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In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

The most recent and prestigious scientific research shows that nitrogen leaching caused by over-used nitrogen fertilizer rapidly acidifies all soil types in China, revolutionizing the basic understanding of the mechanism of soil acidification. The purpose of this paper is to study the impact of nitrogen on soil acidity over the long run, which is the shadow price of nitrogen.

In a discrete dynamic programming model, this paper compares the nitrogen application and soil pH between optimal nitrogen control that takes the shadow price of nitrogen into consideration and myopic nitrogen control that ignores that shadow price. Using a five-year panel experimental data on a rapeseed-rice rotation, this paper simulates and numerically solves the dynamic model.

Both theoretically and empirically, this paper shows that the over-use of nitrogen and the decline in soil pH are explained by ignorance of the shadow price of nitrogen. Compared with optimal nitrogen control, myopic nitrogen control applies more nitrogen in total, resulting in lower soil pH. In addition, over-use in the first season contributes to soil acidification and the carry-over effects mitigate that problem.

This paper enriches the literature by extending the study of the environmental impact of nitrogen leaching to its impact on the long-term loss in agricultural production, providing a new theoretical framework in which to study soil acidification rather than conventionally treating soil acidification as a secondary consequence of acid rain, and showing the possibility of using nitrogen control to mitigate soil acidification when lime applications are not feasible due to socio-economic constraints.

Despite servitization being widely regarded as an essential catalyst to improve manufacturing firms' survival and competitiveness, how to attain servitization remains debatable. The primary objective of this research is to explore whether or not, how, and when the dynamic capabilities affect servitization in the digital economy background. This research investigates the relationships between servitization and dynamic capabilities by incorporating firm ownership, firm lifecycle stage, digital economy level and environmental uncertainty as contingency factors in the research framework.

This research develops and verifies a conceptual framework for manufacturing servitization by employing the fuzzy-set qualitative comparative analysis (fsQCA) in analyzing the secondary longitudinal data from 148 China-listed manufacturing firms involved in servitization from 2015 to 2020.

The analytical results of fsQCA identify several configurational solutions for the success of manufacturing servitization. Each factor can be an enabler for servitization success despite none of the factors discovered as an absolute condition. Manufacturing servitization success within the digital economy depends on the interactions between dynamic capabilities and contingency factors such as digital economy level, environmental uncertainty, firm ownership, and lifecycle stage.

All of the construct's measurements in this research adopt secondary data, and further investigation calls for primary data (e.g. survey) for higher validity.

This research extends the current view of servitization by proposing an integrative conceptual framework, allowing manufacturing servitization to be examined more pertinently and comprehensively. Second, the research is an initial attempt that adopts fsQCA in servitization studies. The study sheds light on the mechanisms of attaining servitization by revealing the importance of dynamic capabilities and their interactions with the contingency factors. Third, the research extends the application scopes of dynamic capability theory, firm lifecycle theory, contingency theory, and institutional theory. Fourth, the research findings enrich the understanding of servitization in the digital economy and give business practitioners insights on leveraging dynamic capabilities in different conditions to attain successful servitization under the current circumstances.

This paper aims to successfully synthesize three-dimensional spindle-like Au functionalized Co₃O₄-ZnO nanocomposites; characterize the structure, morphology and surface chemical properties of the products; study the effect of Au NPs doping concentration, operating temperature different gas to, sensing properties; and introduce an attractive gas sensor for acetone detection.

Au NPs functionalized Co₃O₄-ZnO nanocomposite was prepared by coprecipitation and impregnation methods; the structure and surface chemical property of the products were characterized by XRD, SEM, TEM, UV-Vis, BET and XPS. The sensing ability of Au@Co₃O₄-ZnO for acetone and mechanism was analyzed systematically.

The results of gas sensing tests show that the unique component structure, Schottky junction and catalytic effect of Au functionalization make it have low operating temperature, excellent selectivity, high response (10 ppm, 56) and rapid response recovery time.

All the characterization and test data of the prepared materials are provided in this paper and reveals the gas sensing mechanism of the gas sensor.

The detection limit is 2.92–100 ppb acetone. It is promising to be applied in low-power, micro detection and miniature acetone gas sensors.

The gas sensor prepared has a lower working temperature and low detection limit, so it has promising application prospects in low-concentration acetone detection and early warning.

