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Since the introduction of differential evolution (DE) algorithms, they have achieved remarkable success in the field of evolutionary algorithms and engineering applications. In single-objective DE algorithms, most researchers tend to focus on improving mutation operators and parameter control, while overlooking the study of selection operators. However, the study of selection operators still holds great potential in enhancing the performance of DE algorithms. This study proposes a fitness-distance-based selection (FDS) strategy and a new scaling factor control method. FDS is divided into two stages. The first stage is to determine whether an individual needs to accept discarded trial vectors. The second stage involves selectively accepting these discarded trial vectors, which is based on the information related to the discarded trial vectors and the corresponding target vector. A new setting for the scaling factor parameter is proposed, designed to more effectively assist FDS in enhancing algorithm performance. Based on these strategies, an improved variant of the DE algorithm, called fitness-distance-based DE (FDDE) algorithm, is further proposed by this study. To verify the performance of FDDE, we conducted an in-depth study comparing it with six other advanced DE variants and four famous evolutionary algorithms using the CEC 2017, CEC 2022, and CEC 2011 benchmark sets. The experimental results demonstrate that the FDS strategy and the new scaling factor can significantly improve the performance of DE algorithms, and FDDE is significantly better than other advanced algorithm. [ABSTRACT FROM AUTHOR]

In ridge regression, we are often concerned with acquiring ridge estimators that lead to the smallest mean square error (MSE). In this article, we have considered the problem of ridge estimation in the presence of multicollinearity and heteroscedasticity. We have introduced a scaling factor which leads to significantly improved performance of the ridge estimators as compared to their classical counterparts. For illustration purposes, we have applied our proposed methodology to some of the popular existing ridge estimators but it can be extended to other estimators as well. We have also compared our proposed estimator with popular existing estimators dealing estimation problem in the same scenario. Extensive simulations reveal the suitability of the proposed strategy, particularly in the presence of severe multicollinearity and heteroscedasticity. A real-life application highlights that the proposed strategy has the potential to be a useful tool for data analysis in the case of collinear predictors and heteroscedastic errors. [ABSTRACT FROM AUTHOR]

AbstractIn order to ease the computational burden associated with designing irradiation experiments in the Advanced Test Reactor (ATR), scaling factors are often used to estimate design parameters at different lobe powers. This paper examines the validity of long-standing assumptions about the contribution of lobe power to total experiment heating in the ATR. For each of the ATR’s 77 different experiment positions, the fractional contribution of each of the ATR’s five lobes to the total heating in that position is calculated and compared to traditional assumptions. The updated fractional contributions are then used to scale heating rates in a sample problem, and the results are compared to traditional scaling methods as well as explicit MC21 heating calculations.It is concluded that for experiment locations in close proximity to the ATR driver fuel (i.e. flux traps and the A, H, and B positions), heating rates scaled with the updated fractional contributions generally agree better with explicit MC21 calculations than do heating rates scaled using the traditionally assumed contributions. For the I positions, which are located on the very periphery of the ATR core, both scaling methods led to poor results when compared against explicit calculations due to the effect that movement of the outer shim control cylinders has on the experiment heating in those positions. [ABSTRACT FROM AUTHOR]

In this paper, a bi-directional sliding window decoder is proposed for spatially coupled low-density parity-check (SC-LDPC) codes, which improves the decoding complexity and performance compared to the conventional sliding window decoding (SWD) by sharing messages at the overlapped part of forward and backward decoding windows. Moreover, by using proper scaling factors that determine the weight of each message at the overlapped part of two sliding windows, good local decoding effects can be efficiently spread out to both ends of SC-LDPC code during decoding process. Such effective message updates of the proposed bi-directional overlapped sliding window decoding (BO-SWD) improve error floor performance compared to the conventional SWD. The validity of BO-SWD is verified by simulation with various SC-LDPC ensembles.

Abstract Background Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Results Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue’s component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. Conclusions We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

The influence of grating filling factor on the spectral sensitivity of sensors, characterized by full width at half maximum (FWHM) and a figure of merit (FOM), operating on the principle of waveguide mode resonance in dielectric gratings formed on a substrate, has been investigated. Resonant wavelengths were determined for varying grating thicknesses and filling factors. Sensitivity values were obtained for normally and obliquely incident plane waves, as well as for Gaussian beams. Additionally, a scaling rule is presented, allowing determination of sensor parameters using a calculator based on identical grating parameters and corresponding resonant wavelengths. This method holds when linear dimensions of the grating and tested wavelengths are scaled proportionally. [ABSTRACT FROM AUTHOR]

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model's focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model's accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy ( m A P 0.5 ) and ( m A P 0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. [ABSTRACT FROM AUTHOR]

