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The purpose of this paper is to prepare new modified polypropylene (PP) with phenolic microspheres (PFMs). Furthermore, the crystallinity and mechanical properties of PP modified by fillers (silicon dioxide [SiO₂] and light calcium carbonate [CaCO₃], respectively) have also been investigated and compared.

For effective toughening, three different fillers were added into the PP matrix. PP composites were prepared through melt blending with double-screw extruder and injection moulding machine.

It was found that with the addition of 3 Wt.% PFM, the impact strength was maximum in all PP composites and increased by 1.4 times compared to pure PP. Scanning electron microscopy (SEM) and polarised optical microscopy (POM) analysis confirmed that 3 Wt.% PFM, 3 Wt.% SiO₂ and 2 Wt.% CaCO₃ were optimal to add in PP and PFM to give the best compatibility with PP.

PFM particles not only are tougher and less brittle and can offer other advantages such as enhanced machinability, but also are important organic materials and have a good compatibility with polymer for reinforcing polymer properties.

The method developed provided a simple and practical solution to improving the toughness of PP.

There will be thermoplastic plastics with higher toughness in domestic, packaging and automotive applications, particularly at lower temperatures.

The PP modified by tiny amounts of fillers in this work had high toughness, which can be applied as an efficient material widely used in domestic, packaging and automotive applications.

The purpose of this research is to synthesize Al₂O₃-ZnO thick films, study the effect of doping and optical excitation on their sensing properties and introduce an attractive candidate for acetone detection in practice.

ZnO nanoparticles doped with Al₂O₃ were prepared by sol-gel method and characterized via X-ray diffraction and field-emission scanning electron microscopy. The sensing properties to acetone were investigated with an irradiation of UV. The sensing mechanism was also discussed with UV-Vis spectroscopy.

The doping of Al₂O₃ promoted the sensing response and stability of ZnO nanoparticles. The optimum performance was obtained by 4.96 Wt.% Al₂O₃-ZnO. The response to acetone (1,000 ppm) was significantly increased to 241.81, even just at an operating temperature of 64°C. It was also demonstrated that optical excitation with UV irradiation greatly enhanced the sensing response and the sensitivity can reach up to 305.14.

The sensor fabricated from 4.96 Wt.% Al₂O₃-ZnO exhibited excellent acetone-sensing characteristics. It is promising to be applied in low power and miniature acetone gas sensors.

In the present research, the optimum performance was obtained by 4.96 Wt.% Al₂O₃-ZnO at a low operating temperature of 64°C. The sensing properties were enhanced significantly with optical excitation, and the sensing mechanism was discussed with UV-Vis spectroscopy which has been reported rarely before.

This paper aims to explore the effect of different government subsidy decisions and the differences between the consequences of these decisions when supply chain members engage in cooperative green innovation through cost-sharing arrangements.

This paper investigates the optimal decisions for green supply chains under two types of subsidies, including subsidies for green innovation research and development (R&D) costs and subsidies for consumers, by integrating game theory with numerical simulation.

The optimal R&D cost-sharing ratio is found to be 2/3 for manufacturers and 1/3 for retailers. Under any subsidy policy, the supply chain can achieve maximum total profit. When the supply chain adopts the optimal R&D cost-sharing ratio, subsidies for green innovation R&D costs prove to be the most effective in increasing the supply chain’s profit. However, from the perspective of total social welfare, the analysis reveals that government subsidies to consumers are more beneficial for promoting overall social welfare.

Previous studies on green supply chain decisions have primarily focused on either government subsidies or corporate cost sharing in isolation. In contrast, this study combines both government subsidies and cost sharing within a unified framework for a more comprehensive analysis. Additionally, this paper examines the impact of government subsidies on supply chain cost-sharing decisions and their effect on overall social welfare while considering the presence of cost sharing and using the combination of theoretical modeling and simulation analysis.

The widespread use of artificial intelligence (AI) technology in the hospitality industry has triggered concerns among frontline service employees about their future careers, namely, AI awareness. This study aims to explore whether AI awareness influences frontline service employees’ silence through psychological contract breach and whether this process is contingent on frontline service employees’ moral identity, drawing on social exchange theory and moral identity theory.

The data were collected from 355 frontline service employees in Chinese hotels using a two-wave survey. SPSS macro PROCESS Model 58 was used to test the proposed hypotheses.

AI awareness increases frontline service employees’ silence by prompting psychological contract breach. This process is moderated by frontline service employees’ moral identity. Specifically, moral identity mitigates the effect of psychological contract breach on silence.

Organizations and managers should pay attention to the impact of AI on frontline service employees and take measures to help them better adapt to the rapidly changing work environment. In particular, it helps reduce frontline service employees’ silence by fostering positive attitudes toward AI, maintaining their psychological contracts and developing their moral identities.

This study enriches the research on the outcomes of AI awareness by directing our attention to frontline service employees’ silence. Moreover, this study not only explores the responses to AI awareness that frontline service employees make as “economic persons” but also examine whether they, as “moral persons,” regulate their responses contingent on their moral identity under the impact of AI. Exploring frontline service employees’ dual identities helps bring this research closer to the realities of managerial practice, thereby contributing to a better understanding and management of their complex responses to AI shocks.

This paper aims to present a novel approach of image super-resolution based on deep–shallow cascaded convolutional neural networks for reconstructing a clear and high-resolution (HR) remote sensing image from a low-resolution (LR) input.

