Search Results (44)

This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor that influences aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class-Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio ($AR$), taper ratio ($\lambda$), quarter-chord sweep angle (${\mathrm{\Lambda }}_{0.25}$), and tip twist angle ($\epsilon$). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing the computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag. Full article

Data analysis is widely used in research and industry where there is a need to extract information from data. A significant amount of time within a data analysis project is required to prepare the data for subsequent analysis. This paper presents a comprehensive weighted maturity model to estimate the readiness of data for subsequent data analysis, with the goal of avoiding delays due to data quality problems. The maturity model uses a questionnaire with nine criteria to determine the maturity level of data preparation. The maturity model is integrated into a web application that provides an automated evaluation of maturity and a novel visualization approach that combines a modified spider chart and augmented chord diagrams. The comprehensive weighted maturity model is a ready-to-use application that provides prospective users with an easy and quick way to check their data for maturity for subsequent data analysis, with the goal of improving the data preparation process. The weighted maturity model is applicable to all types of data analysis, regardless of the domain of the data. Full article

Air-void characteristics are defined in the EN-480-11 test method. The primary criticism of Powers’ model comes from the fact that the spacing factor is calculated with the average chord length, without taking into account the chord length distribution. The aim of this study is to determine whether an analysis of the chord length distribution can provide a more accurate estimate of the spacing factor. A data set containing 110 air-entrained concretes with various characteristics was analyzed. The artificial neural network method was applied to develop a model that determines the relationship between the spacing factor, L2, and the parameters of the air-void structure. The input parameters for the ANN-L2 model included the following: A, d, and W—characteristics of the chord size distribution, P—cement paste content, and N5—number of large pores. The ANN model allows for a sufficiently accurate estimation of the spacing factor, L2. The most significant factors that influenced L were the peak amplitude, A; peak width, W; and cement paste content, P. There was a strong correlation between the results of the ANN model and the standard spacing factor L2, indicating that both calculation methods produced comparable results. Finally, a simple method for using the ANN model to calculate the spacing factor in Excel is demonstrated. Full article

Research Subject: The research subject was the error of optoelectronic video endoscopy systems in measuring the chord length of low-pressure cylinder steam turbine blades during shaft rotation. Objective: The objective was to reduce the error of the optoelectronic system in measuring the chord length of turbine rotor blades on a closed cylinder during shaft rotation. Methodology: Analytical research and computer modeling of the information transformation process during blade image formation and processing were carried out. Theoretical and experimental evaluations of the system error were conducted. Main Results: The structure of the components contributing to the error in estimating the chord length of low-pressure turbine blades was analyzed. The contribution of individual components to the total error was identified, and methods for reducing the most significant error components were proposed. Practical Significance: The effectiveness of the proposed methods for error reduction was validated through computer simulations and experimental studies on two system prototypes. The results showed that the standard deviation of the random error component in chord measurement during dynamic operation did not exceed 0.27 mm. Full article

We present a comprehensive comparison of different Markov chain Monte Carlo (MCMC) sampling methods, evaluating their performance on both standard test problems and cosmological parameter estimation. Our analysis includes traditional Metropolis–Hastings MCMC, Hamiltonian Monte Carlo (HMC), slice sampling, nested sampling as implemented in dynesty, and PolyChord. We examine samplers through multiple metrics including runtime, memory usage, effective sample size, and parameter accuracy, testing their scaling with dimension and response to different probability distributions. While all samplers perform well with simple Gaussian distributions, we find that HMC and nested sampling show advantages for more complex distributions typical of cosmological problems. Traditional MCMC and slice sampling become less efficient in higher dimensions, while nested methods maintain accuracy but at higher computational cost. In cosmological applications using BAO data, we observe similar patterns, with particular challenges arising from parameter degeneracies and poorly constrained parameters. Full article