Origami exhibits the remarkable ability to transform into diverse shapes, including quadrilaterals, triangles, and more complex polygons. This unique property has inspired the integration of origami principles into engineering design, particularly in the development of foldable mechanisms. In the field of robotics, when combined with actuators, these foldable mechanisms are referred to as active origami. Origami-based mechanisms play a pivotal role as versatile end effectors or grippers, enabling them to accurately trace desired trajectories. The performance of these mechanisms heavily relies on their specific fold patterns. To shed light on their capabilities, this study focuses on five representative structures using spherical mechanisms: oriceps, Miura ori, MACIOR, and two hexagonal structures. To assess their potential, a comparative analysis is conducted, evaluating their kinematic and scaling performances. The analysis employs the "scaling factor" as a metric, which quantifies the mechanical advantage of these mechanisms. This metric aids in the selection of appropriate structures for various applications. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the relationship among hamstring peak force (PF), rate of force development scaling factor (RFD-SF), countermovement jump (CMJ), and one-repetition maximum (1RM) strength in recreationally trained individuals, as well as to establish the feasibility of RFD-SD assessment for knee flexor muscles. Eighteen volunteers (12 males, 27.3±5.2 years, 6 females, 24.4±3.1 years) participated in the study. Participants performed a knee flexion maximal isometric voluntary contraction and followed a standard RFD-SF protocol. The 1RM for the deadlift was assessed to determine maximal dynamic strength, while CMJ was used to evaluate explosive movement capability. The study found no significant correlations between RFD-SF, CMJ, and 1RM. Additionally, it was observed that RFD-SF in the hamstring muscles can be effectively assessed (mean R2=0.92). In addition, RFD-SF was not different between men and women, which highlight its potential as sex-independent measure of knee flexor muscle function. Future research should involve a more diverse group of athletes to further investigate the relationships among RFD-SF, strength, and explosive movements like CMJ for more comprehensive insights. [ABSTRACT FROM AUTHOR]

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model's accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts. [ABSTRACT FROM AUTHOR]

Estimating the forming limit diagram (FLD) is tedious and cost-intensive. Methods driven by data and artificial intelligence are used to determine the relationship between scaled thickness and the forming rates of various cups drawn out of ETP copper sheets. Machine learning (ML) techniques have a good chance of predicting the FLD of copper alloys, and they are being used increasingly in sensitive electronic and structural applications. The current research aims to create ML-based artificial neural network (ANN) tools to model the relationship between scaled thickness and forming rates as a function of formability. The FLD is measured for copper strips of initial dimensions of 1500 mm long, 750 mm wide, and 6 mm thick, whose thickness was reduced successively by 50% in nine incremental steps. Thus, 3, 1.5, 0.75, 0.38, and 0.19 mm sheets were obtained and used to determine FLD through the Nakajima approach. An FEA model of the drawing was made in Altair Inspire Form, and the simulation results were used to train a two-step ML. A Bayesian regularization (BR) and Levenberg-Marquardt algorithm (LM) were used in the first step to predict strains' maximum and minimum points. In the second step, the minor strains predicted in the first step are used as inputs. Using the same feature set, the BR and LM algorithms predict the major strain, showing a linear trend until the middle and then a nonlinear trend. The trained ML model was used to predict unknown intermediate values for estimating the over-learning and over-fitting problems here for 2 and 0.25 mm thick sheets and are validated experimentally. The variation between the FLDs of predicted and experimentally verified data falls between 2% and 5%. Such small changes in the FLD values show that the proposed ML model could be used to predict the FLDs of copper strips. [ABSTRACT FROM AUTHOR]

Predicting the results of multi-stage deep drawing in determining the thickness distribution and thinning of the workpiece are measures that will help reduce production costs by saving materials and production time. Machine Learning (ML) technique is a promising method for predicting the thickness distribution (TD) of drawn copper metal. Macro and micro products are currently used in sensitive electronic and structural applications. In this study, ML defines the relationship between the scaled thickness and the respective number of stages governed by the scaling law. It includes the development of Artificial Neural Network (ANN) tools based on machine learning to model the relationship between thickness by scaling law and stages based on multi-stage deep-drawn cups. TD is a measure of consistency. The TD is measured from the initial blank of 1500 mm long, 750 mm wide, and 6 mm thick Cu strip, reduced by 50% in nine successive steps until a final thickness of 0.1875 mm. Two ANN models used for TD prediction are Bayesian regularization (BR) and Levenberg-Marquardt (LM) algorithms. A trained machine learning model can successfully predict and verify the unseen data of 1.5 and 0.38 mm TD. ANN is used to predict the Finite Element Analysis (FEA) results and confirm them through the experimental results. The developed model can predict the TD of the multi-stage cup with the die design parameters. The difference between the TD predicted value and the measured value is based on the simulation results of multi-stage cups using the finite element method. When the predicted and measured TD, the difference in cup drawing depth is 0.5%–2.0%. The results show that the LM model is suitable for predicting the TD of formed copper cups following the scaling law. [ABSTRACT FROM AUTHOR]

