Modeling Scheme Research Articles

Characterizing the marine iodine cycle and its relationship to ocean deoxygenation in an Earth system model

Abstract. Iodine (I) abundance in marine carbonates (measured as an elemental ratio with calcium, I / Ca) is of broad interest as a proxy for local/regional ocean redox. This connection arises because the speciation of iodine in seawater, the balance between iodate (IO3-) and iodide (I−), is sensitive to the prevalence of oxic vs. anoxic conditions. However, although I / Ca ratios are increasingly commonly being measured in ancient carbonate samples, a fully quantitative interpretation of this proxy requires the availability of a mechanistic interpretative framework for the marine iodine cycle that can account for the extent and intensity of ocean deoxygenation in the past. Here we present and evaluate a representation of marine iodine cycling embedded in an Earth system model (“cGENIE”) against both modern and paleo-observations. In this framework, we account for IO3- uptake and release of I− through the biological pump, the reduction in ambient IO3- to I− in the water column, and the re-oxidation of I− to IO3-. We develop and test a variety of different plausible mechanisms for iodine reduction and oxidation transformation and contrast model projections against an updated compilation of observed dissolved IO3- and I− concentrations in the present-day ocean. By optimizing the parameters controlling previously proposed mechanisms involved in marine iodine cycling, we find that we can obtain broad matches to observed iodine speciation gradients in zonal surface distribution, depth profiles, and oxygen-deficient zones (ODZs). However, we also identify alternative, equally well performing mechanisms which assume a more explicit mechanistic link between iodine transformation and environment – an ambiguity that highlights the need for more process-based studies on modern marine iodine cycling. Finally, to help distinguish between competing representations of the marine iodine cycle and because our ultimate motivation is to further our ability to reconstruct ocean oxygenation in the geological past, we conducted “plausibility tests” of different model schemes against available I / Ca measurements made on Cretaceous carbonates – a time of substantially depleted ocean oxygen availability compared to modern and hence a strong test of our model. Overall, the simultaneous broad match we can achieve between modeled iodine speciation and modern observations, and between forward proxy modeled I / Ca and geological elemental ratios, supports the application of our Earth system modeling in simulating the marine iodine cycle to help interpret and constrain the redox evolution of past oceans.

Evaluation of sea surface temperature from ocean reanalysis products over the North Indian Ocean

Ocean and sea ice reanalyses (ORAs or ocean syntheses) are reconstructions of the ocean and sea ice states using an ocean model integration constrained by atmospheric surface forcing and ocean observations via a data assimilation method. Ocean reanalyses are a valuable tool for monitoring and understanding long-term ocean variability at depth, mainly because this part of the ocean is still largely unobserved. Sea surface temperature (SST) is the key variable that drives the air–sea interaction process on different time scales. Despite improvements in model and reanalysis schemes, ocean reanalyses show errors when evaluated with independent observations. The independent evaluation studies of SST from ocean reanalysis over the Indian Ocean are limited. In this study, we evaluated the SST from 10 reanalysis products (ECCO, BRAN, SODA, NCEP-GODAS, GODAS-MOM4p1, ORAS5, CGLORS, GLORYS2V4, GLOSEA, and GREP) and five synthetic observation products (COBE, ERSST, OISST, OSTIA, and HadISST) and from the pure observation-based product AMSR2 for 2012–2017 with 12 in-situ buoy observations (OMNI) over the Arabian Sea and Bay of Bengal. Even though the reanalysis and observational products perform very well in the open ocean, the performance is poorer near the coast and islands. The reanalysis products perform comparatively better than most of the observational products. COBE and OISST perform better among the synthetic observational products in the northern Indian Ocean. GODAS-MOM4p1 and GREP performs best among the reanalysis products, often surpassing the observational products. ECCO shows poorer performance and higher bias in the Bay of Bengal. Comparing the BRAN daily and monthly SST, the monthly SST performance of reanalysis is better than the daily time scale.

Efficient Cross-Domain Fault Diagnosis via Distributed Multi-Source Domain Deep Transfer Learning

