Search Results (35)

The influence of distance on Taylor diagrams with one, two, and three depressed inner lobes was analyzed in the context of linear distributions. High sidelobes are tolerated in these patterns, except in the case of the inner lobes, which are positioned at a significantly lower level to minimize interference and optimize efficiency. The classical method described by Elliott was used to compute the necessary roots in the Taylor distribution. The study was conducted considering $\overline{n}$ equal to 6 and a sidelobe level (SLL) of −20 dB for all lobes except the first inner positioned at −40 dB. Full article

The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective B-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor B-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL. Full article

Unmanned aerial vehicles (UAVs) equipped with hyperspectral hardware systems are widely used in urban planning and land classification. However, hyperspectral sensors generate large volumes of data that are rich in both spatial and spectral information, making its efficient processing in resource-constrained devices challenging. While transformers have been widely adopted for hyperspectral image classification due to their global feature extraction capabilities, their quadratic computational complexity limits their applicability for resource-constrained devices. To address this limitation and enable the real-time processing of hyperspectral data on UAVs, we propose a lightweight multi-head MambaOut with a CosTaylorFormer (LMHMambaOut-CosTaylorFormer). First, 3D-2D CNN is used to extract both spatial and spectral shallow features from hyperspectral images. Following this, one branch employs a linear transformer, CosTaylorFormer, to extract global spectral information. More specifically, we propose CosTaylorFormer with a cosine function, adjusting the weights based on the spectral curve distribution, which is more conducive to establishing long-distance spectral dependencies. Meanwhile, compared with other linearized transformers, the CosTaylorFormer we propose better improves model performance. For the other branch, we propose multi-head MambaOut to extract global spatial features and enhance the network classification effect. Moreover, a dynamic information fusion strategy is proposed to adaptively fuse spatial and spectral information. The proposed network is validated on four datasets (IP, WHU-Longkou, SA, and PU) and compared with several models, demonstrating its superior classification accuracy; however, the number of model parameters is only 0.22 M, thus achieving better balance between model complexity and accuracy. Full article

In a modern flexible interconnected distribution network, the dynamic coupling effect between the traditional AC network model and the power electronic converter significantly enhances the nonlinearity and non-convexity of power flow calculations. In particular, when a one-end converter station quits operating due to a fault, it is necessary to ensure that the remaining converter stations can continue to maintain the normal operation of the interconnected system, which leads to the convergence problem of the traditional physical-driven iterative method. Aiming to address this problem, this study discusses the data-driven linearization method of the current distribution network power flow in depth and proposes a linearized power flow calculation (LPFC) of a flexible interconnected distribution network based on a data–physical fusion drive. Based on the traditional linearization method based on physical characteristics and first-order Taylor expansion, the model uses the partial least squares method to compensate for the linearization error and can normally cope with the failure of the flexible interconnected system. The proposed model greatly improves the convergence and computational efficiency of the power flow model under the premise of ensuring the linearization accuracy and can adapt to different load levels to achieve accurate error compensation. In addition, based on an actual engineering example, this paper introduces the converter station model, constructs a flexible interconnected system, and verifies the applicability of the proposed model. Full article

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET₀) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R² of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model. Full article

In this paper, the oscillatory behavior of the solutions for a Parkinson’s disease model with discrete and distributed delays is discussed. The distributed delay terms can be changed to new functions such that the original model is equivalent to a system in which it only has discrete delays. Using Taylor’s expansion, the system can be linearized at the equilibrium to obtain both the linearized part and the nonlinearized part. One can see that the nonlinearized part is a disturbed term of the system. Therefore, the instability of the linearized system implies the instability of the whole system. If a system is unstable for a small delay, then the instability of this system will be maintained as the delay increased. By analyzing the linearized system at the smallest delay, some sufficient conditions to guarantee the existence of oscillatory solutions for a delayed Parkinson’s disease system can be obtained. It is found that under suitable conditions on the parameters, time delay affects the stability of the system. The present method does not need to consider a bifurcating equation. Some numerical simulations are provided to illustrate the theoretical result. Full article