The proposed approach directly learns the residuals and mapping between simulated LR and their corresponding HR remote sensing images based on deep and shallow end-to-end convolutional networks instead of assuming any specific restored models. Extra max-pooling and up-sampling are used to achieve a multiscale space by concatenating low- and high-level feature maps, and an HR image is generated by combining LR input and the residual image. This model ensures a strong response to spatially local input patterns by using a large filter and cascaded small filters. The authors adopt a strategy based on epochs to update the learning rate for boosting convergence speed.

The proposed deep network is trained to reconstruct high-quality images for low-quality inputs through a simulated dataset, which is generated with Set5, Set14, Berkeley Segmentation Data set and remote sensing images. Experimental results demonstrate that this model considerably enhances remote sensing images in terms of spatial detail and spectral fidelity and outperforms state-of-the-art SR methods in terms of peak signal-to-noise ratio, structural similarity and visual assessment.

The proposed method can reconstruct an HR remote sensing image from an LR input and significantly improve the quality of remote sensing images in terms of spatial detail and fidelity.

Despite the growing interest in enhancing mass customization capability (MCC), firms still have little knowledge of dealing with the superimposed challenges of increased market uncertainty and supply chain disruptions. Based on the dynamic capability view, this study focuses on the impacts of frequent sensing and reconfiguring processes on MCC and the mediating roles of proactive and reactive supply chain resilience (SCR).

We collected survey data from 277 manufacturing firms and conducted a structural equation model to test hypotheses.

The results reveal that although its direct effect on MCC is insignificant, sensing process improves MCC indirectly via reactive SCR. Our findings also show that reconfiguring process enhances MCC both directly and indirectly via reactive SCR.

This study provides theoretical and practical insights into how to combine dynamic capability and SCR to strengthen MCC.

Tantalum is a kind of metal material with moderate hardness, high ductility, small thermal expansion coefficient, excellent corrosion resistance and outstanding biocompatibility. The purpose of this study is that its tribological performance could be tested and analyzed so as to use it in different fields.

The friction resistance of a-Ta under dry friction conditions was tested at different roads. The relationships between load and friction coefficient, wear rate and two-dimensional shape of wear scars were studied.

The stable Ta₂O₅ film with lubrication effect was generated in the process of friction. And, the larger the test load, the more Ta₂O₅ would be generated.

This work lays a theoretical foundation for tantalum as an excellent wear-resistant material.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0047/

Influence diagrams (IDs) have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields. Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs, however, most information systems in the real world are nondeterministic, and data in information tables can be interval valued, multiple valued and even incomplete. Consequently, conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain. The paper aims to discuss these issues.

The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems, approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis. Hence, this study proposes a new mathematical model by combining grey rough sets with IDs, and approximate measurements are used instead of probability distribution, an implicational relationship is utilized instead of an indiscernible relationship, and all of the features of the proposed approach contribute to deal with uncertain problems.

The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.

Collaboration of IDs and grey rough sets is first proposed, which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.

Based on the information commons service model, the aim of this article is to propose a new model for knowledge commons. It seeks to define the conceptual model and constructing framework of knowledge commons, which aim for a collaborative knowledge‐sharing environment to support innovative community activities of university library.

By analyzing the innovation activities of communities, infusing theories of knowledge management, collaboration and Library 2.0, the knowledge commons conceptual model is brought forward to improve communication, collaboration, sharing and conversation.

Since the innovative community is interdisciplinary and cross‐campus, the scattered research team and study group requires a library to extend the services to a logical system, while the virtual layer is to make this spatial decentralized and logically centralized system a reality. The core elements, namely information technology, organization and management, culture and spirit, make up the supporting layer, of which, trust and collaboration culture for innovation is important.

Research work and practice of information commons and library 2.0 have aroused a new round of the library service movement, while the knowledge commons conceptual model would provide steering for a knowledge sharing environment.

In this paper, the new model is based on information commons and assimilating the theories of knowledge management, collaboration and Library 2.0, intended to integrate digital library, physical resource, virtual resource and human resource into a whole. It is of great importance for the library to serve education and scientific research well.

In the new retail era, the supply chain synergy produced by quality integration has become the new direction of service supply chain research. The purpose of this paper is to study how to promote the sustainable development of the retail service supply chain (RSSC) by comparing and analyzing the optimal quality behavior, optimal returns, and the combination of conditions and strategies of the participating members of the RSSC.

From the perspective of quality function development and service quality concern, this paper considers RSSC as a two-level supply chain structure composed of functional service provider and retail service integrator. In this paper, a dynamic optimization model of quality input-cooperation-coordination of RSSC is proposed under two quality cooperation modes of decentralization and integration. This paper adopts the differential game method to compare and analyze the optimal quality behavior, optimal income, forming conditions and strategic combination of the participating members of the RSSC in different situations.

(1) Compared with quality dispersion, quality integration has more significant Pareto improvement effect on quality behavior and optimal revenue of RSSC. (2) In the case of quality integration, the optimal revenue obtained by the service sharing model is generally better than that obtained by the retail alliance collaboration model. (3) Benefit distribution ratio and quality cost allocation determine the optimal quality behavior of participating members of the RSSC, and also become the key factors for participating members to choose the collaborative mode in the case of quality integration.

Based on the quality function development of RSSC, this paper introduces the concept of steady service quality, and discusses the relationship between the quality cooperation stability and the mode selection of RSSC. This provides a theoretical basis for how to build a RSSC with efficient operation and stable quality.