Individual Model Components Research Articles

WS-BiTM: Integrating White Shark Optimization with Bi-LSTM for enhanced autism spectrum disorder diagnosis

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition marked by challenges in social communication, sensory processing, and behavioral regulation. The delayed diagnosis of ASD significantly impedes timely interventions, which can exacerbate symptom severity. With approximately 62 million individuals affected worldwide, the demand for efficient diagnostic tools is critical. This study introduces a novel framework that combines a White Shark Optimization (WSO)-based feature selection method with a Bidirectional Long Short-Term Memory (Bi-LSTM) classifier for enhanced autism classification. Utilizing the WSO technique, we identify key features from autism screening datasets, which markedly improves the model's predictive capabilities. The optimized feature set is then processed by the Bi-LSTM classifier, enhancing its efficiency in handling sequential data. We comprehensively address methodological challenges, including overfitting, generalization, interpretability, and computational efficiency. Furthermore, we conduct a comparative analysis against baseline algorithms such as Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, while also employing Particle Swarm Optimization (PSO) for feature selection validation. We evaluate performance metrics, including accuracy, F1-score, specificity, precision, and sensitivity across three ASD datasets: Toddlers, Adults, and Children. Our results demonstrate that the WS-BiTM model significantly outperforms baseline methods, achieving accuracies of 97.6 %, 96.2 %, and 96.4 % on the respective datasets. Additionally, we implemented leave-one-dataset cross-validation and confirmed the statistical significance of our findings through a paired t-test, supplemented by an ablation study to detail the contributions of individual model components. These findings highlight the potential of the WS-BiTM model as a robust tool for ASD classification.

A gray-box model for unitary air conditioners developed with symbolic regression

In this paper, we present the development of a gray-box model for unitary air conditioning equipment that can be trained with as little as 5 data points with higher accuracy on test data. The model utilizes the same model inputs as is typical in building energy simulation, and is accurate. While black-box models require large data sets to deliver accurate results, white-box models require higher computational and engineering efforts along with detailed knowledge of the system, and are often difficult to obtain. The model presented here addresses a hybrid solution that is a steady-state, component-based, gray-box model that requires inputs from the source and sink fluids and rated performance of the specific piece of equipment, only. The basic physics of a vapor compression cycle are captured in individual component models for the heat exchangers, compressor, and expansion valve. These components are generalized to eliminate refrigerant-side inputs. A key addition is the development of correlations for the overall heat transfer coefficient times surface area (UA) obtained from Symbolic Regression (SR). The model successfully predicts the cooling capacity, coefficient of performance (COP), and sensible heat ratio (SHR) for three state-of-the-art variable speed, split-system, air conditioning systems with capacities of 12.3(3.5), 14(4), and 17.6(5) kW(tons), achieving a mean absolute percentage error (MAPE) of less than 3.4%. These results suggest that the gray-box model can be useful in predicting the performance of similar systems in the future, which could be valuable for energy management and optimization purposes.

A novel exponential unsaturated bistable stochastic resonance-boosted incipient fault identification in rotating machineries

Abstract Stochastic resonance (SR) for weak fault detection stands as a significant constructive methodology leveraging noise in nonlinear information systems processing. In virtue of the SR technique in conjunction with coupled non-saturated nonlinear systems, an exponential unsaturated bistable stochastic resonance (EUBSR) model is developed to enhance output levels. By integrating the exponential monostable stochastic resonance system (ESR) and the unsaturated bistable stochastic resonance (UBSR) system through coupling coefficients, this model offers a broader spectrum of resonance characteristics. The performance of the EUBSR is evaluated based on the relevant indicators signal-to-noise ratio (SNR) and residence time distribution ratio. These indicators are treated as multi-objective functions, with the coati optimization algorithm employed to optimize both the parameters and coupling coefficients of the EUBSR model simultaneously. Moreover, the paper takes into account the interdependence of nonlinear systems and their interactions by considering both cascade and parallel models of the ESR and UBSR systems. Fault diagnosis is carried out on simulation signals and bearings to validate the effectiveness of the proposed EUBSR model. The results demonstrate that the EUBSR model surpasses not only its individual component models but also cascade and parallel models.

