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In today's increasingly competitive globalization, innovation is crucial to technological development, and original innovations have become the high horse in the fight for market dominance by enterprises and governments. However, extracting original innovative technologies from patent data faces challenges such as anomalous data and lengthy analysis cycles, making it difficult for traditional models to achieve high-precision identification. Therefore, we propose a Multi-Dimensional Robust Stacking (MDRS) model to deeply analyze patent data, extract leading indicators, and accurately identify cutting-edge technologies. The MDRS model is divided into four stages: single indicator construction, robust indicator mining, hyper-robust indicator construction, and the pioneering technology analysis phase. Based on this model, we construct a technological development matrix to analyze core 3D-printing technologies across the industry chain. The results show that the MDRS model significantly enhances the accuracy and robustness of technology forecasting, elucidates the mechanisms of technological leadership across different stages and application scenarios, and provides new methods for quantitative analysis of technological trends. This enhances the accuracy and robustness of traditional patent data analysis, aiding governments and enterprises in optimizing resource allocation and improving market competitiveness. [ABSTRACT FROM AUTHOR]

The problem of adaptive output feedback control for a class of nonlinear Cyber-Physical Systems(CPSs) with unknown control direction under sensor and actuator attacks is studied. By implementing a suitable linear state transformation, the original system is transformed into a new system for facilitating the design of output feedback controller. Nussbaum gain function method is adopted to overcome the design difficulty resulted from unknown control coefficients and network attacks. Based on the state estimate and the attacked measurement output signal, a new event-triggered adaptive output-feedback control strategy is proposed by means of neural network approximation method. The proposed controller can not only achieve the semi-globally uniformly ultimately boundedness of the closed-loop system, but also greatly reduce the number of transmitted data. In addition, the "complexity explosion" problem in backstepping design can be overcome by means of the dynamic surface control (DSC) method based on a nonlinear filter. Finally, a robotic arm system is utilized to verify the effectiveness and practicality of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) tourism e-commerce platforms (VRTEPs) can provide trial experience for tourists, which is easy to trigger irrational impulse buying. Based on cognitive emotion theory and S-O-R model, this study innovatively explores the impact of the multi cue-interaction stimulation (the interactivity of VR display and the empathy of text display) of VRTEPs on consumers' impulse buying intention through the mediating effect of new cognitive and emotional responses, namely, interactive pleasure (perceived usefulness and immersion). The results reveal that: (1) The interactivity of VR display stimulates interactive pleasure, then positively affects impulse buying intention. (2) The empathy of text display only stimulates perceived usefulness to positively affect impulse buying intention. This study also finds that the interactive comparison has a negative moderating effect on the relationship between immersion and impulse buying intention. These findings contribute to developing the new theory for effectively marketing using highly interactive new technology on VRTEPs. [ABSTRACT FROM AUTHOR]

In the era of information overload and repetitive reviews, there has been limited exploration into the influence of consumers' cultural adaptation to creative symbols in theme parks on the usefulness of online reviews, which is significant for enhancing tourism experiences, targeted marketing, personalized services, and informed tourism choices. This study aims to bridge this gap by examining how cultural adaptation factors interact and impact the review usefulness, and by considering the role of cultural adaptation in simplifying information during consumer decision-making processes. Additionally, the study investigates how consumers' decision reference points, represented by advanced ticket levels, moderate their attention to attribute consistency when evaluating the review usefulness. A sample of 5929 valid consumer reviews of Disney theme parks from 2019 to 2022 on Meituan.com is analyzed using latent semantic analysis and Tobit regression to test the proposed hypotheses. We find that high symbolic creativity reviews stimulate cultural adaptation and increase attention to service attributes when evaluating review usefulness. Moreover, advanced ticket levels do not moderate the usefulness of extremely negative reviews. However, they do have a moderating effect on the usefulness of extremely positive reviews, with the direction of moderation differing based on the levels of symbolic creativity. [ABSTRACT FROM AUTHOR]

The maintenance and strategy operation after patent licensing can bring great market competitiveness and benefits to enterprises. But the large time span from patent licensing to market application makes it challenging to discern the benefits of patent competition strategy. Besides, artificial intelligence (AI) is an emerging industry without ready-to-use experience to formulate patent competition strategy, and particularly current researches have not designed patent competition strategy from the micro patent management perspective of AI enterprises to solve the uncertainty caused by the lag of market application relative to patent licensing. This research builds an expert group discriminant system based on the system dynamics method to address this problem. It integrates expert tacit knowledge to determine the fuzzy variable value and the fuzzy relationship. The patent competition strategy subsystem in national dimension, industry dimension and enterprise dimension for capturing the market from the perspective of enterprise technology competitiveness are constructed. By combining the three subsystems, the enterprise patent competition strategy system dynamics model with evolution analysis is established. Finally, taking typical Chinese AI enterprise iFLYTEK as an example, the multi-scenario simulation is carried out and the results under four different scenarios can provide effective decision supports for managers to formulate reasonable patent competition strategy and gain high market share. This research sheds light on modeling and evolution analysis of the patent competition strategy which comprehensively and systematically considers the operation mechanism of patent management and contributes to dealing with the uncertainty and ambiguity in the system dynamics model effectively. [ABSTRACT FROM AUTHOR]

Transition metal selenides with high performance were thought to be viable electrode materials for alkali-ion batteries. By combining a simple hydrothermal technique with vacuum selenization treatment to generate FeSe2@3DGF grown on three-dimensional graphene foam backbone, a promising anode material for sodium-ion batteries was designed in this article. FeSe2@3DGF had good electrochemical properties, a large reversible capacity, and cycle stability. In particular, the FeSe2@3DGF electrode can provide a reversible capacity of 600.8 mAh g−1 for sodium-ion batteries after 300 cycles at a current density of 500 mA g−1. [ABSTRACT FROM AUTHOR]