Optimization of wing tip store modeling

Compressible flow past thin wings and slender bodies are studied. First, subsonic flow past thin wings are analyzed by means of potential flow theory. The kernel function method is introduced for arbitrary planforms undergoing simple harmonic oscillations. The spanwise polynomial approximation and the chordwise trigonometric function approximations which automatically satisfy the Kutta condition and inherit the leading edge singularity are used. The Doublet-Lattice method as a more general numerical approach is given for the analysis of the flow past additional surfaces like tail or store surfaces, which are not necessarily in plane with the wing surface or surfaces having spanwise deflections. Airfoil response to the arbitrary unsteady motion is also given for subsonic flows. A brief review of shock waves and Mach waves in a supersonic flow is given. Afterwards, unsteady supersonic potential flow is studied for a simple harmonically oscillating point source. First, unsteady flow past an airfoil is considered. Then, supersonic flow about thin wings are analyzed using the Mach box technique. Introduction to supersonic kernel function method is briefly provided. Arbitrary unsteady motion of an airfoil in a supersonic flow is presented. Slender body theory is introduced to analyze the cross flow past bodies of considerable fineness where the cross flow is shown to be approximately incompressible under certain conditions. In connection with the slender body approximation, the Munk’s airship theory is utilized to predict the stability derivatives of missile like bodies.

The discusser would like to thank the author for presenting an improved nonlinear Muskingum model with a variable exponent parameter and would like to draw attention to some points. The need to consider a variable exponent parameter for a nonlinear Muskingum model has been pointed out by the author. The variable exponent parameter accounts for the nonlinearity of flood wave and clearly produces better model performance than that with the constant exponent parameter. The author found that even a small number of inflow levels result in an improvement in model performance. As mentioned by the author, as the number of inflow levels increase, model performance improves, but the improvement was found to be small beyond five levels in different case studies. The variable exponent parameter proposed by the author accounts for the nonlinearity of flood wave in a disaggregate manner. When five inflow levels are used, the number of model parameters will be seven making it a highly overparameterized model. Additional parameters should not be added to a model unless the increased accuracy justifies the increase in model complexity. Also, model complexity can sometimes lead to an undesirable loss of predictive power. In a regression model, accuracy needs to be balanced against complexity, and slight improvements in accuracy do not compensate for the additional complexity. As it will be shown in this discussion, considering a suitable continuous functional form for exponent parameter of nonlinear Muskingum model results in a substantial improvement in model performance. The proposed general functional form has five parameters and thus will be comparable with the case of five inflow levels proposed by the author. The author and most of the previous researchers have used inaccurate explicit Euler’s method [Tung’s method (Tung 1985)] along with the manipulated discharge equation to get a better fit for observed data. This manipulation [using It−1 rather than It in Eq. (6) of the original paper] is not acceptable from a mathematical viewpoint. All explicit solution methods will produce similar results for small size of the time interval, Δt, but in practice for historical field data, the size of the time interval is fixed and may not be small enough, and thus a suitable solution method with sufficient accuracy should be used. As shown by Vatankhah (2010, 2014), fourth-order Runge-Kutta method is an accurate and suitable solution method among the explicit solution methods for solving the Muskingum model which will be used in this discussion.

The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane Grus grus in a spatio‐temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio‐temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity. Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact‐focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability but can be a useful approach to improving insight gained from model outputs.

<p>The importance of carbon fluxes between soils and atmosphere and their storage capacities have made soils a key component of nature-based solutions to mitigate climate change. Consequently, the need to forecast soil carbon dynamics at the decadal time scales is becoming a key research avenue for soil scientists and biogeochemists. One of the most important tools to fill this objective is the use of models, which have been developed and implemented since the mid- 20th century. Presuming that integrating more mechanisms would improve these models, some models of increased complexity were recently developed. Indeed, since roughly two decades, several approaches have been proposed to better represent the effect of some key mechanisms in particular related to soil biology and soil physics. For instance, few models are now able to describe explicitly and reliably the importance of soil microorganisms on the soil carbon dynamics from plot to global scale. Here, we will discuss what is the rationale behind model complexity increase, what are the limitations associated and discuss the status of evaluation for model prediction. In particular, we will show that despite model of increased complexity may provide accurate predictions in some conditions, those complex models also came with their own implicit assumptions and limitations that must be well understood before using complex models to forecast soil carbon dynamics and feed policy decisions. We also consider that models of lower complexity, which have been generally developed earlier, have also their own assets and have often been better evaluated. As a consequence, models of lower complexity may be considered as more robust and more adapted to forecast soil carbon dynamic and the improvement of their parameterization should also be considered as a valuable alternative. We will also present why multi model approach is important to reduce uncertainties and explain why using model ensemble, when implemented a diversity of carefully evaluated models, is a key method to forecast soil carbon dynamics. Finally, we will show that the fabulous growth of model complexity and the societal needs associated to soil is an incredible opportunity for soil scientists to increase the understanding of soil carbon cycle, in particular in the context of global changes, and to improve our future predictions on soil carbon. </p>