Dynamic simulation of a space lithium-cooled reactor system coupled with a He–Xe closed Brayton cycle

The lithium-cooled reactor with Brayton conversion power system mainly consists of fast reactor, intermediate heat exchanger, closed Brayton cycle (CBC) loops filled with Helium–Xenon (He–Xe) binary gas mixtures and heat rejection loop which radiate residual heat through heat pipes to outer space. In this study, a dynamic model of 100-kWe lithium-cooled reactor with Brayton conversion power system was established based on RESYS code, which include reactor core thermal-hydraulic model, six-group neutron kinetics model and decay heat model, heat exchanger model, Closed Brayton cycle model including turbine, compressor and rotating shaft, heat pipe radiator model etc. The nominal steady-state simulation is carried out to verify the correctness and accuracy of individual component model the integrated system model. Four typical transient conditions including accidental reactivity insertion, loss of coolant flow of primary loop, partial and total external load loss and a step reduction in the mass flow rate of heat rejection loop are simulated to analysis the transient characteristics and prove the safety features for those simulated scenarios. The results are helpful for the design, evaluation and operation of space liquid-lithium-cooled reactor power system with He–Xe closed Brayton cycle.

Modeling cryptocurrency market dynamics using machine learning tools

The article analyzes the dynamics of the cryptocurrency market (Bitcoin) using econometric estimation tools based on machine learning models. The forecasting method is improved based on time series decomposition and lagged shifts of financial indicators. An ensemble of short-term forecast models for the Bitcoin exchange rate is built, and its accuracy is analyzed and compared to individual component models. Time series models are used along with calculated financial indicators (ADODS, NATR, TRANGE, ATR, OBV, RSI, ADTV). The absolute deviation of the short-term forecast amounted to $9.5, which is 0.06% of the absolute value.

Component method for bolted SHS end plate splice joints loaded in tension

Bolted end plate connections are traditional solutions for splice joints with square hollow sections (SHS). A cover plate could be used on one side or two adjacent sides of SHS where the end plate is flushed, resulting in two types of asymmetric splice joints. The advantage of the asymmetric joints is that no space is required between the façade panel and the member surface whilst the easy assembly feature remains. The component method is used to design the bolted end plate connection, as stipulated in prEN 1993–1–8. However, the design rule is only available for connections with open cross-sections. In this paper, a modified component method for predicting the tensile resistance (end plate fails with failure mode 1 according to prEN 1993–1–8) and stiffness of the asymmetric bolted SHS end plate splice joints is proposed. First, a parametric study is carried out to develop the component method for the traditional symmetric bolted SHS end plate splices based on the validated finite element (FE) model. Next, a simplified two-dimensional FE model is employed to evaluate the effect of the asymmetric feature on the stiffness of the joint. Finally, a parametric study is carried out on two types of asymmetric joints to validate the interaction model of individual components. The resistance and stiffness of the asymmetric end plate splice joints are well predicted using the modified component method.

A state detection method of offshore wind turbines’ gearbox bearing based on the transformer and GRU