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Deep neural network (DNN) has achieved great success in various fields. Due to its high computing and storage costs, it is difficult to directly deploy them to resource constrained mobile devices. To solve this problem, the importance evaluation of the global filter in the network is studied, and a channel pruning compression method with filter elasticity is proposed to reduce the size of the neural network. Firstly, the method sets the local dynamic threshold between layers to improve the shortage of over pruning in L1 regularization (L1 lasso) sparse training. Then, its output is multiplied by the channel scaling factor to replace the ordinary convolution layer module. The importance of the global filter is defined by the elastic size of the filter. Its values are estimated and ranked by Taylor formula. At the same time, a new iterative pruning framework of the filter is designed to balance the contradiction between the pruning performance and the pruning speed. Finally, the improved L1 regularization training and the importance of the global filter are used to prune the composite channels. VGG-16 is tested on CIFAR-10 using the proposed method, which reduces 80.2% of floating-point operations (FLOPs) and 97.0% of parameter quantities, without significant loss of accuracy, indicating the effectiveness of the method, which can compress neural networks in a large scale, and can be deployed to resource constrained terminal devices. [ABSTRACT FROM AUTHOR]

One of the crucial and challenging issues for researchers is presenting an appropriate approach to evaluate the aerodynamic characteristics of air cushion vehicles (ACVs) in terms of system design parameters. One of these issues includes introducing a suitable approach to analyze the effect of geometric shapes on the aerodynamic characteristics of ACVs. The main novelty of this paper lies in presenting an innovative method to study the geometric shape effect on air cushion lift force, which has not been investigated thus far. Moreover, this paper introduces a new approximate mathematical formula for calculating the air cushion lift force in terms of parameters, including the air gap, lateral gaps, air inlet velocity, and scaling factor for the first time. Thus, we calculate the aerodynamic lift force applied to nine different shapes of the air cushions used in the ACVs in the present paper through the ANSYS Fluent software. The geometrical shapes studied in this paper are rectangular, square, equilateral triangle, circular, elliptic shapes, and four other combined shapes, including circle-rectangle, circle-square, hexagonal, and fillet square. Results showed that the cushion with a circular pattern produces the highest lift force among other geometric shapes with the same conditions. The increase in the cushion lift force can be attributed to the fillet with a square shape and its increasing radius compared with the square shape. [ABSTRACT FROM AUTHOR]

The inadequacy of conventional control strategies for multi-phase interleaved parallel circuits in terms of adaptive adjustment makes it difficult to meet the power source design requirements for transient electromagnetic detection systems. This paper introduces a novel approach, the variable-domain fuzzy proportional–integral (PI) adaptive control strategy. This strategy dynamically adjusts the fuzzy domain in real time based on the input error magnitude, ensuring improved control effectiveness. By leveraging the benefits of both the functional scaling factor and the fuzzy reasoning scaling factor, we design scaling factors for the input and output domains to enhance control precision. The focus of this study is on a four-phase interleaved parallel converter, emphasizing the design of the variable-domain fuzzy PI control strategy for the voltage outer loop within the traditional dual closed-loop structure. An experimental prototype with a 220 Vac input, 380 V output, and a power rating of 1000 W is constructed. A comparative analysis between fuzzy control and variable-domain fuzzy PI control is conducted in the voltage outer loop of the dual closed-loop control. Results reveal that dual closed-loop control with variable-domain fuzzy PI control for the voltage outer loop significantly enhances system stability. The startup time to reach the steady state is reduced to 0.632 s, with an overshoot of 28.8 V. Transitioning from 25% load to full load takes only 0.096 s, resulting in a minimal drop of 21.4 V and an overshoot of 13.4 V. Similarly, switching from full load to 25% load in 0.167 s exhibits an overshoot of only 19.6 V. The adaptive regulation capability of the converter is markedly improved, showcasing smaller overshoots and higher-level controlling effectiveness. [ABSTRACT FROM AUTHOR]

A variable universe fuzzy proportional‐integral‐differential (PID) algorithm was designed and first application for a laser power stabilization system. To validate the algorithm's effectiveness, it is applied to the simulation model of Simulink and the experimental system based on an acousto‐optic modulator. The results demonstrate that the variable universe fuzzy PID presents higher control precision, better robustness, and can suppress long‐term drift phenomena compared with the traditional feedback control algorithm. The algorithm design method in this paper has significant reference and guidance value for laser power stabilization system. [ABSTRACT FROM AUTHOR]