Abstract In the actual industrial production, the working conditions of rotating machinery are complex and changeable, and the health-state monitoring data are increasingly large and difficult to be labeled, which will seriously restrict the accuracy and efficiency of cross-domain fault diagnosis (CDFD) of rotating machinery. Therefore, an efficient multi-source domain deep transfer learning (MDDTL) method for CDFD of rotating machinery is proposed. Firstly, a MDDTL model is constructed to improve the accuracy of CDFD. In the model, a dual-phase domain alignment strategy is designed, which considers the alignment of feature distributions between each source and target domain pair in the feature space and that of prediction probabilities between domain-specific fault classifiers in the output space. The fault prediction results from multiple different fault classifiers are merged dynamically by the proposed imbalanced adaptive prediction strategy. Secondly, a data-parallel distributed training scheme for MDDTL model is proposed. Based on the idea of data parallelism, the distributed parallel training of MDDTL model is performed with Horovod-GPU platform, and the parameters are synchronously updated with the bandwidth-optimal Ring-AllReduce architecture. Under the premise of ensuring the accuracy of fault diagnosis (FD), the training time of MDDTL model is significantly reduced. Finally, extensive experiments are conducted to verify the effectiveness of the proposed MDDTL method. The results demonstrate that the proposed method not only effectively improves the accuracy of CDFD of rotating machinery, but also significantly improves the training efficiency of MDDTL model. After adopting the proposed method, the diagnosis accuracies achieved under two different cross-working condition scenarios reach 97.09% and 97.87% respectively, and the model training time is reduced by 73.62% when facing a large-scale rotating machinery training set.

Tiling Robotics: A New Paradigm of Shape‐Morphing Reconfigurable Robots

The ability of reconfigurable robots to adapt to varying tasks and environments furnishes versatility and efficiency in their operations. In this article, tiling robots are introduced as a novel paradigm of shape‐morphing reconfigurable robots, defining them as polyform‐inspired machines capable of transforming between at least two polymorphic shapes. In the study, the existing and future designs of tiling robots by varying base shapes and polygon selections are explored, identifying a significant gap for further exploration of polyforms in their design. The various reconfiguration‐enabling mechanisms and locomotion mechanisms of tiling robots are comparatively analyzed. Summarized electromechanical developments, along with a proposed generalized kinematic model and control scheme, contribute to a comprehensive understanding of tiling robots. A comparison of tiling robots with other established reconfigurable robots is conducted to position tiling robotics within the broader landscape of reconfigurable robotics. The introduction of a new naming convention addresses the absence of a standardized nomenclature for tiling robots. In this article, highlighting the current focus on area coverage in autonomy algorithms of tiling robots, future developments in diverse application domains like logistics, entertainment, and education are anticipated, emphasizing the adaptability of tiling robots as a critical feature for their proliferation across various domains.

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

One-way reflection waveform inversion with depth-dependent gradient preconditioning

Summary Reflection Waveform Inversion (RWI) is a technique that uses pure reflection data to estimate subsurface background velocity, relying on evolving seismic images. Conventional RWI operates in a cyclic workflow, with two key components in each cycle—migration and reflection tomography. Conventional RWI may result in suboptimal background velocity estimation, partly due to limited or unresolved resolution within each component in each cycle. While gradient preconditioning with the reciprocal of Hessian information helps resolve this issue in both components of RWI, it becomes impractical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform inversion technique in which the forward modeling scheme operates in one direction (downward and then upward) via virtual parallel depth levels within the medium. Leveraging the ORWI framework, we decompose and reduce the linear Hessian operator (also known as the approximate Hessian or Gauss-Newton Hessian) into multiple smaller sub-operators. In particular, the diagonal blocks of the mono-frequency approximate Hessian operators, each corresponding to a single depth level within the medium, are extracted and inverted to precondition the corresponding mono-frequency gradients in both the migration and reflection tomography components of ORWI. This depth-dependent gradient preconditioning transforms standard ORWI into a high-resolution, yet computationally feasible version aimed at addressing suboptimal velocity estimation, referred to as high-resolution ORWI (HR-ORWI). The effectiveness of the proposed approach is demonstrated through successful applications to synthetic data examples.

Type 1 Diabetes in care homes: A practical guide on management.

The primary purpose of the original NAPCHD project was to develop a national Strategic Document of Diabetes Care for Care Homes which has now been completed and well received as a worthwhile, sustainable, and effective guidance for delivering quality diabetes care in the UK. A Working Group of NAPCHD was established to produce a Position Statement on type 1 diabetes in care homes since this area was recommended as a topic to further develop. There are currently limited data on the prevalence and clinical outcomes associated with type 1 diabetes in care homes and management policies have been non-existent in the UK. Communication among all key stakeholders involved in direct care of residents with type 1 diabetes is generally fragmented and lacks coordination. This is compounded by a slowly growing utilisation of diabetes technology and the absence of a standard/agreed community-based model of interdisciplinary collaboration. The Rationale and Objectives were defined prior to commencing the work and a work plan with individual tasks was initially set out. After multiple correspondences and Team calls over a period of 9 months, the Group successfully generated a first draft in October 2023. This draft was then finalised the following month and circulated among stakeholders for feedback. Nine chapters have been provided including minimum standards of diabetes care, insulin regimens, avoiding hospitalisation and discharge planning. A scheme for a community-based model of care for type 1 diabetes has been included. Eight key messages were developed. In addition, an Appendix has been created which includes key assessments such as nutritional assessment, detection of frailty, sick day rules and foot risk stratification (available online).