Through the analysis of data from the SCADA system of a wind turbine unit in a specific offshore wind farm located in Zhanjiang, it was observed that the most prevalent type of fault is bearing alarms on the gearbox’s generator side. Considering the growing need for intelligent offshore wind turbine maintenance, this study employed GRA on SCADA data collected over a significant duration from a representative wind turbine unit. Relevant features were extracted, with temperature serving as the target parameter. To address the challenge of long-term dependencies in long-term time series forecasting tasks, this study uniquely combined the GRU with the advanced Transformer neural network which incorporates attention mechanisms, to predict the temperature trend of the gearbox’s generator-side bearing. Based on the prediction residuals obtained during normal operation and their subsequent analysis, the study devised an effective anomaly detection process to identify early abnormal states of the gearbox’s generator-side bearing. Comparative performance evaluations were conducted, comparing the combined model with its individual component models, as well as the traditional LightGBM, in terms of temperature time series prediction and their application in anomaly detection. The results unequivocally demonstrate that the combined model outperforms both the individual models and LightGBM in terms of time series prediction accuracy and anomaly detection effectiveness, indicating an enhanced ability to handle long-term memory challenges. Furthermore, the combined model exhibits great potential of practical application for the early warning of gearbox bearing anomalies during actual wind turbine daily operation and maintenance, providing a valuable solution for the offshore wind turbine industry.

Objects guide human gaze behavior in dynamic real-world scenes.

The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by space-based attention towards locations with salient features. Increasing evidence suggests, however, that visual attention does not select locations with high saliency but operates on attentional units given by the objects in the scene. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented five different models within this framework: two purely spatial models, where one is based on low-level saliency and one on high-level saliency, two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information, and a mixed model with object-based attention and selection but space-based inhibition of return. We optimized each model's parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and returning to objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.

Exergy analysis of pressure reduction, back pressure and intermittent air supply configuration of utilization/expansion stage in compressed air systems

Despite their many advantages, industrial compressed air systems are characterized by high energy consumption and very low energy efficiency. This is primarily due to the oversizing of pneumatic actuators, leading to excessive compressed air consumption in the utilization stage. Various methods have been proposed in the literature to address this issue, which can be classified into three groups: supply pressure reduction, back pressure increase, and intermittent power supply. However, no exergy analysis compares these methods in the utilization stage of a pneumatic system was performed. In this study, we conducted an exergy analysis to compare the exergy consumption and exergy savings of different pneumatic system configurations: the classic configuration, supply pressure reduction, back pressure increase, and intermittent power supply. The paper shows the most key places in CAS in terms of exergy losses, which result mainly from dead volumes, outlet loss and oversizing of components such as directional control valve, distributed pneumatic line and actuator. Losses in those components can amount to over 80% of input exergy and can be reduced even 3–9 times. Exergy evaluation of oversizing configurations showed that the highest exergy savings were obtained for a back pressure configuration (77%) and intermittent air supply (75%). The exergy savings for the pressure reduction configurations amounted to almost 50%. The paper presents mathematical models for several individual pneumatic components, including the actuator, directional control valve, muffler, pneumatic line and pressure reducer. The demonstrated mathematical model and exergy analysis can play a instrumental role in evaluating the exergy efficiency and optimization of a pneumatic system.

A Modular Approach for Diesel Engine Air Path Control Based on Nonlinear MPC

This article presents a systematic approach to realize highly dynamic control strategies for the air path of diesel engines. It is based on grey-box models of individual air path components, designed to be applied in nonlinear model predictive control (NMPC). Specifically, they are suited for algorithmic differentiation and gradient-based optimization. This modular approach allows to derive models for a variety of complex air path systems and can be identified with a low amount of measurement data. An NMPC structure, which is based on these models, enables the tracking of arbitrary air path reference signals and allows the introduction of further control objectives for overactuated systems. We demonstrate our approach’s general applicability and effectiveness on two different laboratory engines using rapid control prototyping hardware. For a turbocharged light-duty diesel engine with dual-loop exhaust gas recirculation (EGR), we apply the proposed control structure to track intake manifold gas conditions while simultaneously minimizing engine pumping losses. Experimental results show an excellent tracking performance and reduced pumping losses compared to other control strategies. For a turbocharged heavy-duty engine with high-pressure EGR, we experimentally demonstrate a superior tracking performance to that obtained with a reference controller. Due to the modular and systematic approach, the algorithm design is straightforward, and the experimental calibration effort is low.

