Zheng Wang

This paper endeavors to appraise scholarly works from the 1940s to the contemporary era, examining the scientific quest to transpose human cognition and consciousness into a digital surrogate, while contemplating the potential ramifications should humanity attain such an abstract level of intellect. The discourse commences with an explication of theories concerning consciousness, progressing to the Turing Test apparatus, and intersecting with Damasio’s research on the human cerebrum, particularly in relation to consciousness, thereby establishing congruence between the Turing Test and Damasio’s notions of consciousness. Subsequently, the narrative traverses the evolutionary chronology of transmuting human cognition into machine sapience, and delves into the fervent endeavors to metamorphose human minds into synthetic counterparts. Additionally, theoretical perspectives from the domains of philosophy, psychology, and neuroscience provide insight into the constraints intrinsic to AI implementations, contentious hypotheses, the perils concealed within artificial networks, and the ethical considerations necessitated by AI frameworks. Furthermore, contemplation of prospective repercussions facilitates the refinement of strategic approaches to safeguard our future Augmented Age Realities within AI, circumventing the prospect of inhabiting an intimidating technopolis where a mere 30% monopolize the intellect and ingenuity of the remaining 70% of human minds.

In the complex environment, the suddenly changing structural parameters and abrupt actuator failures are often encountered, and the negligence or unproper handling method may induce undesired or unacceptable results. In this paper, taking the suddenly changing structural parameters and abrupt actuator failures into consideration, we focus on the robust adaptive control design for a class of heterogeneous Takagi–Sugeno fuzzy nonlinear systems subjected to discontinuous multiple uncertainties. The key point is that the switch modes not only vary with the system time but also vary with the system states, and the intrinsic heterogeneous characteristics make it difficult to design stable controllers. Firstly, the concepts of differential inclusion are introduced to describe the heterogeneous fuzzy systems. Meanwhile, a fundamental lemma is provided to demonstrate the criteria of the boundness for a Filippov solution. Then, by using the set-valued Lie derivative of the Lyapunov function and introducing a vector of specific continuous functions, the closed-loop T-S fuzzy differential inclusion systems are proved to be ultimately bounded. The sufficient conditions for system stability are derived in term of linear matrix inequalities, which can be solved directly. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control algorithm.

With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem, the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, and finally, a distributed robust vehicle routing model for uncertain customers is established. The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. Compared with the certain method, the distributed robust model can effectively reduce the total cost and the robust conservatism while ensuring customer satisfaction. The improved algorithm also has good performance.

Chronic stress impairs working memory, but few studies have explored the protective factors of the impairment. We aimed to investigate the effect of self-awareness on WM processing in people under chronic stress. Participants under chronic stress completed an n-back task after a self-awareness priming paradigm during which electroencephalograms were recorded. The behavioral results showed that participants whose self-awareness was primed reacted faster and more accurately than the controls. Event-related potentials revealed the following P2 was more positive in the self-awareness group than in the controls, indicating that self-awareness enhanced allocation of attention resources at the encoding stage. N2 was attenuated in the self-awareness group compared with the controls, indicating that smaller attention control efforts were required to complete WM tasks adequately after self-awareness priming; and enhanced late positive potential was evoked in the self-awareness group compared with the controls, suggesting self-awareness enabled participants to focus attention resources on the information at the maintenance stage. Critically, mediational analyses showed that LPP mediated the relationship between self-awareness and WM response times. This result suggests that the fact that participants whose self-awareness was primed were able to achieve better behavioral performances may be attributed to their mobilization of sustained attention resources at the maintenance stage. In summary, self-awareness exerted a protective effect on WM in those under chronic stress, which may be due to the enhancements in the allocation and mobilization of attention. These results could be used to develop more specific coping strategies for people under chronic stress.

This study investigated the impact of transformational leadership on affective organizational commitment and job performance with the mediating role of employee engagement. This study gathered data from 845 hotel employees in China and the structural equation modeling technique was used to verify the results. The findings indicated that transformational leadership has a positive effect on affective organizational commitment and job performance. Meanwhile, results showed that employee engagement partially mediates in the relationship between transformational leadership, affective organizational commitment, and job performance. This study contributes to the research on transformational leadership in the Chinese hospitality sector and analyzes its effects on work performance metrics. Furthermore, theoretical and practical implications were also discussed in this article.

