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To address the ill-conditioning of the Jacobian matrix in the geometric error calibration of parallel mechanisms, a Newton method with characteristic value correction (NMCVC) is proposed. This method integrates and enhances the principles of the characteristic value correction iteration method (CVCIM), and Newton method, offering targeted improvements for more effective calibration. First, the damping coefficient is introduced into the CVCIM, and an adaptive strategy for determining the damping coefficient is developed with rigorous proof steps according to the relationship between the condition number and the singular value, which enhances computing efficiency while avoiding the ill-conditioning of the Jacobian matrix. Second, a dynamic adjustment strategy for the CVCIM’s termination condition is designed. This strategy initially estimates the descending direction roughly to approximate the actual descending direction, enhancing computing speed, and then estimates it more accurately at the end of the training stage to obtain precise geometric error values. Finally, by taking a 3RPS parallel mechanism as the instance, three sets of simulation experiments have been designed to test and verify the effectiveness of the algorithm.

Due to the high price of river sand, its shortage and unsustainable extraction from the environment, artificial sand (AS) has been promoted as a fine aggregate for producing concrete. However, it has been acknowledged that a high content of limestone powder (LP), up to 15 wt.%, as a by-product in AS coexists and it has an adverse impact on the mechanical properties of concrete. To compensate for the performance loss of C80 concrete with a high LP content to the applications of concrete on a large scale, this study evaluates the contribution of steel fiber content to the performance improvement of concrete by means of a developed statistical method. Experimental results show that when increasing the LP in concrete over 5%, it can influence axial compression, flexural intensity, splitting tension and the modulus of elasticity, in particular, presenting an obvious decrease in axial compressive intensity, splitting tension and modulus of elasticity. Incorporating steel fibers in such concrete prepared with a high amount of artificial sand powder is a way to compensate for its performance loss. Referring to the experimental results and probability theory, the probability density function of the characteristic value of mechanical characteristic of one type of concrete and the difference between the characteristic values of mechanical characteristics of any two concretes were developed to establish a scientific criterion that can be used to compare the sizes of any two characteristic probability values, which is superior to the comparative approach of arithmetic averages in publications. By adopting this method, the high-strength concrete with a high LP and steel fiber content could be applied in engineering practices from the point of view of its mechanical properties. Meanwhile, the study provides an evaluation method for other scientific research on the size comparison of any two stochastic physical variables.

Masonry walls, the main load-bearing elements, are susceptible to shear damage due to seismic action. Currently, most masonry structures have various degrees of damage and must be strengthened in a timely manner to enhance their performance during earthquakes. This study presents pseudostatic test results for the use of FRCMs to strengthen damaged confined brick masonry walls. Tests were conducted on five walls, and performance indicators were compared for one unreinforced masonry wall and four masonry walls that were damaged and reinforced with various retrofit measures. Reinforcement by FRCM improved the integrity and increased the deformation of damaged walls. The study also examined the damage mechanism, hysteresis characteristics, deformation capacity, and energy dissipation. This study demonstrated the value of using FRCMs to repair damaged masonry walls to improve their seismic performance. The skeletal curves of the walls reinforced on both sides exhibited better postpeak behavior, and the seismic performance indices of the damaged walls strengthened by FRCM were better than those of the unreinforced wall. In accordance with the results of these experiments and previous studies by other scholars, the damage severity and limit state of the FRCM-strengthened walls were determined on the basis of displacement drift. Furthermore, the calculated shear capacities of the FRCM-reinforced walls agreed well with the experimental values.

Over the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

In this paper, we present a calculation method for the radiation response eigenvalue based on a monolithic active pixel sensor. By comparing the statistical eigenvalues of different regions of a pixel array in bright and dark environments, the linear relationship between the statistical eigenvalues obtained by different algorithms and the radiation dose rate was studied. Additionally, a dose rate characterization method based on the analysis of the eigenvalues of the MAPS response signal was proposed. The experimental results show that in the dark background environment, the eigenvalues had a good linear response in the region of any gray value in the range of 10–30. In the color images, due to the difference in the background gray values in adjacent color regions, the radiation response signal in dark regions was confused with the image information in bright regions, resulting in the loss of response signal and affecting the analysis results of the radiation response signal. For the low dose rate radiation field, as the radiation response signal was too weak and there was background dark noise, it was necessary to accumulate frame images to obtain a sufficient response signal. For the intense radiation field, the number of response events in a single image was very high, and only two consecutive frames of image data needed to be accumulated to meet the statistical requirements. The binarization method had a good characterization effect for the radiation at a low dose rate, and the binarization processing and the total gray value statistics of the response data at a high dose rate could better characterize the radiation dose rate. The calibration experiment results show that the binarization processing method can meet the requirements of using a MAPS for wide-range detection.