The detection of road objects plays an essential role in the development of autonomous vehicles in intelligent transportation systems, which has become an emerging field in deep learning. Several objects on the road, like vehicles, pedestrians, etc., are necessary to be accurately identified, which guarantees the safety of other people and vehicles in the surroundings. In this article, we aim to design an efficient object detection model for autonomous driving systems. To achieve this, we investigate the recently developed YOLOv7 and optimize the same for improving the detection performance to satisfy the realtime safety requirements of autonomous vehicles. We perform extensive experimentation and demonstrate the effectiveness of our method on the BDDIOOK dataset. Experimental results show that our proposed method increases the detection accuracy to 82.6% and inference speed to 97.2 FPS compared to the baselines, with no additional increase in model complexity.

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

Many polymer-type materials show a rate-dependent and nonlinear rheological behavior. Such a response may be modeled by using a series of spring-dashpot systems. However, in order to cover different time scales the number of systems may become unreasonably large. A more appropriate treatment based on continuum mechanics will be presented herein. This approach uses representation theorems for deriving material equations and allows for a systematic increase in modeling complexity. Moreover, we propose an approach based on energy to determine thematerial parameters.This method results in a simple linear regression problemeven for highly nonlinearmaterial equations. Therefore, the inverse problem leads to a unique solution. The significance of the proposed method is that the stored and dissipated energies necessary for the procedure are measurable quantities. We apply the proposed method to a “semi-solid” material and measure its material parameters by using a simple-shear rheometer.

Most faults in Iceland strike roughly parallel to the divergent plate boundary, a part of the North American‐Eurasian plate boundary, which would be expected to lead to primarily normal faulting. However, several studies have observed a significant component of rift‐parallel strike‐slip faulting in Iceland. To investigate these fault kinematics, we use the boundary element method to model fault slip and crustal stress patterns of the Icelandic tectonic system, including a spherical hotspot and uniaxial stress that represents rifting. On a network of faults, we estimate the slip required to relieve traction imposed by hotspot inflation and remote rifting stress and compare the model results with observed slip kinematics, crustal seismicity, and geodetic data. We note a good fit between model‐predicted and observed deformation metrics, with both indicating significant components of normal and strike‐slip faulting and consistency between recent data and longer‐term records of geologic fault slip. Possible stress permutations between steeply plunging σ1 and σ2 axes are common in our models, suggesting that localized stress perturbations may impact strike‐slip faulting. Some increases in model complexity, including older hotspot configurations and allowing fault opening to simulate dike intrusion, show improvement to model fit in select regions. This work provides new insight into the physical mechanisms driving faulting styles within Iceland away from the current active plate boundary, implying that a significant portion of observed strike‐slip faulting is likely caused by the combined effects of tectonic rifting, hotspot impacts, and mechanical interactions across the fault network.

The use of MRI-guided radiation therapy (MRIgRT) has shown considerable advantages. However, the acquisition of electron density information still relies on Computed tomography (CT) images. In the context of medical image synthesis, achieving high global and local accuracy, as well as efficient synthetic CT (sCT) generation, remains a key challenge. This study aims to improve the accuracy of sCT image generation from MRI by introducing a novel deep learning model that leverages global context and local detail, with a minimal increase in model complexity. In this study, we propose a novel generative adversarial network based on Mamba block and Residual Constraint (RC) strategy, namely RC-MambaGAN, which achieves a significant improvement in accuracy with only a minimal increase in computation time required for sCT generation. Specifically, we integrate Mamba block into the generator to enhance the model's ability to capture long-range contextual information, while preserving the precision of local features and maintaining the global structure of the image. The RC strategy improves the alignment between sCT and real CT by minimizing residuals. The model was validated on multi-center datasets that included both tumor and pelvic-region data. Furthermore, the dosimetric accuracy of the generated sCT images was evaluated to assess their clinical applicability. RC-MambaGAN achieved high performance with mean absolute error (MAE) of 37.094±8.761 HU, peak signal-to-noise ratio (PSNR) of 28.424±1.145dB, structural similarity index measure (SSIM) of 0.947±0.004, and Mutual Information (MI) of 1.426±0.012. External validation demonstrated that RC-MambaGAN outperformed existing state-of-the-art methods in both image quality and quantitative accuracy, even for structures visible in MRI but not in CT images. The proposed RC-MambaGAN markedly improves the image quality and anatomical accuracy of sCT images derived from MRI, thereby advancing the feasibility of MRI-only radiotherapy workflows.