The focus of this paper is on estimating the stress concentration factor of circular hollow section KK-joints with different geometric parameters and subsequently assessing the effectiveness of carbon fiber-reinforced polymer (CFRP) wrapping for repairing joints with cracks. Different geometric parameters, such as θ (brace inclination angle), γ (the ratio of the outer diameter to the wall thickness of the chord), and τ (the thickness ratio of the brace to the chord), were studied to investigate changes in stress concentration using numerical simulation. The results indicated that the stress concentration factor was most sensitive to changes in θ, followed by γ. Subsequently, the effect of crack length and depth was analyzed to simulate cracks in joints subjected to reciprocating load. The results showed that changing D from T/16 to T/2 (where T is the thickness of the chord) can cause more stress concentration, with an average of 8.37%. Next, damaged joints were wrapped in carbon fiber-reinforced polymer as a repair. Analysis of the effects of different layers and directions of polymer wrap revealed that even six layers of wrapping effectively reduced the stress concentration compared to the initial model. Finally, based on the results of parametric analysis and nonlinear fitting, a calculation formula for the stress concentration factor suitable for KK-joints under axial loads is proposed. Full article

Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. Seven ML models—artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), AdaBoost, gradient boosting (GBR), and extreme gradient boosting (XGB)—were compared against three mechanics-based models: the Tran’s NLT Model (2020), the Multi-Action Shear Model (MASM), and the Compression Chord Capacity Model (CCC). Among the ML models, XGB and GBR demonstrated the highest predictive accuracy, with coefficients of determination (R²) of 0.974 and 0.966, respectively, indicating strong correlation with experimental data. Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. Statistical analysis revealed minimal bias and variability in the predictions of XGB and GBR, with a coefficient of variation (CoV) of 14%, ensuring high reliability. The NLT model, the most accurate of the mechanical-based models, achieved a mean of 1.02 and a CoV of 16% for its model error, demonstrating reasonable prediction reliability but falling behind XGB and GBR in accuracy. With Shapley additive explanations (SHAPs), the beam width and depth were identified as primary predictors of shear strength, providing critical insights for enhancing design and construction practises. Full article

Chiggers (chigger mites) are a group of tiny arthropods, and they are the exclusive vector of Orientia tsutsugamushi (Ot), the causative agent of scrub typhus (tsutsugamushi disease). Dehong Prefecture in Yunnan Province of southwest China is located on the China–Myanmar border and is an important focus of scrub typhus. Based on the field surveys in Dehong between 2008 and 2022, the present paper reports the infestation and ecological distribution of chiggers on the body surface of rodents and other sympatric small mammals (shrews, tree shrews, etc.) in the region for the first time. The constituent ratio (C_r), prevalence (P_M), mean abundance (MA), and mean intensity (MI) were routinely calculated to reflect the infestation of small-mammal hosts with chiggers. Additionally, the species richness (S), Shannon–Wiener diversity index (H), Simpson dominance index (D), and Pielou’s evenness index (E) were calculated to illustrate the chigger community structure. Preston’s log-normal model was used to fit the theoretical curve of species abundance distribution, and the Chao 1 formula was used to roughly estimate the expected total species. The “corrplot” package in R software (Version 4.3.1) was used to analyze interspecific relationships, and the online drawing software was used to create a chord diagram to visualize the host–chigger associations. From 1760 small-mammal hosts, a total of 9309 chiggers were identified as belonging to 1 family, 16 genera, and 117 species, with high species diversity. The dominant chigger species were Leptotrombidium deliense, Walchia ewingi, and Gahrliepia longipedalis, with a total C_r = 47.65% (4436/9309), among which L. deliense is the most important vector of Ot in China. The overall infestation indexes (P_M, MA, and MI) and community parameters (S, H, and E) of chiggers in the mountainous areas and outdoors were higher than those in the flatland areas and indoors, with an obvious environmental heterogeneity. Leptotrombidium deliense was the dominant species in the flatland and indoors, while G. longipedalis was the prevalent species in the mountainous and outdoor areas. The species abundance distribution of the chigger community conformed to log-normal distribution with the theoretical curve equation: ${S\left(R\right)}^{\prime }=28e^{{-[0.23(R-0)]}^2}$, indicating the existence of many rare species and only a few dominant species in the community. The expected total number of chigger species was roughly estimated to be 147 species, 30 more than the 117 species actually collected, suggesting that some uncommon species may have been missed in the sampling survey. The host–parasite association analysis revealed that one host species can harbor different chigger species, and one chigger species can parasitize different host species with low host specificity. A positive or negative correlation existed among different chigger species, indicating a cooperative or competitive interspecific relationship. The species diversity of chiggers is high in Dehong on the China–Myanmar border, and a large host sample is recommended to find more uncommon species. There is an obvious environmental heterogeneity of the chigger community, with different species diversity and dominant species in different environments. The low host specificity of chiggers and the occurrence of a large number of L. deliense in Dehong, especially in flatland areas and indoors, would increase the risk of persistent transmission of scrub typhus in the region. Full article