The aim of the paper is to analyze changes and peculiarities of behavior of economic agents with bounded rationality in the New Keynesian model, in which imported equipment and technology are one of the factors of production, and households consume only domestic products. The formation of output gap and inflation expectations by agents is based on stationary values of these variables and on extrapolation of the latest available data on inflation and the output gap. The weight shares of agents applying these rules change endogenously. Histograms of the frequency distribution of the degree of buoyancy and the impulse responses of monetary policy shocks and technology shocks to the variables under study show that a less open economy tends to go through an economic cycle with a smaller amplitude than a more rigid economy. Analyses of the trade-offs between the volatility of inflation and the output gap at different parameter values in the Taylor rule show their non-linear nature (in contrast to standard models with rational expectations). An important result obtained in this analysis is that the rational expectations hypothesis is more consistent with a closed economy than with an open one. Full article

A comprehensive assessment of Antarctic sea ice cover prediction is conducted for twelve CMIP6 models under the scenario of SSP2-4.5, with a comparison to the observed data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) during 2015–2021. In the quantitative evaluation of sea ice extent (SIE) and sea ice area (SIA), most CMIP6 models show reasonable variation and relatively small differences compared to AMSR2. CMCC-CM4-SR5 shows the highest correlation coefficient (0.98 and 0.98) and the lowest RMSD (0.98 × 10⁶ km² and 1.07 × 10⁶ km²) for SIE and SIA, respectively. In the subregions, the models with the highest correlation coefficient and the lowest RMSD for SIE and SIA are inconsistent. Most models tend to predict smaller SIE and SIA compared to the observational data. GFDL-CM4 and FGOALS-g3 show the smallest mean bias (−4.50 and −1.21 × 10⁵ km²) and the most reasonable interannual agreement of SIE and SIA with AMSR2, respectively. In the assessment of sea ice concentration (SIC), while most models can accurately predict the distribution of large SIC surrounding the Antarctic coastal regions, they tend to underestimate SIC and are unable to replicate the major patterns in the sea ice edge region. GFDL-CM4 and FIO-ESM-2-0 exhibit superior performance, with less bias (less than −5%) and RMSD (less than 23%) for SIC in the Antarctic. GFDL-CM4, FIO-ESM-2-0, and CESM2 exhibit relatively high positive correlation coefficients exceeding 0.60 with the observational data, while few models achieve satisfactory linear trend prediction of SIC. Through the comparison with RMSD, Taylor score (TS) consistently evaluates the Antarctic sea ice cover and proves to be a representative statistical indicator and applicable for its assessment. Based on comprehensive assessments of sea ice cover, CESM2, CMCC-CM4-SR5, FGOALS-g3, FIO-ESM-2-0, and GFDL-CM4 demonstrate more reasonable prediction performance. The assessment findings enhance the understanding of the uncertainties associated with sea ice in the CMIP6 models and highlighting the need for a meticulous selection of the multimodel ensemble. Full article

Composite films of a graded miscrostructure hold the promise of achieving optimal use of the filler material, resulting in composites with improved and application-taylored properties. In the context of barrier materials in which the reinforcing phase comes in the form of flakes or platellets, concentrating the filler particles in certain critical regions is thought to achieve economy in filler usage while ensuring superior barrier performance. The objective of the present article is to quantitatively test this hypothesis and provide guidelines on the expected barrier improvement. A model is developed, according to which a graded miscostructure in a composite film offers a quantitative improvement over an equivalent homogeneous microstructure; this improvement is quantified using a coefficient $\beta$, which depends on the form of the graded miscrostructure, specifically the distribution of the number-density of the filler particles across the film. It is shown that $\beta =1$ for a uniform microstructure and $\beta >1$ for a graded one, indicating that a graded miscrostructure will indeed result in improved barrier properties. Analytical expressions for $\beta$ are developed for certain typical distributions; for a linear filler distribution, it is shown that $\beta =4/3$. This model is tested against detailed multi-particle simulations and is found to be in excellent agreement with computational results. Full article