Basin presence is believed to affect the ground surface response due to earthquakes, particularly in areas around the basin edge. Previous studies showed that 1-D and 2-D wave propagation analyses resulted in significant differences in amplification at the basin edge. However, the link between 1-D and 2-D responses has not been studied for engineering practices. In practical application, seismic studies were commonly performed using 1-D analysis, for example, to develop a city micro-zonation map. Based on practical considerations, it is necessary to estimate the scaling factor for the 1-D analysis by considering the basin presence, particularly for one containing soft soil. There are three stages carried out in this study. The first stage: collecting data on some basin geometries for the 2-D modeling references and then defining selected site class and input motions. The second stage: modeling 1-D and 2-D wave propagation using D-MOD and Fast Lagrangian Analysis of Continua (FLAC), respectively. The third stage: comparing spectral acceleration resulting from the 1-D and 2-D analyses to obtain the scaling factors. This research studied and reported the relationship between PGA values varied as 0.2 g, 0.3 g, 0.4 g, and 0.5 g, basin geometry (e.g., the angle was set to 5°, 10°, 15°, 30°, and 45°, with depth and width variations of 0.0125, 0.025, 0.05, 0.075, 0.1, 0.2, and 0.4, while the basin width was adjusted to 500 m, 1 km, 2 km, and 4 km), and the spectral acceleration in several observation points on the ground surface. Based on this evaluation, a series of scaling factors are proposed. These factors can be used for spectral acceleration from available hazard maps, commonly developed based on 1-D analysis. The application example of this scaling factor is presented in this study, using the Bandung Basin case. [ABSTRACT FROM AUTHOR]

During the electric power system (EPS) modelling with massive use of distributed energy resources (DERs) - distributed generation (DG), storage and other distributed technologies such as electric vehicles - simplified and ideal conditions are assumed for the active distribution network. From the grid side, these elements are modelled as absorption and injection of power and/or current. In this paper, using the model MV-Benchmarck System CIGRE Task Force C6.04, a comparative analytical straightforward algorithm of convergence limits on load flow based on sum of powers and sum of currents along the topological matrix has been simulated. The convergence sensitivity analysis was examined for 3 system characteristics: radial and meshed Configuration, DG penetration and R/X ratio, finding percentage differences of up to 6% convergence sensitivity by power hosting capacity between two-non-linearmethods used for load flow. [ABSTRACT FROM AUTHOR]

There is inevitably a performance deviation between an engine model and an actual engine that is influenced by unpredictable factors such as the unsuspected environmental conditions and the natural performance degradation in the process of use. Because the engine model precision largely depends on the accuracies of the component maps, it is possible to revise the engine model to determine a better trend for the engine performance from recorded measurements by adjusting the maps. This paper presents a new method for updating the variable geometry component maps of a variable cycle engine (VCE) by using a set of scaling factors estimated with the cubature Kalman filter (CKF). A mapping function is created between the scaling factors and the component characteristic scaling coefficients for the adjustments of the maps. The proposed method is applied to a VCE model according to the VCE benchmark steady-state performance data. The results show that the maximum simulation error of the engine steady-state model decreases from 5.33 to 0.93%, and the CKF-based adaptation method provides a much faster computing rate than the particle swarm optimization (PSO) based adaptation method, which verifies the effectiveness and engineering applicability of the variable geometry characteristic adaptive correction method. [ABSTRACT FROM AUTHOR]

Origami exhibits the remarkable ability to transform into diverse shapes, including quadrilaterals, triangles, and more complex polygons. This unique property has inspired the integration of origami principles into engineering design, particularly in the development of foldable mechanisms. In the field of robotics, when combined with actuators, these foldable mechanisms are referred to as active origami. Origami-based mechanisms play a pivotal role as versatile end effectors or grippers, enabling them to accurately trace desired trajectories. The performance of these mechanisms heavily relies on their specific fold patterns. To shed light on their capabilities, this study focuses on five representative structures using spherical mechanisms: oriceps, Miura ori, MACIOR, and two hexagonal structures. To assess their potential, a comparative analysis is conducted, evaluating their kinematic and scaling performances. The analysis employs the “scaling factor” as a metric, which quantifies the mechanical advantage of these mechanisms. This metric aids in the selection of appropriate structures for various applications.

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model’s focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model’s accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy (mAP0.5) and (mAP0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images.