Analiza izbranih drevesnih modelov razvoja gozdov, primernih za modeliranje na velikoprostorski ravni, in možnosti njihove uporabe v Sloveniji

This article discusses single-tree growth models, which have become an important tool for decision-making, management and strategic decisions in the field of forest management. In Slovenia, we have only recently begun systematic development in the field of forest modelling, which includes the development of a matrix population model, testing selected models from abroad and the development of individual model components. The goal of our work is to introduce the field of empirical single-tree models for modelling forest development on a larger scale. We provide a detailed overview of established methods for modelling individual components of tree models, such as radial and height growth, crown recession, mortality, and recruitment and regeneration. We evaluated the suitability of the selected models from the perspective of their applicability in Slovenia. We conclude that the SILVA, WEHAM, MASSIMO and CALDIS models have the greatest potential for use in Slovenia, as they are all suitable for the different forest types and mixed forests with different structure that prevail in Slovenia. In addition to testing the existing models, we propose the development of new models adapted to the heterogeneous and mixed stands in Slovenia. We also propose expanding the set of indicators in forest inventories and measuring additional tree characteristics, such as canopy characteristics, which would expand forest modelling opportunities in Slovenia. In the conclusions, we also discuss the potential use of machine learning in forest development modelling, as this type of model could represent the next generation of forest models.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts’ predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models' predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency.

Residual risk identified in routine noninvasive follow-up assessments in pulmonary arterial hypertension.

The 2022 ESC/ERS guidelines on pulmonary hypertension recommend noninvasive risk assessments based on three clinical variables during follow-up in patients with pulmonary arterial hypertension (PAH). We set out to test whether residual risk can be captured from routinely measured noninvasive clinical variables during follow-up in PAH. We retrospectively studied 298 incident PAH patients from a German pulmonary hypertension centre who underwent routine noninvasive follow-up assessments including exercise testing, echocardiography, electrocardiography, pulmonary function testing and biochemistry. To select variables, we used least absolute shrinkage and selection operator (LASSO)-regularised Cox regression models. Outcome was defined as mortality or lung transplant after first follow-up assessment. 12 noninvasive variables that were associated with outcomes in a training sub-cohort (n=208) after correction for multiple testing entered LASSO modelling. A model combining seven variables discriminated 1-year (area under the curve (AUC) 0.83, 95% confidence interval (CI) 0.68-0.99, p=8.4×10-6) and 3-year (AUC 0.81, 95% CI 0.70-0.92, p=2.9×10-8) outcome status in a replication sub-cohort (n=90). The model's discriminatory ability was comparable to that of the guideline approach in the replication sub-cohort. From the individual model components, World Health Organization functional class, 6-min walking distance and the tricuspid annular plane systolic excursion to systolic pulmonary arterial pressure (TAPSE/sPAP) ratio were sensitive to treatment initiation. Addition of TAPSE/sPAP ratio to the guideline approach numerically increased its ability to discriminate outcome status. Our real-world data suggest that residual risk can be captured by noninvasive clinical procedures during routine follow-up assessments in patients with PAH and highlights the potential use of echocardiographic imaging to refine risk assessment.

Design of Proposed Software System for Prediction of Iliosacral Screw Placement for Iliosacral Joint Injuries Based on X-ray and CT Images.

