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Strengthening the combination of technology and finance can significantly promote the development of economy and society. Urbanization is a crucial standard to measure the economic and social development of a country and region, and urban regional planning based on science and technology finance has always been the focus of both domestic and foreign research institutions. Thus, this paper takes Mianyang, the first city of science and technology, as the object of research, and from the angle of the development process of Mianyang, investigates the three stages of the construction and development of this science and technology city. This study analyzes the characteristics of regional planning of Mianyang City and sums up the idea of relying on the old city to build another new district, which boosts the development of science and technology as well as the economy. From two specific angles (i.e., urban spatial function region planning and urban and rural planning), this paper thoroughly studies a multiscale planning scheme of Mianyang’s urban area in recent years by researching the local policy, system, finance, and society. Empirical measurement proves that reasonable planning and construction of the science and technology city Mianyang can accelerate the development process of the western region, effectively promoting the economic development of the surrounding areas of Sichuan and remarkably improving the overall quality of the regional economy of both Chongqing and Sichuan Provinces.

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.

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.

This study analyzes in-depth how knowledge-intensive small and medium-sized enterprises (SMEs) can achieve higher new product development (NPD) process performance in the epidemic era and examine the internal development mechanism of knowledge-intensive SMEs in the process of continuous digital transformation.

This issue is tested with partial least squares on data collected via a survey conducted from November 2021 to February 2022. The sample comprises 487 knowledge-intensive SMEs operating in China.

The results indicate that one form of cross-functional ambidexterity, market development strategy (MDS), plays an important role in process performance from an inside-out financial perspective and an outside-in customer perspective. Simultaneously, product innovation efficiency (PIE) mediates the relationship between MDS and the above results. Big data analytics capabilities (BDACs) positively regulate the relationship between MDS and PIE.

The authors do not consider other contingency factors. Future research should introduce influential factors such as leadership and competitive intensity to further distinguish the effects of MDS on NPD process performance.

The study findings offer suggestions to help knowledge-intensive SME managers better manage their NPD process by making better use of their limited resources in developing countries such as China.

This study is one of only a few to adopt a process-oriented perspective to specifically examine how one form of cross-functional ambidexterity, MDS, impacts knowledge-intensive SME process performance in the epidemic era. This study also extends the theoretical framework of cross-functional ambidexterity to BDAC research.

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 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.

This study aims to distinguish how unabsorbed and absorbed slack affects market and financial performance via proactive and reactive supply chain resilience (SCRES), particularly under varying conditions of organizational ambidexterity.

By collecting survey data from 277 Chinese manufacturers, we verify the conceptual model applying structural equation modeling.

Proactive SCRES mediates the positive impacts of both unabsorbed and absorbed slack on market and financial performance, whereas reactive SCRES mediates only their positive effects on financial performance. High levels of organizational ambidexterity strengthen the indirect effects of both types of slack on market and financial performance via proactive SCRES, but not when mediated by reactive SCRES.

We introduce a new theoretical perspective to view fits (as mediation) between the use of unabsorbed/absorbed slack in different ways when switching attentions to proactive or reactive SCRES, both of which can be improved through organizational ambidexterity. This study offers novel insights into how managers can switch attentions between proactive and reactive SCRES knowing when to appropriately use unabsorbed/absorbed slack for which purposes, and the use of different learning modes (explorative vs exploitative).

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/

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.

In additive manufacturing processes such as stereolithography and fused deposition modeling, optimal part orientation is pivotal in improving the quality of the part. This paper aims to propose an automatic and optimal part orientation system to improve part quality/accuracy in additive manufacturing, which minimizes the production time and hence reduces the cost of product.

The developed system reads STEP AP 203 E2 file from CATIA V5 and generates data extraction output file by extracting the relevant geometrical and topological data using an object-oriented approach. Afterwards, the algorithms and rules are developed to extract and recognize feature faces along with their geometric properties such as face type, face area, parallelism and perpendicularity. The feature data obtained that are used to develop feasible part orientations depend on the maximization of G&DT for all part faces. The automatic slicing is then achieved by creating slicing file using CATVBA editor inside CATIA V5.

After slicing, output data are exported in Excel data sheet to calculate the total additive volume of the part. The building time of the part is then calculated on the basis of machine parameters, part geometry, part height, layer thickness and amount of support volume needed to build the part. The optimal orientation of the part is achieved by maximization of G&DT value and minimization of production time. The proposed methodology is tested using an illustrative example.

Although lot of approaches have been discussed in the literature, automation of setup planning/orientation of the part in additive manufacturing is not fully attained. Therefore, the article focuses on the automation of setup planning by adding automatic feature extraction and recognition module along with the automatic slicing during setup planning. Moreover, the significance of adding feature extraction and recognition module is to achieve best accuracy for form feature faces and hence reduction in post processing machining/finishing operations.