Systems design methods should aim for systems creating value. The decision-making processes in system engineering struggle to optimize this objective; however, even though the traditional concept of system value as a purely economic metric is recognized as deficient, a well-defined and standard conceptualization of comprehensive system value is still lacking. This study set out to facilitate different stakeholders, involved in developing systems, with a broad perspective on value. We define the system value as the system's holistic impact, encompassing the multi-domain effects on processes, environments, and stakeholders. This inclusive view, to be used by practitioners designing systems and policies, is expected to update existing practices and enhance resulting systems. This paper renders an extensive review of value references in multiple domains, both in system engineering and external, non-engineering, disciplines, and sets the foundation for a revised framing of value in systems engineering. To enable future applications for systems optimization, system value is thoroughly characterized, including its dependency on internal and external factors. This research lays the groundwork for problem formulation of a system value measure, its application in system engineering methods, and further analysis of the subject, both for engineered and non-technical systems.

Bamboo culm has been regarded as a traditional element in construction; meanwhile, it has great potential for the construction of rural houses to achieve green and low-carbon development. However, traditional bamboo houses are usually constructed according to previous experience, and it is hard to design bamboo houses in a standard way. Structural grading of the bamboo culm is an essential work to achieve standardization design. Grading the Moso bamboo culm (P. edulis) based on its minimum external diameter is proposed in this paper. The geometric, physical and mechanical properties of 883 Moso bamboo culms with three different treatment processes were measured and analyzed, namely untreated, with chemical preservatives and heat treatment. It was found that the external diameter of the Moso bamboo culm could be determined by the perimeter in practice. The treatment process has a great influence on the geometric, physical and mechanical properties. Bamboo culms with three different treatment processes could be divided into five, five and four grades, respectively. Meanwhile, based on measurement data, the characteristic values of each grade are presented, including the wall thickness, external and internal taper, linear mass, nominal density and compressive strength. The minimum chemical treatment factor is 0.785, 0.662 and 0.649, while the minimum heat treatment factor is 0.722, 0.644 and 0.877 for wall thickness, linear mass and nominal density, respectively. The treatment factor for compressive strength is 1.12 and 1.52 of chemical treatment and heat treatment, respectively. This study may aid establishing technical specifications and a standard design method for Moso bamboo structural building.

Understanding the dynamic changes of relationships between ecosystem services (ESs) and their dominant factors can effectively adjust human activities to adapt proactively to global climate change. In this study, the Huang-Huai-Hai Plain (HHHP) was selected to assess the dynamics of four key ESs (NPP, net primary productivity; WY, water yield; SC, soil conservation; FP, food production) from 2000 to 2020. The constraint lines of interactions among ESs were extracted based on a segmented quantile regression model. On this basis, the effects of both human activities and natural factors on the key features of the interactions between ESs were quantified with the help of automatic linear model. The results indicated that two types of constraint relationships, including exponential and humped-shaped, existed among the six pairs of ESs. In the past two decades, small changes in NPP thresholds would lead to large variations in other ESs thresholds. Precipitation and normalized difference vegetation index were the key factors to determine the constraint strength of ESs in the HHHP. The potential maximum value of WY in the HHHP could be increased by adjusting landscape shape to make it more complicated. This study helps to improve the potential of target ESs and provides a decision-making basis for promoting regional sustainable development.

In the phenomenological approach, the problem of the possibility of applying the additivity principle to the calculation of physical quantities or characteristics of multicomponent solid metal solutions, including high-entropy ones, is analyzed. These values and characteristics include the lattice parameter and the Debye temperature, since they can be considered indirectly through the radius and mass of an atom as their own, rather than collective, characteristics of individual components, such as, for example, the magnetic moment of an atom, magnetization, heat capacity. It is proposed to consider the relative change in resistance when measuring the thermal coefficient of resistance or the strain coefficient as conditionally individual characteristics of individual components. At the same time, applying the principle of additivity to collective quantities or characteristics (melting temperature, Fermi energy, mean free path of electrons) can give very approximate values.

This study investigates six types of prediction methods for estimating extreme bridge traffic load effects, aiming to establish a correlation between prediction accuracy and data quality. Accurately determining the distribution functions of maximum values is crucial for assessing bridge safety under traffic loads. Methods including the Peaks Over Threshold, the block maxima approach, fitting to a Normal distribution, and the Rice formula based level crossing method, are investigated. Additionally, Bayesian Updating and Predictive Likelihood techniques, integrated with the block maxima approach, are explored. The performance of these methods is assessed using two distinct datasets. The first dataset is generated from a known distribution, allowing the estimated distribution parameters and extreme values derived from each method to be compared with the true values. The analysis is then extended to more realistic scenarios, where long-run simulations provide benchmark results for evaluating the accuracy of each method. Based on the findings, recommendations are provided for selecting the most suitable prediction method, considering factors such as sample size, time interval, and the type of load effect. This work offers practical insights for improving the reliability of extreme value prediction methods in bridge safety assessments.