Accurate measurement of track irregularity and the corresponding spectrum is essential for evaluating the performance of transportation systems. Chord measuring methods can achieve fine accuracy but are limited by waveform distortion and a restricted range of recoverable wavelength. To address this, this work explores the effectiveness of integrating inclination data in chord-based measurement to obtain a higher precision and more reliable spectrum. Firstly, the theoretical principles and mathematics of the proposed method are described. We demonstrate that by utilizing inclinometer sensors, the measuring reference can be maintained throughout the measurement, therefore obtaining an authentic waveform of track irregularity. Adaptive technics are employed to examine and extract cumulative components in the measured signal, which also benefits the accuracy of spectral estimation. Error analysis is then conducted by simulated sampling. Furthermore, a case study of field measurement and numerical simulation via multi-body dynamics for a monorail system is presented. The results verify the accuracy and robustness of the proposed method, showing that it provides a broader range of recoverable wavelength, minimum parametric interference, and advantages of signal authenticity. The simulation results prove the significant effects of track irregularity on the dynamic response of the monorail system, hence revealing the value of the presented methods and results. Full article

New or in-service truss bridges, with or without upper bracing systems, may display instability phenomena such as general lateral torsional buckling of the upper chord. The buckling of structural elements, particularly in the case of steel bridges, can be associated with the risk of collapse or temporary/permanent withdrawal from service. Such incidents have occurred in the case of several bridges in different countries: the collapse of the Dee bridge with truss girders in 1847 in Cheshire, England; the collapse of the semi-parabolic truss girder bridge near Ljubičevo over the Morava River in Serbia in 1892; the collapse of the Dysart bridge in Cambria County, Pennsylvania in 2007; the collapse of the Chauras bridge in Uttarakhand, India in 2012; and the collapse of a bridge in Nova Scotia, Canada (2020), and such examples may continue. Buckling poses a significant danger as it often occurs at lower load values compared to those considered during the design phase. Additionally, this phenomenon can manifest suddenly, without prior warning, rendering intervention for its prevention impossible or futile. In contemporary times, most research and design calculation software offer the capability to establish preliminary values for buckling loads, even for highly intricate structures. This is typically achieved through linear eigenvalue buckling analyses, often followed by significantly more complex large displacement nonlinear analyses. However, interpreting the results for complex bridge structures can be challenging, and their accuracy is difficult to ascertain. Consequently, this paper aims to introduce an original method for a more straightforward estimation of the buckling load of the upper chord in steel truss bridges. This method utilizes the theory of beams on discrete elastic supports. The buckling load of the upper chord was determined using both the finite element method and the proposed methodology, yielding highly consistent results. Full article

Raman spectroscopy is a useful tool for polymorphic form-monitoring during the crystallization process. However, its application to solute concentration estimation in two-phase systems like crystallization is rare, as the Raman signal is influenced by various changing factors in the crystallization process. The development of a robust calibration model that covers all variations is complex and represents a major challenge for the implementation of Raman spectroscopy for in-line monitoring and control of the solution crystallization process. This paper describes the development of a Raman-based calibration model for estimating the solute concentration of the active pharmaceutical ingredient ceritinib. Several different calibration approaches were tested, which included both temperature and spectra of clear solutions and slurries/suspensions. It was found that the concentration of the ceritinib solution could not be accurately predicted when suspended crystals were present. To overcome this challenge, the approach was enhanced by including additional variables related to crystal size and solid concentration obtained via in-line process microscopy (chord-length distribution percentiles D10, D50 and D90) and turbidity. Partial least squares regression (PLSR) and artificial neural network (ANN) models were developed and compared based on root mean square error (RMSE). ANN models estimated the solute concentration with high accuracy, with the prediction error not exceeding 1% of the nominal solute concentration. Full article