This paper deals with the scaling factor's effect on the cantilever beam's natural frequency. Scaling a prototype dimension will make it easier to manufacture and test the dynamic characteristics of structures with enormous sizes. Natural frequency is one of the essential parameters of the vibration characteristics of the system. The phenomenon that often occurs in vibrating structures is the resonance phenomenon. Resonance is a system state in which an abnormally large vibration is generated in response to an external stimulus, happening when the frequency of the disturbance is equal to, or nearly identical to, the natural frequency of the system. This research investigates the scale factor on natural frequency using the analytical method using Euler-Bernoulli beam theory, the MSC Nastran/Patran software, and experimental testing using an impact hammer on a cantilever beam. The three methods were applied to beams with four scaling variations: 1:1, 1:2.04, 1:2.85, and 1:3.48. Based on the results, the natural frequencies increase by the size scale reduction value in the modeling. The natural frequency value in the actual structure has a value equivalent to 1/S of the natural frequency value of the scale, whereas S is defined as the downgrade scale value. This result follows the Similitude theory.

During the electric power system (EPS) modelling with massive use of distributed energy resources (DERs) - distributed generation (DG), storage and other distributed technologies such as electric vehicles - simplified and ideal conditions are assumed for the active distribution network. From the grid side, these elements are modelled as absorption and injection of power and/or current. In this paper, using the model MV-Benchmarck System CIGRE Task Force C6.04, a comparative analytical straightforward algorithm of convergence limits on load flow based on sum of powers and sum of currents along the topological matrix has been simulated. The convergence sensitivity analysis was examined for 3 system characteristics: radial and meshed Configuration, DG penetration and R/X ratio, finding percentage differences of up to 6% convergence sensitivity by power hosting capacity between two -non-linear- methods used for load flow.

Basin presence is believed to affect the ground surface response due to earthquakes, particularly in areas around the basin edge. Previous studies showed that 1-D and 2-D wave propagation analyses resulted in significant differences in amplification at the basin edge. However, the link between 1-D and 2-D responses has not been studied for engineering practices. In practical application, seismic studies were commonly performed using 1-D analysis, for example, to develop a city micro-zonation map. Based on practical considerations, it is necessary to estimate the scaling factor for the 1-D analysis by considering the basin presence, particularly for one containing soft soil. There are three stages carried out in this study. The first stage: collecting data on some basin geometries for the 2-D modeling references and then defining selected site class and input motions. The second stage: modeling 1-D and 2-D wave propagation using D-MOD and Fast Lagrangian Analysis of Continua (FLAC), respectively. The third stage: comparing spectral acceleration resulting from the 1-D and 2-D analyses to obtain the scaling factors. This research studied and reported the relationship between PGA values varied as 0.2 g, 0.3 g, 0.4 g, and 0.5 g, basin geometry (e.g., the angle was set to 5°, 10°, 15°, 30°, and 45°, with depth and width variations of 0.0125, 0.025, 0.05, 0.075, 0.1, 0.2, and 0.4, while the basin width was adjusted to 500 m, 1 km, 2 km, and 4 km), and the spectral acceleration in several observation points on the ground surface. Based on this evaluation, a series of scaling factors are proposed. These factors can be used for spectral acceleration from available hazard maps, commonly developed based on 1-D analysis. The application example of this scaling factor is presented in this study, using the Bandung Basin case.

Traditionally, sensitivity analysis has been utilized to determine the importance of input variables to a deep neural network (DNN). However, the quantification of sensitivity for each neuron in a network presents a significant challenge. In this article, a selective method for calculating neuron sensitivity in layers of neurons concerning network output is proposed. This approach incorporates scaling factors that facilitate the evaluation and comparison of neuron importance. Additionally, a hierarchical multi-scale optimization framework is proposed, where layers with high-importance neurons are selectively optimized. Unlike the traditional backpropagation method that optimizes the whole network at once, this alternative approach focuses on optimizing the more important layers. This paper provides fundamental theoretical analysis and motivating case study results for the proposed neural network treatment. The framework is shown to be effective in network optimization when applied to simulated and UCI Machine Learning Repository datasets. This alternative training generates local minima close to or even better than those obtained with the backpropagation method, utilizing the same starting points for comparative purposes within a multi-start optimization procedure. Moreover, the proposed approach is observed to be more efficient for large-scale DNNs. These results validate the proposed algorithmic framework as a rigorous and robust new optimization methodology for training (fitting) neural networks to input/output data series of any given system. [ABSTRACT FROM AUTHOR]

In teleoperation, it is important to utilize the large workspace of a slave robot as well as to guarantee the execution accuracy in sophisticated work. However, the difference between the workspaces of the master device and the slave robot makes the teleoperational mission more difficult. To solve this problem, this study develops a new method for determining the scale factor of workspace mapping based on both 1) the impedance between the slave robot and object and 2) the moving distance of the master device. The object feature and information on the working environment collected from an RGB-D camera in the slave robot are used to set the scaling factor for collision avoidance. Furthermore, application of the movable distance of the master device into the determination of the scale factor helps to improve work efficiency. The fuzzy rule in this study plays a role in determining the scaling coefficient by considering two different pieces of information. Two experimental tests show the effectiveness of the proposed method in both work efficiency and execution accuracy. [ABSTRACT FROM AUTHOR]

