable predictive modeling for disease onset, which is crucial for enabling transparency and interpretability in ML-based healthcare.

Two-layer surrogate-assisted collaborative framework for expensive constrained optimization problems involving mixed integer variables

Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown exceptional abilities in solving expensive constrained optimization problems (ECOPs). However, few of them are designed for ECOPs involving mixed integer variables (ECOPs-MI). Therefore, a two-layer surrogate-assisted collaborative framework called TLSACF is designed, in which the radial basis function (RBF) and Gaussian Process (GP)-based mixed-integer collaborative frameworks are combined effectively. In the RBF-based collaborative framework, the combination between RBF-assisted prescreening and RBF-based local search achieves effective population update by balancing the exploration and exploitation on high potential parental information. For the GP-based collaborative framework, a variable type-based cooperative mutation is utilized to obtain potential candidate individuals by assigning targeted mutation for different variables, and a hypervolume-based complete expected improvement matrix function is derived to prescreen these candidate solutions from the perspective of balancing the optimization on constraints and the objective for the effective search on disconnected sub-feasible regions caused by integer variables. Experimental results on ten classical problems show that TLSACF performs excellent in solving ECOPs-MI.

Improving Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model

AbstractDeficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDL's 0.25° nominal horizontal grid spacing OM4 (uncoupled) configuration of the MOM6 ocean model, with various modifications that target biases in the original model. We test several enhancements to the existing mixing schemes and evaluate them against observational constraints from Tropical Atmosphere Ocean moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its surface boundary layer mixing scheme, and can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing, leading to improved diurnal deep‐cycle variability, a more realistic time‐mean tropical thermocline structure, and a better Pacific Equatorial Undercurrent.

An integrated optimization method for the damper placements, shape and size of truss structures

This work extends the application of the approximation concept in dynamic topology optimization for truss structures. The corresponding problem contains both continuous and discrete variables, encompassing damper placements, truss shape and cross-sectional area. First, the utilization of a branched multipoint approximation (BMA) function establishes sequential approximation problems that also involve such mixed variables, marking its innovative application in dynamic optimization. Then, a modified genetic algorithm (GA) is used to optimize discrete variables. To enhance convergence stationarity, the concept of ‘adjacent individuals’ is proposed to control the degree of difference in topology configuration between initial population members and the current optimal. Additionally, adjustments to the sequence-based crossover process ensure compliance with damper quantity constraints. Examples demonstrate that the introduction of adjacent individuals is beneficial to convergence stationarity, and the shape change of the truss increases the mode damping ratio by more than 20%. The created optimization platform can also tackle other mixed variables optimization problems.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Clinical phenotypes of heart failure patients with supranormal ejection fraction.

Heart failure (HF) with supranormal ejection fraction (HFsnEF) represents a distinct clinical entity characterized by limited treatment options and an unfavourable prognosis. Revealing its phenotypic diversity is crucial for understanding disease mechanism and optimizing patient management. We aim to identify phenotypic subgroups in HFsnEF using unsupervised clustering analysis. Consecutive hospitalized patients with a diagnosis of HF and a left ventricular ejection fraction ≥65% at baseline echocardiographic evaluations were included for analysis. We conducted unsupervised hierarchical clustering analysis on principal components (HCPC) to identify HFsnEF phenogroups using mixed data variables including demographics, HF duration, vital signs, anthropometrics, smoking/drinking status, HF aetiology, comorbid diseases, laboratory tests and echocardiographic parameters. We then employed decision tree modelling to identify parameters capable of distinguishing distinct clusters. Clinical outcomes, including all-cause death, cardiovascular (CV) death and CV readmission for different clusters, were examined. Three mutually exclusive clusters were identified from the cohort of 221 HFsnEF patients. Cluster 1 (52.5%) predominantly consisted of patients with valvular heart disease, who had larger cardiac chambers and a higher prevalence of atrial fibrillation/atrial flutter. Cluster 2 (26.2%) primarily comprised older ischaemic patients with a higher prevalence of metabolic comorbidities. Cluster 3 (21.3%) were mainly hypertrophic cardiomyopathy patients. Two clinical variables were identified that could be used to group all HFsnEF patients into one of the clusters; they were HF aetiology and comorbid diabetes. During the median follow-up of 53.4months, 46 (20.8%) all-cause deaths occurred, among them 39 of CV causes. Seventy (31.7%) patients experienced CV readmissions. Three clusters showed distinct differences in mortality outcomes, with Cluster 1 exhibiting the highest risk of all-cause mortality [Cluster 1 vs. Cluster 2: adjusted hazard ratio (aHR)=3.32, P=0.022; Cluster 1 vs. Cluster 3: aHR=3.81, P=0.036; Cluster 2 vs. Cluster 3: aHR=1.15, P=0.865] and CV mortality (Cluster 1 vs. Cluster 2: aHR=3.73, P=0.022; Cluster 1 vs. Cluster 3: aHR=4.27, P=0.020; Cluster 2 vs. Cluster 3: aHR=1.15, P=0.870). CV readmission risk was comparable among the three clusters (Cluster 1 vs. Cluster 2: aHR=0.82, P=0.590; Cluster 1 vs. Cluster 3: aHR=1.04, P=0.900; Cluster 2 vs. Cluster 3: aHR=1.28, P=0.580). In a heterogeneous HFsnEF cohort, three clusters were identified by unsupervised HCPC with distinct clinical characteristics and outcomes.