One of the crucial tasks for the planning of surgery of the iliosacral joint is placing an iliosacral screw with the goal of fixing broken parts of the pelvis. Tracking of proper screw trajectory is usually done in the preoperative phase by the acquisition of X-ray images under different angles, which guide the surgeons to perform surgery. This approach is standardly complicated due to the investigation of 2D X-ray images not showing spatial perspective. Therefore, in this pilot study, we propose complex software tools which are aimed at making a simulation model of reconstructed CT (DDR) images with a virtual iliosacral screw to guide the surgery process. This pilot study presents the testing for two clinical cases to reveal the initial performance and usability of this software in clinical conditions. This model is consequently used for a multiregional registration with reference intraoperative X-ray images to select the slide from the 3D dataset which best fits with reference X-ray. The proposed software solution utilizes input CT slices of the pelvis area to create a segmentation model of individual bone components. Consequently, a model of an iliosacral screw is inserted into this model. In the next step, we propose the software CT2DDR which makes DDR projections with the iliosacral screw. In the last step, we propose a multimodal registration procedure, which performs registration of a selected number of slices with reference X-ray, and based on the Structural Similarity Index (SSIM) and index of correlation, the procedure finds the best match of DDR with X-ray images. In this pilot study, we also provide a comparative analysis of the computational costs of the multimodal registration upon various numbers of DDR slices to show the complex software performance. The proposed complex model has versatile usage for modeling and surgery planning of the pelvis area in fractures of iliosacral joints.

Fuzzy Algorithmic Modeling of Economics and Innovation Process Dynamics Based on Preliminary Component Allocation by Singular Spectrum Analysis Method

In this article, the authors propose an algorithmic approach to building a model of the dynamics of economic and, in particular, innovation processes. The approach under consideration is based on a complex algorithm that includes (1) decomposition of the time series into components using singular spectrum analysis; (2) recognition of the optimal component model based on fuzzy rules, and (3) creation of statistical models of individual components with their combination. It is shown that this approach corresponds to the high uncertainty characteristic of the tasks of the dynamics of innovation processes. The proposed algorithm makes it possible to create effective models that can be used both for analysis and for predicting the future states of the processes under study. The advantage of this algorithm is the possibility to expand the base of rules and components used for modeling. This is an important condition for improving the algorithm and its applicability for solving a wide range of problems.

Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease

Despite recent progress, non-invasive tests for the diagnostic assessment and monitoring of non-alcoholic fatty liver disease (NAFLD) remain an unmet need. Herein, we aimed to identify diagnostic signatures of the key histological features of NAFLD. Using modified-aptamer proteomics, we assayed 5,220 proteins in each of 2,852 single serum samples from 636 individuals with histologically confirmed NAFLD. We developed and validated dichotomized protein-phenotype models to identify clinically relevant severities of steatosis (grade 0 vs. 1-3), hepatocellular ballooning (0 vs. 1 or 2), lobular inflammation (0-1 vs. 2-3) and fibrosis (stages 0-1 vs. 2-4). The AUCs of the four protein models, based on 37 analytes (18 not previously linked to NAFLD), for the diagnosis of their respective components (at a clinically relevant severity) in training/paired validation sets were: fibrosis (AUC 0.92/0.85); steatosis (AUC 0.95/0.79), inflammation (AUC 0.83/0.72), and ballooning (AUC 0.87/0.83). An additional outcome, at-risk NASH, defined as steatohepatitis with NAFLD activity score ≥4 (with a score of at least 1 for each of its components) and fibrosis stage ≥2, was predicted by multiplying the outputs of each individual component model (AUC 0.93/0.85). We further evaluated their ability to detect change in histology following treatment with placebo, pioglitazone, vitamin E or obeticholic acid. Component model scores significantly improved in the active therapies vs. placebo, and differential effects of vitamin E, pioglitazone, and obeticholic acid were identified. Serum protein scanning identified signatures corresponding to the key components of liver biopsy in NAFLD. The models developed were sufficiently sensitive to characterize the longitudinal change for three different drug interventions. These data support continued validation of these proteomic models to enable a "liquid biopsy"-based assessment of NAFLD. Not applicable. An aptamer-based protein scan of serum proteins was performed to identify diagnostic signatures of the key histological features of non-alcoholic fatty liver disease (NAFLD), for which no approved non-invasive diagnostic tools are currently available. We also identified specific protein signatures related to the presence and severity of NAFLD and its histological components that were also sensitive to change over time. These are fundamental initial steps in establishing a serum proteome-based diagnostic signature of NASH and provide the rationale for using these signatures to test treatment response and to identify several novel targets for evaluation in the pathogenesis of NAFLD.