The popularity of LSTM networks in wind prediction is due to their inherent capabilities in handling the long-term dependencies of sequential data. This normally outperforms traditional methods like ARIMA. However, current work has illustrated that EMD-based approaches outperform LSTM models in many critical performance metrics. Although it is very powerful in gathering historical patterns due to the enhanced gating mechanisms, LSTM has a lot of challenges regarding computational efficiency and flexibility. EMD models overwhelmingly outperformed the LSTM model in computational time by probably about 20-25%, thereby solving a weakness that may be one of the intrinsic weaknesses of the LSTM model. In addition, the EMD models improved by about 2-4% in accuracy as compared to LSTM, thus evidencing better ability in the prediction of the wind data. On feature extraction, EMD did better, improving by 7% over LSTM. Moreover, EMD has better robustness and adaptability, with improvements of 6% and 4%, respectively. This introduction of EMD does bring a bit of an increase in model complexity, this disadvantage is rather insignificant when compared with the huge advantages it brings in efficiency and accuracy. While an LSTM network is very good at modeling a long-term relationship, it suffers from some problems in handling nonlinear interactions and continuous series data, thus its effectiveness may be limited in some cases of forecasting. In contrast, EMD has a clear advantage in real-time applications owing to enhanced efficiency and precision. From the results obtained in this study, it becomes easy to envision that the EMD methodology is un-equivocally better than LSTM in all the critical aspects, such as computational efficiency, accuracy in prediction, feature extraction, resilience, and adaptability when used individually. Results indicate that EMD has a more constructive methodology toward wind pattern predictions, beating LSTM on many measures of performance. The advantages presented should be maximized in future studies aimed at further improving and optimizing a wind-forecasting system, with special attention toward EMD's benefits in gaining more accurate and efficient forecasts.

Physiological trend analysis of a novel cardio-pulmonary model during a preload reduction manoeuvre

Evidence of alternative states in freshwater lakes: A spatially-explicit model of submerged and floating plants

In recent years, many ship detection algorithms based on convolutional neural networks (CNNs) have been proposed to improve the performance of ship detection. However, with the increase in model complexity and size, it is challenging to deploy these models to resource-constrained edge platforms. In this letter, a low coupling algorithm that belongs to anchor-free methods is proposed for ship detection to reduce the model complexity and still obtain a competitive performance. The proposed low coupling network (LCNet) is easy to deploy and contributes to speeding up the inference and improving memory utilization. In addition, we propose a model compression process consisting of the quantization-aware training (QAT) method and a structural pruning method based on Taylor expansion, which can effectively reduce the model size according to hardware resource constraints. Comparative experimental results demonstrate that LCNet outperforms the state-of-the-art ship detection and natural object detection algorithms, with a 95.27% mAP and 88.91% F1 score on the HRSC2016 dataset. Our proposed model compression method also achieves a compression ratio of at least 80% with a negligible loss of performance.

A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model

The paper presents a comparative analysis of the generalized linear regression model with the leading machine learning method Feed Forward Neural Network (FFNN) from the point of view of predicting counting data. These two models are described and compared from a theoretical and practical point of view. The stability of the models on the bicycle rental data set is checked, their accuracy is evaluated, the learning curves are built on test and training data sets. In order to improve the interpretability of models, the importance of input variables is evaluated. Because FFNN is often called the “black box” method, there is no direct way to evaluate the importance of variables. A new indirect method for assessing the importance of variables for deep neural networks based on the principles of information theory is proposed. It has been demonstrated that the FFNN network provides much better predictive power compared to the generalized linear regression model with a slight increase in model complexity.