Including active renal excretion in physiologically based kinetic (PBK) models can improve their use in quantitative in vitro- in vivo extrapolation (QIVIVE) as a new approach methodology (NAM) for predicting the acute toxicity of organic cation transporter 2 (OCT2) substrates like paraquat (PQ). To realise this NAM, kinetic parameters Vmax and Km for in vitro OCT2 transport of PQ were obtained from the literature. Appropriate scaling factors were applied to translate the in vitro Vmax to an in vivo Vmax. in vitro cytotoxicity data were defined in the rat RLE-6TN and L2 cell lines and the human A549 cell line. The developed PQ PBK model was used to apply reverse dosimetry for QIVIVE translating the in vitro cytotoxicity concentration-response curves to predicted in vivo toxicity dose-response curves after which the lower and upper bound benchmark dose (BMD) for 50% lethality (BMDL 50 and BMDU 50) were derived by applying BMD analysis. Comparing the predictions to the in vivo reported LD 50 values resulted in a conservative prediction for rat and a comparable prediction for human showing proof of principle on the inclusion of active renal excretion and prediction of PQ acute toxicity for the developed NAM. [Display omitted] • A new approach methodology (NAM) predicting acute paraquat toxicity was defined. • This NAM included a newly developed PBK model with active renal excretion via OCT2. • Cytotoxicity data in lung epithelial cell lines were used for QIVIVE. • QIVIVE was based on PBK model based reverse dosimetry. • The in vivo dose response curves adequately predicted acute paraquat toxicity. [ABSTRACT FROM AUTHOR]

In gravity inversion, traditional inversion methods usually generate smooth inversion results, that is, there are no obvious boundaries between different geological units. Fuzzy C-Means (FCM) algorithm is introduced into the inversion to solve the problem mentioned above to improve the accuracy and spatial resolution of inversion results. However, when the volume of an anomalous body is much smaller than that of the surrounding rock, and the weight coefficient of the FCM clustering term in the objective function is not selected properly, the algorithm is prone to cause uniform shrinkage of the anomaly inversion results, resulting in lower inversion accuracy, or even failure of the inversion.The main reason for the inversion failure is usually because the total volume of the anomalous bodies is much smaller than the volume of the surrounding rock.For this reason, in this paper, the scaling factor is introduced into the FCM clustering term of the objective function to balance the membership degree of the model parameters to each cluster, so as to reduce the influence of small anomalous body volume compared with the surrounding rock volume. By establishing a simple positive correlation between the scaling exponent ek and the distance snormal from the normalized clustering center and the real clustering center, the scaling factor ρk is continuously updated during the inversion process, which significantly reduces the difficulty in selecting the weight coefficient of the FCM clustering term in the objective function, and avoids the problem of volume shrinkage of the inverted anomalous bodies, thus enhancing the stability of the inversion. The numerical experiments of inversion with theoretical gravity anomaly data and actual data inversion show that the improved algorithm has higher inversion stability and accuracy compared with the previous FCM method.

Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model’s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts.

This paper deals with scale-fullscale models of rotor-bearing systems on rotating condition. The investigation is focused on the gyroscopic effect, which causes forward and backward whirl frequencies. When the rotor-bearing system is scaled proportionally in its dimension (height, length, and width), the scaling factor of whirl frequencies can be derived. It depends on the ratio between a transverse and polar moment of inertia on its rotating axis called the gyroscopic factor. The experimental study is conducted to validate it on three scaled rotor-bearing systems, which shifts its disc from the middle of the shaft on the scale of 1:1, 2:1, and 3:1 to clearly show the gyroscopic effect on first bending natural frequency. The scaling factor is then validated using the Campbell diagram by finding its critical speed. From this critical speed, the whirl frequencies along the range of the full-scale model speed can be obtained. The result also shows that the scaling factor remains the same whether it is at rotation or rest condition. Consideration must be made on the effect of the structural design, that is, blade and support, because of its unsymmetric stiffness that can cause backward whirl frequencies. The bearing stiffness must be ensured to be scaled proportionally, especially on journal bearing cases. This finding can be used by engineers to deal with scaling method implementation on rotating machinery design. [ABSTRACT FROM AUTHOR]