Comparative analysis of the results of the calculation of the frame node in the IDEA STATICA СCONNECTION software and according to the methods of normative documents

The development of modern construction increases the requirements for the reliability and accuracy of determining the stress-strain state of structures, nodes and details of designed objects. At the same time, today it is difficult to imagine the calculation of any structure without the use of modern software. When calculating the building as a whole, all joints, abutments and connections, as a rule, are specified in a simplified manner. At the same time, the special attention should be paid to the calculation of nodes connecting structures to each other, since, depending on the operating conditions of the node, a different distribution of the stress-strain state is possible, which in turn affects the geometric characteristics and operation of the structures. Thus, the calculation scheme of the node should reflect its real behavior as part of the structure, which, in turn, can lead to the complication of model creation, significant time costs and errors in calculations. Models of materials and individual components (bolts, welds) should also match the real ones as much as possible. This process is most relevant in the design of nodes of metal structures. The design of typical nodes is carried out using serial solutions, standard methods and manuals, but in the case of non-standard nodes of a complex shape, it is advisable to use the component method of finite elements reflected in foreign literature. This approach is implemented in the Idea StatiCa software complex. The model made in this program gives the modern engineer a sufficient amount of information about the behavior of the unit, its stress-strain state and the coefficients of use of individual components, as well as about the results of joint inspections. The results obtained during the calculation of the frame unit using this complex were compared with the calculations performed manually according to the methods of various regulatory documents.

Deep learning models for histologic grading of breast cancer and association with disease prognosis

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study

BackgroundThe multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk...

Skill Assessment of NCEP Three-Way Coupled HWRF–HYCOM–WW3 Modeling System: Hurricane Laura Case Study

Abstract In this research, we develop a three-way coupled prediction system to advance the realization of air–sea interaction processes. This study considers the sea-state-dependent momentum flux and nonlinear interactions between waves, winds, and ocean currents using the U.S. National Centers for Environmental Prediction’s operational Hurricane Weather Research and Forecasting (HWRF)-Hybrid Coordinate Ocean Model (HYCOM) coupled modeling system. Wave feedback is performed through the air–sea interaction module (ASIM) added to WAVEWATCH III (WW3), which employs the wave boundary layer to parameterize unresolved high-frequency tail spectra by using the mean flux profile constructed from the conservation of total momentum and wave energy. The atmospheric momentum flux is updated using the sea-state-dependent Charnock coefficient, wave-induced stress, and ocean surface currents before being passed to HYCOM. Wave coupling in HYCOM includes Coriolis–Stokes forcing to simulate wave–current interactions and to enhance mixing to account for Langmuir turbulence. The fully coupled system is tested for Hurricane Laura (2020). This paper examines the forecast skills of the individual component models by comparing simulations with observations. Without skill degradation of HYCOM and WW3, the three-way coupling method improves the track and intensity forecast skills by 5% each over those of HWRF-HYCOM coupling, and 27% and 17% over those of uncoupling, respectively. Importantly, this fully coupled system outperforms rapid intensification by reducing the intensification magnitude and matching the occurrence and duration. Overall, the forecast performance evaluated in the study establishes a baseline for the next-generation hurricane prediction system. Significance Statement This study is the documentation of the numerical advancement of tropical cyclone (TC) forecasting and the demonstration of the improvement of the TC intensity forecast. A key asset is the importance of wave coupling and inclusion of the nonlinear interactions in the air–sea interaction zone, and is to advance the current U.S. NCEP operational coupled hurricane modeling system. By assessing simulations for Hurricane Laura (2020), we demonstrate skill improvement of the storm structure, and intensity forecasts, especially for rapid intensification (RI) by correcting the timing and the magnitude of RI simulated by uncoupling and two-way coupling.