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In general, the existing compressor design methods require abundant knowledge and inspiration. The purpose of this study is to identify an intellectual design optimization method that enables a machine to learn how to design it.

The airfoil design process was solved using the reinforcement learning (RL) method. An intellectual method based on a modified deep deterministic policy gradient (DDPG) algorithm was implemented. The new method was applied to agents to learn the design policy under dynamic constraints. The agents explored the design space with the help of a surrogate model and airfoil parameterization.

The agents successfully learned to design the airfoils. The loss coefficients of a controlled diffusion airfoil improved by 1.25% and 3.23% in the two- and four-dimensional design spaces, respectively. The agents successfully learned to design under various constraints. Additionally, the modified DDPG method was compared with a genetic algorithm optimizer, verifying that the former was one to two orders of magnitude faster in policy searching. The NACA65 airfoil was redesigned to verify the generalization.

It is feasible to consider the compressor design as an RL problem. Trained agents can determine and record the design policy and adapt it to different initiations and dynamic constraints. More intelligence is demonstrated than when traditional optimization methods are used. This methodology represents a new, small step toward the intelligent design of compressors.

This paper aims to present a human-in-the-loop natural teaching paradigm based on scene-motion cross-modal perception, which facilitates the manipulation intelligence and robot teleoperation.

The proposed natural teaching paradigm is used to telemanipulate a life-size humanoid robot in response to a complicated working scenario. First, a vision sensor is used to project mission scenes onto virtual reality glasses for human-in-the-loop reactions. Second, motion capture system is established to retarget eye-body synergic movements to a skeletal model. Third, real-time data transfer is realized through publish-subscribe messaging mechanism in robot operating system. Next, joint angles are computed through a fast mapping algorithm and sent to a slave controller through a serial port. Finally, visualization terminals render it convenient to make comparisons between two motion systems.

Experimentation in various industrial mission scenes, such as approaching flanges, shows the numerous advantages brought by natural teaching, including being real-time, high accuracy, repeatability and dexterity.

The proposed paradigm realizes the natural cross-modal combination of perception information and enhances the working capacity and flexibility of industrial robots, paving a new way for effective robot teaching and autonomous learning.

This paper aims to propose a shape factor for granular materials based on particle shape. The scientific goal is to investigate the influence of particle shape on the mechanical properties of rockfill materials.

The method of generating four regular-shaped particles is based on the observation that most rockfill grains are regarded as like-triangle, like-rhombus, like-square and like-hexagon. A shape factor F that is developed using the Blaschke coefficient and a concave–convex degree is proposed. A biaxial compression test on rockfill materials under stress path is numerically simulated by discrete element method. The evolution of the shape factor F under the simulated stress paths is analyzed, and particle breakage rate, peak intensity and peak-related internal friction angle for rockfill materials are derived. A method of determining the shape factor F involved in the two functions is proposed.

A new micro-parameter is calibrated using the test data of one rockfill material. Particle shape greatly affects the particle breakage rate, peak intensity and peak-related internal friction angle for rockfill materials. The final experimental grading curves all approach the particle breakage grading curve proposed by Einav (the fractal dimension is 2.7).

This study proposes a shape factor F, which describes the geometric features of natural rockfill particles. The proposed shape factor F has a simple structure, and its parameters are easy to determine. The method provides an opportunity for a quantitative study on the particle shape of granular materials, and this study helps to better understand the influence of particle shape on the mechanical characteristics of rockfill materials.

The purpose of this paper is to study the influence of temperature on the acceleration and simulation of indoor corrosion tests and the corrosion behavior of Q235 carbon steel.

The indoor corrosion test was carried out by continuous salt spray in a salt spray chamber. Weight loss analysis, X-ray diffraction, cannon 1500 D, scanning electron microscopy and electrochemical techniques are used to analyze the results.

It was found that thickness loss of Q235 carbon steel increases with higher temperature and it can reach 0.095 mm at 50°C. Compared with the Xisha exposure test, the acceleration rate can achieve 230 times. This phenomenon indicates that decreasing the experimental temperature is beneficial to the anti-corrosion of the Q235 carbon steel. It is fascinating to find that acceleration and simulation increase with temperature simultaneously, which shows that β-FeOOH promotes the corrosion rate and α-FeOOH provides high simulation. Meanwhile, electrochemical impedance spectroscopy indicates that the resistance of the rust layer improves with temperature.

Through the study, the authors found that with the increase of temperature, the acceleration and simulation of indoor corrosion test improved, corrosion products and kinetics are the same as those in outdoor exposure test, and which means that the laboratory can achieve the long-term corrosion degree of outdoor exposure in a short time, and the similarity with outdoor exposure is high. This helps to the study of marine atmospheric corrosion, and indoor accelerated corrosion tests can largely eliminate regional differences by adjusting some environmental factors, and lay a foundation for marine atmospheric corrosion.