The skeleton is a secretory organ, and the goal of some osteoporosis therapies is to maximize bone matrix output. Nmp4 encodes a novel transcription factor that regulates bone cell secretion as part of its functional repertoire. Loss of Nmp4 enhances bone response to osteoanabolic therapy, in part, by increasing the production and delivery of bone matrix. Nmp4 shares traits with scaling factors, which are transcription factors that influence the expression of hundreds of genes to govern proteome allocation for establishing secretory cell infrastructure and capacity. Nmp4 is expressed in all tissues and while global loss of this gene leads to no overt baseline phenotype, deletion of Nmp4 has broad tissue effects in mice challenged with certain stressors. In addition to an enhanced response to osteoporosis therapies, Nmp4-deficient mice are less sensitive to high fat diet-induced weight gain and insulin resistance, exhibit a reduced disease severity in response to influenza A virus (IAV) infection, and resist the development of some forms of rheumatoid arthritis. In this review, we present the current understanding of the mechanisms underlying Nmp4 regulation of the skeletal response to osteoanabolics, and we discuss how this unique gene contributes to the diverse phenotypes among different tissues and stresses. An emerging theme is that Nmp4 is important for the infrastructure and capacity of secretory cells that are critical for health and disease. [ABSTRACT FROM AUTHOR]

Metamodels are popular approaches to improve design efficiency in practical-engineering problems through replacing the time-consuming simulation models with easy-to-evaluate numerical models. To improve the generalization performance of the radial basis function (RBF) metamodel, this work sets up an anisotropic radial basis function metamodeling algorithm enhanced by recursive evolution Latin hypercube design and fast K-fold cross-validation method. First, the recursive evolution Latin hypercube design method splits the large-sample design into several small-sample designs to reduce computation cost and naturally splits the training samples into several folds for K-fold cross-validation. Then, the training samples are utilized to build the anisotropic RBF model, where the sensibility of model to each dimension is considered. In anisotropic RBF method, the optimization of hyperparameters in RBF model is transformed to anisotropic-scaling factors to improve the performance and computational efficiency. Afterwards, the evolutionary algorithm and fast K-fold cross-validation method are employed to optimize the anisotropic-scaling factors, and thus, the final metamodel is constructed. Finally, several numerical functions and an engineering case of cylindrical stiffened shell performance prediction are conducted to test the proposed enhanced anisotropic RBF metamodel. Results indicate that the proposed method is competitive compared with other popular metamodel methods and state-of-the-art multi-width RBF modeling methods, and can effectively deal with practical-engineering problems. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the constant speed control strategy of the aquaculture water pump equipped by a large aquaculture worker ship will cause a lot of power waste and increase the aquaculture cost, a Whale Optimization Algorithm(WOA)Fuzzy PID controller for the aquaculture water pump is designed. The whale algorithm is used to optimize the quantitative factor and scaling factor in the Fuzzy PID controller,which makes the controller can dynamically adjust the speed of the aquaculture pump for the complex and changeable conditions of water changing,feeding,cleaning,and surface foam discharge,so as to realize the regulation of seawater flow. Simulation results show that compared with the PID controller and Fuzzy PID controller,the overshoot and steady-state error are smaller. The adjustment time is shortened by 73. 14% compared with the PID controller and 84. 49% compared with the Fuzzy PID controller,which can better meet the control requirements of the aquaculture water pump and achieve the goal of energy saving. [ABSTRACT FROM AUTHOR]

As a stochastic search algorithm, the seagull optimization algorithm (SOA) has significant optimization performance, but it still has the problem of low population diversity and easy to fall into local optimum which resulting in low optimization accuracy. This paper proposes a improving seagull optimization algorithm combine multiple strategies (CMSOA) to improve the defects of the seagull optimization algorithm. Firstly, using the sine-cosine algorithm (SCA) to perturb and update the stagnant seagull population in the iterative process, which improved the diversity of the overall population; Secondly, introducing a scaling factor to adjust dynamically the relative displacement between the current seagull individual and the optimal individual, which improved the exploration and development ability of the algorithm; Finally, adopting Random opposition-based learning to fine-tune the optimal individual position of the seagull, which leaded the entire seagull move to the correct position in the given search space to improve the ability to jump out of the local optimum, and further increasing the optimization accuracy. In order to test the optimization performance of the improved CMSOA algorithm, this paper uses 14 CEC2017 test functions as the test benchmark to test the performance of CMSOA and others comparison algorithm. The experiments show that CMSOA has the advantage of optimization in the statistical sense based on the Freidman test. CMSOA has also achieved the best results in the path planning of 3 D UAV. [ABSTRACT FROM AUTHOR]

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CLint) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SFlin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CLint. These SFs contain both SFlin and an exponential SF (SFβ) of fraction unbound in plasma (fu,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CLint, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CLint. This observation is different than in rats, where rat liver microsomal CLint correlates well with in vivo CLint for compounds demonstrating permeability-limited metabolism. The good CLint IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure–activity relationships to improve metabolic stability. [ABSTRACT FROM AUTHOR]