Wind-Sea and Swell Climate in the Black and Azov Seas, Based on 42-Year Spectral Wave Hindcast

Long-term wave climates of the Black and Azov Seas are widely assessed based on the bulk wave parameters of the mixed sea. However, studies based on spectral wave partitions are rarely documented. Assessment of the wave climate based on the individual wave systems provides a more reliable description of the sea states' characteristics and the variability in the long-term wave climate. For instance, the wind-sea climate may reflect a real change in the local climate. However, local change in swell characteristics may not necessarily reflect a change in the local climate, and it can locally influence the mixed sea climate variability. This study assessed the spatial distributions of the long-term averages and maxima of mixed seas, wind-sea, and swell partitions at annual and seasonal scales. The contributions of wind-sea and swell partitions to the mixed-sea were computed, and their spatial and temporal variability was also assessed for overall and extreme sea state conditions. Finally, the occurrence in main wave systems' directions and occurrence of crossing, opposing, and following wind-sea and swell interactions were computed. The wind-sea contribution to the mixed-sea exceeds 70 % in all Black Sea and >95 % in the Azov Sea. The swells are more dominant on the Northeastern and Northwestern Black Sea basins. Thus, the results show spatial and temporal variability in the wind-sea climate close to the mixed sea but largely different compared to the swell climate. The inter-annual trends in the swell partitions and wind-sea are inversed at some locations.

A multi-ship power sharing strategy: Using two-stage robust optimization and Shapley value approach

In recent years, the use of cold ironing (CI) has effectively reduced the carbon emissions of ships in port areas. However, due to the limitation of port CI quantity and power supply capacity, the power demand of some ships cannot be met. To solve this problem, this paper proposes a multi-ship power sharing strategy based on two-stage robust optimization and improved Shapley value method. Firstly, considering the uncertainty of ship photovoltaic power generation, a multi-ship power sharing model based on two-stage robust optimization is established. This model is a three-level optimization model with mixed integer variables, which is a NP-hard problem. In this paper, the nested column-and-constraint generation algorithm combined with duality theory and big-M method is used to obtain the optimal scheduling strategy of ship shared power, CI power and load demand response power. Secondly, an improved Shapley value method considering berth and remaining berthing time is proposed to realize the cost allocation in the process of ship power sharing. Finally, the effectiveness of the multi-ship power sharing strategy in reducing berthing costs and carbon emissions is verified by simulation. This method is expected to provide a new idea for the development of green ports.

Stabilized time‐series moving morphable components method for topology optimization

AbstractThe moving morphable components (MMC) method has been widely used for topology optimization due to its ability to provide an explicit description of topology. However, the MMC method may encounter the instability issue during iteration. Specifically, the iteration history is highly sensitive to parameters of the optimizer, that is, the move limits in the method of moving asymptotes (MMA). Additionally, the final topology obtained from the MMC method usually depends on the initial values. To address these issues and improve the stability of the MMC method in practical applications, this article introduces two strategies. The first strategy is based on the time‐series MMC (TSMMC) method, which proposes a unified description of curved components. However, the use of control‐points‐based design variables may introduce instability into the iteration process due to the strong locality associated with these variables. To mitigate this, global design variables have been incorporated into the formulation. Numerical examples demonstrate that this mixed formulation, combining global and local design variables, can enhance stability significantly. To further enhance stability, the second strategy involves using the trust region‐based moving asymptotes (TRMA) method as the optimizer instead of MMA. The TRMA method incorporates an accuracy control mechanism, resulting in stable and fast convergence behavior, as demonstrated in the numerical examples.

Drivers of seasonal to decadal mixed layer carbon cycle variability in subantarctic water in the Munida Time Series

Drivers of seasonal to decadal mixed layer carbon cycle variability in subantarctic water in the Munida Time Series

Spatial and Temporal Variability of Turbulent Mixing in the Deep Northwestern Pacific

AbstractSmall‐scale turbulent mixing supplies potential energy for the upwelling of deep waters in the abyssal ocean, a key component of the global overturning circulation. This process is particularly significant in critical regions such as the Northwestern Pacific where the upwelling structure of deep waters remains poorly understood due to limited knowledge of deep ocean mixing. Here, we investigate the full‐depth spatiotemporal variability of turbulent mixing in the deep Northwestern Pacific based on hydrographic data collected over repeated surveys. Nineteen‐year‐average diapycnal diffusivity of 1.42 × 10−4 m2 s−1 is reveled in the deep Philippine Sea, indicating significantly stronger mixing compared to the stratified ocean interior. Spatially, turbulent mixing strengthens toward the bottom and intensifies westward from the open Pacific to the Philippine Sea due to rough topography. At certain mixing hotspots, enhanced mixing can penetrate up to 2,500 m above the bottom, suggesting a substantial potential for upwelling. Below 2,000 m, turbulent mixing exhibits pronounced seasonal variation that deep mixing is more intense in summer (winter) than in winter (summer) in the West Caroline Basin (the Parece Vela Basin). This spatially varying seasonality may be attributed to the inhomogeneous internal tidal energy dissipation in the Northwestern Pacific. Our study will serve to clarify the modulation of turbulent mixing to deep‐water mass transformation and circulation in the Northwestern Pacific.

