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The purpose of this paper is to establish the horizontal displacement angle limit values under different performance level, use damage as the quantitative index of performance level and determine the design principle of the RACFST column for performance-based seismic fortification target based on the damage.

The paper is based on the seismic performance test of the RACFST column.

First, three-level seismic are introduced into the performance design foundation of the RACFST column. Second, the performance level of the RACFST column is divided into five grades: normal use, temporary use, use after repair, life safety and prevention of collapse. Third, the seismic performance targets of RACFST columns are divided into four categories: unacceptable situation, basic performance target, important performance target and special performance target.

The initial damage of the recycled aggregate occurs in the process of crushing and screening, and the damage evolution and development of the RACFST column occur under cyclic load. This is one of the problems that should not be avoided in the design of the seismic performance of the RACFST column. New levels are introduced in the performance design foundation of the RACFST column.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

The purpose of this paper is to evaluate design performance of 51 gear shaping machines by using data envelopment analysis (DEA).

Existing studies extend traditional DEA by handling bounded and discrete data based on envelopment models. However, value judgment is usually neglected and fail to be incorporated in these envelopment models. In many cases, there is a need for prior preferences. Using existing DEA approaches as a backdrop, the current paper presents a methodology for incorporating assurance region (AR) restrictions into DEA with bounded and discrete data, i.e. the assurance region bounded discrete (AR-BD) DEA model. Then, the AR-BD DEA model is combined with a context-dependent DEA to obtain an efficiency stratification.

The authors examine different AR restrictions and calculate efficiency scores of five scenarios of AR restrictions by using the proposed AR-BD DEA model. It shows that AR restrictions have a great impact on the efficiency scores. The authors also identify nine efficient frontiers in total. For each decision-making unit, it could set benchmarks and improve its performance based on each higher efficient frontier.

This paper first evaluates efficiency of gear shaping machines by considering different (bounded and discrete) variable types of data and including AR restrictions. The AR-BD DEA model and context-dependent AR-BD DEA model proposed in this paper further enrich the DEA theory. The findings in this paper certainly provide useful information for both producers and consumers to make smart decisions.

This paper aims to investigate how to improve new product performance in turbulent circumstances of emerging economies.

This paper used regression analysis to examine the performance impact of customer relationship management (CRM) and product development management (PDM) concentration strategy in new product development (NPD). A detailed contingent analysis of the market and institutional environments in emerging economies is also conducted based on a survey of 114 Chinese high-tech manufacturers.

The research findings show that PDM has a stronger positive effect on new product performance than CRM in emerging economies and that the contingent effects of the market and institutional environment vary. More specifically, technological turbulence and enforcement inefficiency can positively moderate the relationship between CRM and new product performance, whereas the moderating effect of market turbulence on CRM is negative. Meanwhile, enforcement inefficiency negatively moderates the effect of PDM on new product performance, while the moderating effect of market turbulence on PDM is positive.

This paper is limited to a survey of high-tech manufacturing enterprises in China. Further research should continues to explore and document the strategic issue about NPD in emerging economies by longitudinal study.

This paper contributed to theoretical and practical initiatives on the strategic issue of NPD and provided firms a further understanding of how to select the right NPD strategy in emerging economies to improve new product performance.

The purpose of this paper is to predict a suitable paper substrate which has high capillary pressure with the tendency of subsequent fluid wrenching in onward direction for the fabrication of microfluidics device application.

The experiment has been done on the Whatman^TM grade 1, Whatman^TM chromatography and nitrocellulose paper samples which are made by GE Healthcare Life Sciences. The structural characterization of paper samples for surface properties has been done by scanning electron microscope and ImageJ software. Identification of functional groups on the surface of samples has been done by Fourier transform infrared analysis. A finite elemental analysis has also been performed by using the “Multiphase Flow in Porous Media” module of the COMSOL Multiphysics tool which combines Darcy’s law and Phase Transport in Porous Media interface.

Experimentally, it has been concluded that the paper substrate for flexible microfluidic device application must have large number of internal (intra- and interfiber) pores with fewer void spaces (external pores) that have high capillary pressure to propel the fluid in onward direction with narrow paper fiber channel.

Surface structure has a dynamic impact in paper substrate utilization in multiple applications such as paper manufacturing, printing process and microfluidics applications.