A Relaxed Decoupled Second-Order Energy-Stable Scheme for the Binary Phase-Field Crystal Model

A Relaxed Decoupled Second-Order Energy-Stable Scheme for the Binary Phase-Field Crystal Model

Decoupled and energy stable schemes for phase-field surfactant model based on mobility operator splitting technique

Decoupled and energy stable schemes for phase-field surfactant model based on mobility operator splitting technique

A Second Order Accurate in Time, Energy Stable Finite Element Scheme for a Liquid Thin Film Coarsening Model

A Second Order Accurate in Time, Energy Stable Finite Element Scheme for a Liquid Thin Film Coarsening Model

The asymptotic preserving unified gas kinetic scheme for the multi-scale kinetic SIR epidemic model

In this paper, we present an asymptotic preserving scheme for the two-dimensional space-dependent and multi-scale kinetic SIR epidemic model which is widely used to model the spread of infectious diseases in populations. The scheme combines a discrete ordinate method for the velocity variables and finite volume method for the spatial and time variables. The idea of unified gas kinetic scheme (UGKS) is used to construct the numerical boundary fluxes which needs the formal integral solutions of the model. Due to the coupling of the three transfer equations in the SIR model, it is difficult to obtain these integral solutions dependently. We decouple the system by constructing the fluxes in a separate way. Then following the framework of UGKS we can obtain the macro auxiliary quantities which is needed in the scheme. Thus the SIR model can be solved in the sequential way. In addition, we can show numerically that the scheme is second-order accurate both in space and time. Moreover, it can not only capture the solution of the diffusion limit equations without requiring the cell size and time step being related to the smallness of the scaling parameters, but also resolve the solution in hyperbolic regime in a natural way. Furthermore, the positive property of the UGKS is analyzed in detail, and through adding time step constraint conditions and applying nodal limiters together, the positive UGKS, called PPUGKS2−order, is obtained. Moreover, in order release the time step constraints in the diffusion regime, a temporal first-order accuracy positive preserving UGKS, called PPUGKS1−order, is proposed. Finally, several numerical tests are included to validate the performance of the proposed schemes.

Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.

The Sharing Energy Storage Mechanism for Demand Side Energy Communities

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side.

Predicting hardwood porosity domains: Toward cascading computer-vision wood identification models

Prior work on computer-vision wood identification (CVWID) for North American hardwoods yielded two independent deep learning models – a 22-class model for diffuse-porous woods and a 17-class model for ring-porous woods – but did not address semi-ring-porous woods nor provide a CVWID solution for an unknown specimen without a human first determining which model to deploy. As untrained human operators would lack the anatomical proficiency to differentiate among porosity domains, it is necessary to develop a consolidated model that can identify diffuse-, ring-, and semi-ring-porous woods. Previous research suggests that prediction accuracy might decrease as class number grows. A potential strategy to reduce the number of classes a CVWID system must consider at a time is to hierarchically deploy a cascade of models. In pursuit of a unified model that can cover North American hardwoods of all porosity types, this study compared the accuracies of a consolidated 39-class (ring- + diffuse-porous) model and a consolidated 42-class (ring- + diffuse- + semi-ring-porous) model with a two-tiered, cascading model scheme whereby images are first differentiated into three porosity domain classes and then again into only those taxonomic classes with that porosity. The results showed that the cascading model scheme can mitigate the accuracy reductions incurred by the 42-class model and nearly eliminate the occurrence of cross-domain misidentifications.

Evaluation of 10‐m Wind Speed From ISD Meteorological Stations and the MERRA‐2 Reanalysis: Impacts on Dust Emission in the Arabian Peninsula

AbstractMineral dust is one of the most important aerosols when studying the radiative balance and climate of the planet. There are different dust emission schemes utilized by the atmospheric modeling communities, many of which disagree on basic output quantities such as mass of dust emitted and distribution of mass among size bins. In this work, we examined mineral dust emission from a leading model scheme, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), as utilized in the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) Reanalysis and compared it to dust emissions calculated using wind measurements from ground based weather stations located in the Arabian Peninsula that are included in the National Oceanic and Atmospheric Administration’s (NOAA) integrated surface database (ISD). An intercomparison of 10‐m wind speed is shown for the Arabian Peninsula region, differences of the observed and modeled wind field are quantified, and impacts of differences on dust emissions are calculated. This analysis shows 10‐m winds in the ISD were generally lower than MERRA‐2 winds, which propagated to dust emissions errors. Our estimate of one of the most significant mass impacts in dust emission is 0.178 Tg/year/grid box with a percent change of over 200% to the recalculated dust emissions from MERRA‐2. These differences in wind speed propagated to a difference in dust mass emitted by the use of a static source function which aids in scaling the mass emitted by the availability of dust in each grid. Additionally, the magnitude of these differences varies seasonally.