A two-level game theoretic approach for task offloading in mobile edge computing

In the mobile edge computing system subject to wireless interference, the Edge Server Provider (ESP) aims to offer profitable computing resources to Device Managers (DMs), who make optimal strategies based on the provided prices. However, the existence of mixed variables typically constitutes an NP-hard problem, posing a significant challenge for optimization. In response to address this issue, we formulate a bi-level optimization problem, where the upper level is devoted to optimizing the pricing of computing resources. The lower level optimizes DM migration strategies and resource allocation at specified prices. Leveraging game theory, we achieve distributed and efficient computation offloading by formulating the distributed computing offloading strategy among lower-level DMs as a task offloading game. Our analysis identifies Nash equilibrium and finite improvement characteristics within the game. Based on these insights, we present a bi-level distributed computation offloading algorithm capable of reaching Nash equilibrium, thus optimizing the profit for both DMs and ESP. The experiments have demonstrated the algorithm’s effectiveness in reducing system costs and maximizing the profits of ESP with DMs across diverse scenarios.

Northbound Transport of the Mediterranean Outflow and the Role of Time‐Dependent Chaotic Advection

AbstractThe Mediterranean Sea releases approximately 1 Sv of water into the North Atlantic through the Gibraltar Straits, forming the saline Mediterranean Outflow Water (MOW). Its impact on large‐scale flow and specifically its northbound Lagrangian pathways are widely debated, yet a comprehensive overview of MOW pathways over recent decades is lacking. We calculate and analyze synthetic Lagrangian trajectories in 1980–2020 reanalysis velocity data. Sixteen percent of the MOW follow a direct northbound path to the sub‐polar gyre, reaching a 1,000 m depth crossing window at the southern tip of Rockall Ridge in about 10 years. Surprisingly, time‐dependent chaotic advection, not steady currents, drives over half of the northbound transport. Our results suggest a potential 15–20 years predictability in the direct northbound transport. Additionally, monthly variability appears more significant than inter‐annual variability in Lagrangian mixing and spreading the MOW.

Assessing copula models for mixed continuous-ordinal variables

Abstract Vine pair-copula constructions exist for a mix of continuous and ordinal variables. In some steps, this can involve estimating a bivariate copula for a pair of mixed continuous-ordinal variables. To assess the adequacy of copula fits for such a pair, diagnostic and visualization methods based on normal score plots and conditional Q–Q plots are proposed. The former uses a latent continuous variable for the ordinal variable. The methods are applied to data generated from some existing probability models for a mixed continuous-ordinal variable pair, and for such models, Kullback-Leibler divergence is used to assess whether simple parametric copula families can provide adequate fits. The effectiveness of the proposed visualization and diagnostic methods is illustrated on a dataset.

Rapid Restratification Processes Control Mixed Layer Turbulence and Phytoplankton Growth in a Deep Convection Region

AbstractThe Gulf of Lion, Northwestern Mediterranean Sea, is one of few oceanic regions where deep convection occurs. We investigate the restratification following a convection event using measurements from an ocean glider equipped with turbulence microstructure sensors. This unique combination of instruments provides a high‐resolution description of the mixed layer with regard to turbulence, stratification and chlorophyll. We observe a rapid restratification process that proceeds over a timescale of days to one week. We find that restratification exerts a leading order control on surface mixed layer turbulence variability, as abrupt changes in turbulence dissipation rates are associated with the formation of near‐surface stratification. The near‐surface formation of stratification occurs through both the diurnal variability in surface buoyancy fluxes and through lateral advective processes. We conclude that daily near‐surface processes that influence stratification control mixed layer turbulence levels, and thus the phytoplankton response in the critical transition period to spring bloom.

A numerical method for two-dimensional transient nonlinear convection-diffusion equations

In this research, we have developed the half-boundary method (HBM) for nonlinear convection-diffusion equations (CDEs), which hold significant importance in nuclear power engineering. The HBM employs a variable relationship between the nodes within the computational domain and the nodes located on half of the boundaries. This approach offers notable benefits, includingthe reduction ofthe maximum matrix order and the optimization the maximum memory storage for calculations. Moreover, the HBM is an efficient and streamlined approach to directly handle Neumann boundary conditions, thanks to the utilization of mixed variables. We primarily investigate the memory usage and accuracy of the proposed algorithm in the unsteady-state CDEs, in context of material nonlinear CDEs, the Burgers’ equation and the system of Burgers' equations. The numerical results obtained demonstrate the method’s potential in simulating flow and heat transfer phenomena, particularly in situations where convection is dominant.