With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

The special structure of the vortex pump contributes to its complex internal flow pattern. A type of horizontal 150WX-200-20 vortex pump is taken as a research subject to deeply study the progression and distribution of flow pattern in its channel. To explain the mechanism of flow in this pump, numerical analysis of the whole flow and experiment have been conducted.

The authors studied and analyzed the distribution and evolution of flow pattern under different flow, such as circulating-flow, through-flow and other forms. Finally, a model of flow pattern in the vortex pump has been built, which has more perfectly fit the reality.

They are through-flow affected by circulating-flow, main and subsidiary circulating-flow, vortices between vanes and other vortices (or liquid impingement) in volute. Entering the pump, part of the flow stays in vanes and turn into vortices while the other goes into the front chamber. The flow that runs into the front chamber will be divided into two parts. One part will be collected by viscosity into a vortex rope when it passing through the interface between the impeller and the vaneless chamber, which closely relates to the circulating-flow, and the rest directly goes out of the field through the diffuser. Besides, a fraction of circulating-flow joins the through-flow when it goes through the section V and leaves the pump.

The research results build a theoretical foundation for working out the flow mechanism of the vortex pump, improving its efficiency and optimizing its hydraulic design.

The purpose of this paper is to obtain liquid acrylate oligomers containing carboxyl groups as excellent toughening agents for epoxy resins.

Liquid acrylate oligomers containing carboxyl groups were synthesised by the solution polymerisation of butyl acrylate (BA), acrylic acid (AA) and acrylonitrile (AN) as monomers. The liquid acrylate oligomers were used as the toughening agents for epoxy resins. The chemical structure of the oligomers was characterised by ¹³C nuclear magnetic resonance (NMR) spectroscope. The morphology of modified epoxy networks was analysed by scanning electron microscope (SEM). The mechanical and thermodynamic properties were measured by universal testing machine and dynamic mechanical analyser (DMA).

The results show that AA and oligomer concentrations have great influence on the morphology, mechanical and thermodynamic properties of the modified epoxy networks. When the 10 wt percent oligomer containing BA and AN and AA in the ratio of 75/20/5 is used to modify the epoxy resin, the increase in impact strength of the modified epoxy network is 291.5 percent over the unmodified epoxy network due to addition of the oligomers without a sacrifice in heat‐resistance properties. Fracture surface analysis by SEM indicates the presence of a two‐phase microstructure.

The modified epoxy networks can be used as high performance materials such as adhesives, sealants and matrices of composites.

The liquid acrylate oligomers containing carboxyl and nitrile groups which were synthesised with BA, AA and AN as monomers by the solution polymerisation are novel and can greatly increase the toughness of epoxy resins without loss of thermal resistance.

In practical engineering, oil filters often work under asymmetric cyclic loading. In order to improve the prediction accuracy of fatigue life of the oil filters under asymmetric cyclic loading, the effect of strain ratio and low cycle fatigue plastic deformation on fatigue life need to be considered. This paper aims to discuss the aforementioned objective.

First, strain-controlled fatigue tests with strain ratios of 0, 0.5 and −1 were carried out on the oil filter material 2A70-T6 aluminum alloy, and the test data were used to obtain strain fatigue life curves at three strain ratios. Then, based on the idea of the constant life curve method, the average value of the ratio of the strain amplitude corresponding to different strain ratios under the same partial life was defined as the strain ratio factor. Finally, the elastic-plastic factor was modified by the strain ratio factor, and a new fatigue life prediction model considering the effect of strain ratio was proposed.

The proposed model was validated, respectively, by fatigue test data of 2A70-T6 aluminum alloy, 2124-T851 aluminum alloy and oil filter and the results of the proposed model were compared with the Coffin–Manson equation, Morrow model and Smith–Watson–Topper (SWT) model, showing that the proposed model had higher applicability and accuracy.

In this work, a strain ratio factor is established based on the idea of the constant life curve method, and the strain ratio factor is used to modify the introduced elastic-plastic factor, and then a new fatigue life prediction model considering the influence of strain ratio and low cycle fatigue plastic deformation on material fatigue damage accumulation is proposed. The results show that the prediction results of the proposed model are in good agreement with the experimental data, and the proposed model has good fatigue life prediction ability considering the influence of strain ratio and lays a foundation for the fatigue life prediction of the oil filter.