Probability Updating in the Classical and the Quantum Model

Imagine a Bayesian decision agent who is keen to invest only in technologies or businesses that are conductive to achieving a sustainable economic future. Being initially keen to invest in a certain technology, subsequently two pieces of new information are received that both individually reduce the agent’s inclination to make that investment. In such a situation, it would be natural to assume that the simultaneous consideration of both pieces of information should further reduce the agent’s initial enthusiasm; after all the Bayesian method has been called “ nothing but common sense reduced to calculation.” Somewhat surprisingly, making general statements like this about the double conditional probability requires substantial additional assumptions in classical Bayesian probability theory. We investigate four schemes of assumptions that allow ‘reasonable’ conclusions about the double conditional probability. We compare this with two schemes for the quantum probability model where the double conditional results from sequential updating through projections.

RKFD Scheme for Quantum Reflection Model of Bose-Einstein Condensates (BEC) from Silicon Surface

We applied the numerical combination of Runge-Kutta and Finite Difference (RKFD) scheme for a quantum reflection model of Bose-Einstein condensate (BEC) from a silicon surface. It is by the time-dependent Gross-Pitaevskii equation (GPE), a non-linear Schrödinger equation (NLSE) in the context of quantum mechanics. The role of cut-off potential δ and negative imaginary potential Vim is essential to estimating non-interacting BEC reflection models. Relying on these features, we performed a numerical simulation of the BEC quantum reflection model and calculated the effect of reflection probability R versus incident speed vx. The model is based on the three rapid potential variations: positive-step potential +Vstep, negative-step potential -Vstep, and Casimir-Polder potential VCP. As a result, the RKFP numerical scheme was successfully set up and applied to the quantum reflection model of BEC from the silicon surface. The numerical simulations results show that the reflection probability R decays exponentially to the incident speed vx.

Impact of Directly Assimilating Radar Reflectivity Using a Reflectivity Operator Based on a Double-Moment Microphysics Scheme on the Analysis and Forecast of Typhoon Lekima (1909)

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent.

Comparative Study on Artificial Fracture Modeling Schemes in Tight Reservoirs—For Enhancing the Production Efficiency of Tight Oil and Gas

In order to improve the reliability of the deployment of production schemes after artificial fracturing in tight reservoirs, it is urgent to carry out research on the description of fractures after artificial fracturing. In this study, taking the Chang 61 oil formation group in the Wangyao South area of Ordos Basin as an example, three different fracture modeling schemes are used to establish the geological model of fractured reservoirs, and the fitting ratios of the respective reservoir models are calculated by using the method of reservoir numerical simulation of the initial fitting, and the optimal fractured reservoir modeling scheme is screened in the end. The research area adopts three types of fracture prediction results based on FMI fracture interpretation data, seismic fracture prediction data, and rock mechanics artificial fracturing simulation data. On this basis, geological models of fractured reservoirs are established, respectively. The initial fitting of reservoir values of each geological model are compared, and the highest initial fitting rate of reservoir values is 88.44%, which is based on rock mechanics artificial fracturing simulation data. However, the initial fitting rate of the reservoir model was the lowest at 75.76%, which was established based on the fracture random modeling results of FMl fracture interpretation data. Under the constraints of seismic geostress prediction results and microseismic monitoring data, the simulation results of rock mechanics artificial fracturing fracture are used as the basis, on which the geological model of artificially fractured reservoirs is thus established, and this scheme can more realistically characterize the characteristics of fractured reservoirs after artificial fracturing in the study area.

Paving the Way for Sustainable UAVs Using Distributed Propulsion and Solar-Powered Systems

Hybrid systems offer optimal solutions for unmanned aerial platforms, showcasing their technological development in parallel and series configurations and providing alternatives for future aircraft concepts. However, the limited energetic benefit of these configurations is primarily due to their weight, constituting one of the main constraints. Solar PV technology can provide an interesting enhancement to the autonomy of these systems. However, to create efficient propulsion architectures tailored for specific missions, a flexible framework is required. This work presents a methodology to assess hybrid solar-powered UAVs in distributed propulsion configurations through a two-level modeling scheme. The first stage consists of determining operational and design constraints through parametric models that estimate the baseline energetic requirements of flight. The second phase executes a nonlinear optimization algorithm tuned to find optimal propulsion configurations in terms of the degree of hybridization, number of propellers, different wing loadings, and the setup of electric distributed propulsion (eDP) considering fuel consumption as a key metric. The results of the study indicate that solar-hybrid configurations can theoretically achieve fuel savings of up to 80% compared to conventional configurations. This leads to a significant reduction in emissions during long-endurance flights where current battery technology is not yet capable of providing sustained flight.