The unique component structure, Schottky junction and catalytic effect of Au functionalization Co₃O₄-ZnO make it have low operating temperature, excellent selectivity and rapid response recovery time.

This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

This paper aims to explore the preparation and tribological performance of MoS₂ nanoparticles supported on fly ash (FA) microparticles.

FA was activated by NaOH, oleic acid and HCl to obtain three modified FA samples. Nano-MoS₂ was deposited on them to form MoS₂/FA additives for poly-α-olefin (PAO) modification. Tribological tests were conducted on a reciprocating rig through the ball-on-disk friction manner. Using X-ray diffraction, scanning electron microscope, energy dispersive spectrometer, Raman spectrometer and element analyzers, the products and their lubrication mechanisms were characterized.

At 1.5 Wt.%, nano-MoS₂ and MoS2/FA could remarkably improve the tribological properties of PAO. The nano-MoS₂ deposited on the HCl-activated FA presented better lubrication performance than nano-MoS₂. It could reduce friction and wear by approximately 27% and approximately 66%, respectively. The lubrication of MoS₂/FA can be attributed to the formation of MoS₂ and carbon containing lubricating film.

FA was applied as a supporter to prepare MoS₂/FA lubricants. The reuse of FA, a solid waste, is important for environmental protection. Moreover, MoS₂/FA is more economical than nano-MoS₂ as a lubricant, because it contains approximately 71% of low-cost FA.

The purpose of this paper is to study the resonance failure sensitivity analysis of straight-tapered assembled pipe conveying nonuniform axial fluid by an active learning Kriging (ALK) method.

In this study, first, the motion equation of straight-tapered assembled pipe conveying nonuniform fluid is built. Second, the Galerkin method is used for calculating the natural frequency of assembled pipe conveying nonuniform fluid. Third, the ALK method based on expected risk function (ERF) is used to calculate the resonance failure probability and moment independent global sensitivity analysis.

The findings of this paper highlight that the eigenfrequency and critical velocity of uniform fluid-conveying pipe are less than the reality and the error is biggest in first-order natural frequency. The importance ranking of input variables affecting the resonance failure can be obtained. The importance ranking is different for a different velocity and mode number. By reducing the uncertainty of variables with a high index, the resonance failure probability can be reduced maximally.

There are no experiments on the eigenfrequency and critical velocity. There is no experiments about natural frequency and critical velocity of straight tapered assembled pipe to verify the theory in this paper.

The originality of this paper lies as follows: the motion equation of straight-tapered pipe conveying nonuniform fluid is first obtained. The eigenfrequency of nonuniform fluid and uniform fluid inside the assembled pipe are compared. The resonance reliability analysis of straight-tapered assembled pipe is first proposed. From the results, it is observed that the resonance failure probability can be reduced efficiently.

This paper aims to use an ionic liquid (IL, [HMIM]PF₆) to improve the lubrication performance of liquid metal (LM) as a lithium grease additive and to expand the application range of LM.

In this paper, the different mass ratios of [HMIM]PF₆/LM mixtures were added into the lithium grease on a four-ball tribo-meter to investigate the effects of its tribological behavior. Scanning electron mircoscope/energy dispersive spectroscopy and X-ray photoelectron spectroscopy were used to reveal the anti-wear and friction-reducing mechanism of the additives.

When the load was used at 461 N, the average coefficient of friction (ACOF) and average wear scar diameter (AWSD) of steel ball Lubricated with grease with an optimal ratio of 2:3 ([HMIM]PF₆/LM) were reduced by 32.8% and 30.5%, respectively. Friction and wear mechanisms are ascribed to friction-induced additive components that can simultaneously form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

Compared with the pure lithium-based grease, when [HMIM]PF₆/LM was added with an optimal ratio of 2:3, the ACOF and AWSD were reduced by 12.4% from 0.097 to 0.085 and 23.8% from 552.117 µm to 420.590 µm under 392 N, respectively. When at 461 N, the ACOF and AWSD of steel ball were reduced by 32.8% from 0.122 to 0.082 and 30.5% from 715.714 µm to 497.472 µm, respectively. It was shown that the simultaneous addition of LM and [HMIM]PF₆ can form a composite lubrication film consisting of FePO₄, FeF₃, Ga₂O₃, In₂O₃ and SnO₂.

In this paper, [HMIM]P F₆ is added with LM simultaneously to improve the lubrication properties of lithium grease, and expand the application scope of LM.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0017/