This study addresses how site index may affect crown shape by developing a crown shape regression model for the planted L. kaempferi at high altitudes in the subtropical climate of China. A total of 9241 branches from 78 trees, including 39 dominant trees and 39 intermediate trees, were measured in Hubei Province, southern China. The branch characteristics, including branch length, branch angle, and branch chord length of all living branches, were measured by felling trees. The models that showed good performance in crown shape modeling were used and compared in the present study. The goodness of fit statistics and stability of parameter estimation of the modified Kozak equation were better than those of the segmented polynomial equation. A nonlinear mixed-effect crown shape model was developed based on the modified Kozak equation. In addition to the tree level variables of diameter at the breast height (DBH), crown ratio (CR), and tree height-to-DBH ratio, an attempt was made to incorporate the site index into the crown shape model for the planted L. kaempferi. However, the site index was not a significant variable in the crown shape model. The purpose of our study was to lay the foundation for further study of the growth of the trees and the effects of crown morphology on stem growth in the future. Full article

The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area. Full article

The estimation of the frequency of sinusoids has been the object of intense research for more than 40 years. Its importance in classical fields such as telecommunications, instrumentation, and medicine has been extended to numerous specific signal processing applications involving, for example, speech, audio, and music processing. In many cases, these applications run in real-time and, thus, require accurate, fast, and low-complexity algorithms. Taking the normalized Cramér–Rao lower bound as a reference, this paper evaluates the relative performance of nine non-iterative discrete Fourier transform-based individual sinusoid frequency estimators when the target sinusoid is affected by full-bandwidth quasi-harmonic interference, in addition to stationary noise. Three levels of the quasi-harmonic interference severity are considered: no harmonic interference, mild harmonic interference, and strong harmonic interference. Moreover, the harmonic interference is amplitude-modulated and frequency-modulated reflecting real-world conditions, e.g., in singing and musical chords. Results are presented for when the Signal-to-Noise Ratio varies between −10 dB and 70 dB, and they reveal that the relative performance of different frequency estimators depends on the SNR and on the selectivity and leakage of the window that is used, but also changes drastically as a function of the severity of the quasi-harmonic interference. In particular, when this interference is strong, the performance curves of the majority of the tested frequency estimators collapse to a few trends around and above 0.4% of the DFT bin width. Full article

Aerodynamic characteristics of revolving wing models were investigated to assess the validity of the normal force model. Aerodynamic force and torque measurements were conducted for six wing planforms (with aspect ratios of 2 and 3, and area centroid locations at 40%, 50%, and 60% of the wing length) at three different Reynolds numbers (0.5 × 10⁴, 1 × 10⁴, and 1.5 × 10⁴) and three thickness-to-chord ratios (3%, 4%, and 5%). Both early and steady phase measurements were extracted for a range of angles of attack relevant to insect flight. It was shown that the so-called “normal force” model conveniently captures the variation of the lift and drag coefficients along the first quadrant of angles of attack for all cases tested. A least squares best fit model for the obtained experimental measurements was used to estimate the key parameters of the normal force model, namely the lift curve slope, the zero-lift drag coefficient, and the peak drag coefficient. It was shown that the knowledge of only the lift curve slope and the zero-lift drag coefficient is sufficient to fully describe the model, and that clear trends of these two parameters exist. Notably, both parameters decreased with the increase in area centroid location. For instance, for steady measurements and on average, the lift curve slope for a wing with an area centroid location at 40% span was 15.6% higher compared to an area centroid location at 60% span. However, the increase in the zero-lift drag coefficient for wings with a lower area centroid location had a detrimental effect on aerodynamic efficiency assessed via glide ratio. Wings with a lower area centroid location consistently led to a lower glide ratio regardless of the change in aspect ratio, thickness-to-chord ratio, or Reynolds number. Increasing the aspect ratio decreased the zero-lift drag coefficient but generally had a slighter increasing effect on the lift curve slope. Increasing the Reynolds number within the range experimented decreased both the lift curve slope and the zero-lift drag coefficient. Finally, the effect of the thickness-to-chord ratio was mainly pronounced in its effect on the zero-lift drag coefficient. Full article