The effects of temperature on the acceleration and simulation of indoor corrosion tests are discussed. Through laboratory experiments, the long-term service life of Q235 carbon in the Xisha marine atmosphere can be predicted effectively.

The purpose of this paper is to use the viscosity of fluid to transmit power known as the hydro-viscous drive (HVD) to research the effect of two-phase flow on transmission characteristics.

In this paper, a 3D computational model of oil film between friction pair was built to study the transmission characteristics of a two-phase oil film, and the distribution contours of pressure and temperature of oil film were investigated using the computational fluid dynamics technology.

The finding of the paper suggests that the distribution law of pressure and temperature of two-phase oil film is almost linear along the radial direction. However, since the physical phenomena near the outlet of the oil film are entirely different, there exists fluctuation. Meanwhile, the volume fraction of air was obtained at different rotation speeds, and the maximum value is 10.55 percent. Compared to the single-phase oil film, the torque transferred by the oil film is not linear with the rotation speed, its value decreases gradually.

This paper’s conclusions are very important for the study of HVD and its applications, which provide a new idea to further study the mechanism of oil film transmission and its cavitation. The development of fluid viscous speed clutch is dedicated to a large industrial fan and water pump speed regulation and energy saving. With the successful application of the technology, it will have more wide applications in different fields, such as, in steel, water, petrochemical, power plant of slag pump and exhaust fan.

The patient flow performance achievable by care pathways is constrained by competing flow and resource efficiency, which can negatively impact improvements. This paper probes the divergence between resource and flow efficiency and how care pathways can lead to improved patient flow. By framing the problem through the lens of paradox theory, a set of design principles is proposed to assist decision-makers in care pathway implementation. Implications are derived for research and practice.

The authors used conceptual research to develop design principles for care pathways based on a systematic review of relevant care pathway research. The initial search contained 515 unique articles, resulting in a final sample of 56 studies.

When applying care pathways, patient flow may be negatively affected in relation to the dimensions of bottlenecks, non-value-adding activities, and variability. However, the findings also indicate methods that can be applied to manage organizational paradoxes, which can contribute to more efficient patient flow along each of the three dimensions.

The study is limited to care pathways and therefore could have missed relevant studies in similar fields, such as care coordination.

Health care managers, politicians, and IT developers can apply the proposed design principles when developing, implementing, and improving care pathways and supporting technologies.

While existing research has studied care pathways from a medical perspective, this is the first paper to the author’s knowledge that addresses care pathways directly by considering paradox theory and in light of the operations management literature.

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which separates the domain knowledge from the modeling framework with different views.

By establishing the mechanism of association and fusion among the views, the description and characterization of system from different aspect and point of view can form a complete system model. Based on the approach, a modeling and simulation (M&S) platform named SimFaster is developed. Modeling environment and simulation engine are the most important parts of the platform. The modeling environment provides multi‐views and multi‐layers to help the developers to modeling the structure, layers, composition, behavior, and interactions of an application system. The simulation engine provides mechanism of integration and interaction for components and objects, and provides runtime support for the concepts and terms from modeling environment. The simulation engine organizes the objects in the memory of distributed system as reflective object database system, so it is repository centered architecturally.

Based on the approach of multi‐views modeling, the platform is a flexible framework and supports top‐down design, model reuse and interoperation, dynamic refinement of models, corporative design among different users in different stages, and the rebuilt of application rapidly.

This paper deals with high‐level models of the complex systems.

This platform helps to design, modeling, and simulation complex system (especially for weapon combat system). It can participate into all the stages of the development of complex product/system, and can support the validation, refinement, optimization of models, and systems.

This paper presents a multi‐views modeling approach for the modeling of complex system.

This paper aims to evaluate the dynamic performance of hybrid roller bearings under lubricant contamination.

Some steel rollers in traditional cylindrical thrust roller bearings were replaced with ceramic rollers to assemble hybrid roller bearings. Friction experiments were conducted under lubricant contamination using alumina as the contaminant, and simultaneous vibration acceleration signals from the bearings were collected to evaluate their tribological and dynamic performance.

Under lubricant contamination, hybrid roller bearings with a sufficient number of ceramic rollers exhibit greater wear resistance compared to traditional all-steel bearings. There is a noticeable suppression of energy in both tangential and normal frequency bands of the bearings, with more pronounced suppression observed in higher frequency bands.

This study provides valuable insights for the development of hybrid ceramic bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0291/

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.