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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.

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.

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 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 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.

Based on upper echelons theory and social contagion theory, this study aims to explore how to translate leader sustainability orientation (LSO) into green supply chain integration (GSCI) by green entrepreneurial orientation (GEO), as well as the moderating effects of four dimensions of organizational learning capability (OLC).

This study conducts hierarchical regression analysis using multi-sourced survey data collected in 264 Chinese companies to examine hypotheses.

The results indicate that LSO has positive influences on green supplier and customer integration. Furthermore, GEO partially mediates the impacts of LSO on green supplier and customer integration. Managerial commitment positively moderates the impact of LSO on GEO, while other dimensions such as systems perspective, openness and experimentation and knowledge transfer and integration have non-significant moderating effects.

This study enriches the existing research on internal drivers of GSCI and contributes to the understanding of the direct impact of LSO and mediating effects of GEO. This study extends social contagion theory boundaries by investigating the moderating effects of OLC on the relationship between LSO and GEO.

The purpose of this paper is to provide a maximum speed algorithm for serial palletizing robots, which guarantees relatively low system modeling requirements and can be easily implemented in actual applications.

Operation speed is an important index of palletizing robots performance. In order to improve it, features of palletizing motions are analyzed, and a refined iterative learning control algorithm for maximum speed optimization is proposed. The refined algorithm learns to increase local speed when the following error does not exceed a predefined tolerance, unlike conventional applications which make actual output identical to its reference. Furthermore, experiments were developed to illustrate the new algorithm's ability to take full advantage of motor capacity, drive ability and repetitive link couplings to improve palletizing efficiency.

Experiments show that motion time decreases more than 20 percent after optimization.

The new iterative control algorithm can be easily applied to any repetitive handling operations where manipulating efficiency matters.

The purpose of this paper is to introduce the elementary concepts and fundamental principles of grey systems and the main components of grey systems theory. Also to discuss the astonishing progress that grey systems theory has made in the world of learning and its wide‐ranging applications in the entire spectrum of science.

The characteristics of unascertained systems including incomplete information and inaccuracies in data are analysed and four uncertain theories: probability statistics, fuzzy mathematics, grey system and rough set theory are compared. The scientific principle of simplicity and how precise models suffer from inaccuracies are also shown.

The four uncertain theories, probability statistics, fuzzy mathematics, grey system and rough set theory are examined with different research objects, different basic sets, different methods and procedures, different data requirements, different emphasis, different objectives and different characteristics.

The scientific principle of simplicity and how precise models suffer from inaccuracies are shown. So, precise models are not necessarily an effective means to deal with complex matters, especially in the case that the available information is incomplete and the collected data inaccurate.

The elementary concepts and fundamental principles of grey systems and the main components of grey systems theory are introduced briefly. The reader is given a general picture of grey systems theory as a new method for studying problems where partial information is known, partial information is unknown; especially for uncertain systems with few data points and poor information.