Seventy-two infiltration tests were conducted to model soil water infiltration variability using scaling in the first and third terraces of Yangling District, respectively. The relationship between the scaling factor of the Kostiakov–Lewis equation and soil properties for multiple scales was determined through two-dimensional continuous wavelet transform (2D-CWT) and path analyses, and the pedotransfer functions (PTFs) based on the support vector machine (SVM) and back propagation (BP) neural network were developed for estimating the scaling factor. The results indicated that when scaling factor was calculated using the least square method (FS), the best match was achieved between the predicted infiltration and measured values. In the first and third terraces, the results of 2D-CWT and path analyses indicated that FS were positively correlated with sand content, silt content, and soil organic matter content. However, FS was negatively correlated with bulk density in the first terrace, and which was negatively correlated with clay content and initial water content in the third terrace. The accuracy of SVM-based PTF is higher than BP-based PTF, the mean absolute value of relative errors of SVM-based PTF were 11.3% and 8.16% for the first and third terrace, respectively. Therefore, the SVM is preferred for PTF development. [ABSTRACT FROM AUTHOR]

To evaluate AERMOD for predicting livestock odour dispersion in the Canadian climate and flat terrain, 22 odour plumes were measured around a broiler barn for two days with distinct atmospheric stability classes, namely Aug 31st with C & D and Sep 2nd with A & B. The diurnally varying odour emission rates were measured and input for the modelling. The relationship between odour concentration (OC) and intensity (OI) previously established for this broiler barn was utilised to convert the measured OI to OC or modelled OC to OI. For comparing the hourly modelled predictions and 10-min field measurements, two peak-to-mean ratios (PTM) including a constant factor of 4 (PTM C) and a variable factor dependent on the atmospheric stability (PTM V) were applied. However, neither of them could sufficiently diminish the significance in the paired differences between model predictions and field measurements (p < 0.05). The efficacy of scaling factors specifically developed for the corresponding atmospheric stability classes based on the PTM adjustments were demonstrated, leading to an average OI agreement of 76% for Aug 31st but 97% for Sep 2nd, regardless of the PTM type. The choice of scaling factors could significantly influence the model performance. For Aug 31st, SF Geo was favourable for improving model predictions over short distances (within 300 or 400 m), otherwise SF All should be used for guaranteeing insignificant paired differences. In contrast, SF Geo was consistently preferred for Sep 2nd in terms of improving or at least retaining the acceptable percentage of FB i besides narrowing the paired differences. • AERMOD was assessed for predicting livestock odour dispersion in a cold flat region. • Previously obtained diurnal OE and OC–OI relationship of the broiler barn was used. • Constant or atmospheric stability based PTM was poor in adjusting model accuracy. • Scaling factor should be specifically derived for each atmospheric stability class. • Average OI agreement was 76% for Aug 31st (C & D) but 97% for Sep 2nd (A & B). [ABSTRACT FROM AUTHOR]

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of 10−5. A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations. [ABSTRACT FROM AUTHOR]

Wear on articulating bearing surfaces is a key factor causing revision in total hip replacement (THR). Wear debris that releases particles from bearing surfaces might result in adverse soft tissue reactions requiring revision surgeries. In this study, a comprehensive computational wear model based on the Archard wear equation was performed to investigate the wear performance under a three-dimensional (3D) physiological gait cycle, mimicking a normal walking condition (5 million cycles). The study shows that the accuracy of the model is highly dependent on the mesh convergence, the wear fraction, and the scaling factor. The simulations were run to provide a vast amount of detail for the reproducibility of the work. Cobalt chromium (CoCr) on cross-linked polyethylene (XLPE) and CoCr on carbon-fiber-reinforced polyether ether ketone (CFR-PEEK) prototype models were created in silico. The volumetric wear rates for CoCr-on-XLPE were calculated as 0.2989 mm 3 / year for CoCr head and 21.0271 mm 3 / year for XLPE liner, while for CoCr-on-CFR-PEEK they were 0.3484 mm 3 / year for CoCr head and 1.8476 mm 3 / year for CFR-PEEK liner. When compared to in vivo and in vitro studies, the wear patterns of these two prototypes are consistent with those of the conventional polyethylene liners in the literature. Although the volumetric wear rate of the CFR-PEEK liner is about 11 times lower than the counterpart of XLPE in MoP implants, the wear rate of CoCr was higher when compared to its use with XLPE. Therefore, CFR-PEEK articulating against orthopa\edic metals may not be as good an alternative as XLPE, due to higher indicative metallic wear. This detailed computational wear modeling methodology could be utilized in design improvements of implants. [ABSTRACT FROM AUTHOR]