This paper proposes a recursive conic approximation methodology to deal with the optimal power flow (OPF) problem in unbalanced bipolar DC networks. The OPF problem is formulated through a nonlinear programming (NLP) representation, where the objective function corresponds to the minimization of the expected grid power losses for a particular load scenario. The NLP formulation has a non-convex structure due to the hyperbolic equality constraints that define the current injection/absorption in the constant power terminals as a function of the powers and voltages. To obtain an approximate convex model that represents the OPF problem in bipolar asymmetric distribution networks, the conic relation associated with the product of two positive variables is applied to all nodes with constant power loads. In the case of nodes with dispersed generation, a direct replacement of the voltage variables for their expected operating point is used. An iterative solution procedure is implemented in order to minimize the error introduced by the voltage linearization in the dispersed generation sources. The 21-bus grid is employed for all numerical validations. To validate the effectiveness of the proposed conic model, the power flow problem is solved, considering that the neutral wire is floating and grounded, and obtaining the same numerical results as the traditional power flow methods (successive approximations, triangular-based, and Taylor-based approaches): expected power losses of 95.4237 and 91.2701 kW, respectively. To validate the effectiveness of the proposed convex model for solving the OPF problem, three combinatorial optimization methods are implemented: the sine-cosine algorithm (SCA), the black-hole optimizer (BHO), and the vortex search algorithm (VSA). Numerical results show that the proposed convex model finds the global optimal solution with a value of 22.985 kW, followed by the VSA with a value of 22.986 kW. At the same time, the BHO and SCA are stuck in locally optimal solutions (23.066 and 23.054 kW, respectively). All simulations were carried out in a MATLAB programming environment. Full article

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor’s series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO${}_2$ to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about $43.9754\%$ in daily energy losses, $26.9957\%$ in energy purchasing and operating costs, and $27.3771\%$ in CO${}_2$ emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods. Full article

When the grid-based finite difference (FD) method is used for elastic wavefield forward modeling, it is inevitable that the grid divisions will be inconsistent with the actual velocity interface, resulting in problems related to the stepped grid diffraction and inaccurate travel time of reflected waves. The generalized finite difference method (GFDM), which is based on the Taylor series expansion and weighted least square fitting, solves these problems. The partial derivative of the unknown parameters in the differential equation is represented by the linear combination of the function values of adjacent nodes. In this study, the Poisson disk node generation algorithm and the centroid Voronoi node adjustment algorithm were combined to obtain an even and random node distribution. The generated nodes fit the internal boundary more accurately for model discretization, without the presence of diffracted waves caused by the stepped grid. To avoid the instability caused by the introduction of boundary conditions, a Cerjan damping boundary condition was proposed for boundary reflection processing. The test results generated by the different models showed that the generalized finite difference method can effectively solve the problems related to inaccurate travel time of reflection waves and stepped grid diffraction. Full article

A fair curve with exceptional properties, called the log-aesthetic curves (LAC) has been extensively studied for aesthetic design implementations. However, its implementation in terms of functional design, particularly hydrodynamic design, remains mostly unexplored. This study examines the effect of the shape parameter $\alpha$ of LAC on the drag generated in an incompressible fluid flow, simulated using a semi-implicit backward difference formula coupled with P${}_2$−P${}_1$ Taylor–Hood finite elements. An algorithm was developed to create LAC hydrofoils that were used in this study. We analyzed the drag coefficients of 47 LAC hydrofoils of three sizes with various shapes in fluid flows with Reynolds numbers of 30, 40, and 100, respectively. We found that streamlined LAC shapes with negative $\alpha$ values, of which curvature with respect to turning angle are almost linear, produce the lowest drag in the incompressible flow simulations. It also found that the thickness of LAC objects can be varied to obtain similar drag coefficients for different Reynolds numbers. Via cluster analysis, it is found that the distribution of drag coefficients does not rely solely on the Reynolds number, but also on the thickness of the hydrofoil. Full article

The study of rotating-disk heat-flow problems is relevant to computer storage devices, rotating machineries, heat-storage devices, MHD rotators, lubrication, and food-processing devices. Therefore, this study investigated the effects of a Hall current and motile microorganisms on nanofluid flow generated by the spinning of a disk under multiple slip and thermal radiation conditions. The Buongiorno model of a nonhomogeneous nanofluid under Brownian diffusion and thermophoresis was applied. Using the Taylor series, the effect of Resseland radiation was linearized and included in the energy equation. By implementing the appropriate transformations, the governing partial differential equations (PDEs) were simplified into a two-point ordinary boundary value problem. The classical Runge–Kutta dependent shooting method was used to find the numerical solutions, which were validated using the data available in the literature. The velocity, motile microorganism distribution, temperature, and concentration of nanoparticles were plotted and comprehensively analyzed. Moreover, the density number, Sherwood number, shear stresses, and Nusselt number were calculated. The radial and tangential velocity declined with varying values of magnetic numbers, while the concentration of nanoparticles, motile microorganism distribution, and temperature increased. There was a significant reduction in heat transfer, velocities, and motile microorganism distribution under the multiple slip conditions. The Hall current magnified the velocities and reduced the heat transfer. Thermal radiation improved the Nusselt number, while the thermal slip conditions reduced the Nusselt number. Full article