To cope with economic globalization and improve the competitiveness of transnational start-ups, the impact of the work environment and innovation behavior on corporate performance of multinational new ventures is analyzed. First, a model of the interaction among environment, innovation behavior, and enterprise performance is proposed. Then, 296 transnational start-ups in coastal areas are surveyed, and the model results are analyzed. Finally, a series of results are obtained. The results show that from the perspective of psychology, work dynamic organizational learning environment has a positive impact on enterprise performance, and resource environment has a significant positive impact on enterprise performance. Strategic environment also positively affects enterprise performance. Therefore, the working environment of multinational new ventures has a positive impact on firm performance, and the mediating function between the working environment and firm performance is firm innovation behavior. With the research to achieve enterprise innovation of multinational new ventures by improving their response to the dynamic environment, the corporate performance has been greatly promoted, and finally, the new ventures would participate in the international market competition.

This paper investigates the antidisturbance formation control problem for a class of cluster aerospace unmanned systems suffering from multisource high-dynamic uncertainties. Firstly, to estimate and compensate the uncertainties existing in CAUS coordinate dynamics, an adaptive antidisturbance formation control law, which is combined by a robust adaptive control law and the second order disturbance observer, has been designed. Secondly, aiming at the adverse influences caused by the nonlinear time-varying nonlinearities existing in the formation flight dynamics, the radial basis function neural network is introduced. Furthermore, considering the rapidly varying characteristics of the aforementioned formation flight nonlinearities, a novel board RBFNN has been constructed and utilized to improve the approximation and compensation performance. In virtue of the fusing of the B-RBFNN and the second-order disturbance observer-based adaptive formation control law, the rapid response rate and the higher control accuracy of the formation control system can be achieved. As a result, a novel B-RBFNN-based intelligence adaptive antidisturbance formation control algorithm has been established for CAUS trajectory coordination and formation flight. Numerical simulation results are proposed to illustrate the effectiveness and advantages of the proposed B-RBFNN-based intelligent adaptive formation control method for the CAUS.

Whale optimization algorithm, known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array is used as an accelerator, of which the high-level synthesis utilizes open computing language as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization and competitive swarm optimizer are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

Memory exhibits episodic superposition, an analog of the quantum superposition of physical states: Before a cue for a presented or unpresented item is administered on a memory test, the item has the simultaneous potential to occupy all members of a mutually exclusive set of episodic states, though it occupies only one of those states after the cue is administered. This phenomenon can be modeled with a nonadditive probability model called overdistribution (OD), which implements fuzzy-trace theory's distinction between verbatim and gist representations. We show that it can also be modeled based on quantum probability theory. A quantum episodic memory (QEM) model is developed, which is derived from quantum probability theory but also implements the process conceptions of global matching memory models. OD and QEM have different strengths, and the current challenge is to identify contrasting empirical predictions that can be used to pit them against each other

Quantum cognition is an emerging field that uses mathematical principles of quantum theory to help formalize and understand cognitive systems and processes. The topic on the potential of using quantum theory to build models of cognition (Volume 5, issue 4) introduces and synthesizes its new development through an introduction and six core articles. The current issue presents 14 commentaries on the core articles. Five key issues surface, some of which are interestingly controversial and debatable as expected for a new emerging field

Quantum cognition research applies abstract, mathematical principles of quantum theory to inquiries in cognitive science. It differs fundamentally from alternative speculations about quantum brain processes. This topic presents new developments within this research program. In the introduction to this topic, we try to answer three questions: Why apply quantum concepts to human cognition? How is quantum cognitive modeling different from traditional cognitive modeling? What cognitive processes have been modeled using a quantum account? In addition, a brief introduction to quantum probability theory and a concrete example is provided to illustrate how a quantum cognitive model can be developed to explain paradoxical empirical findings in psychological literature

Question order effects are commonly observed in self-report measures of judgment and attitude. This article develops a quantum question order model (the QQ model) to account for four types of question order effects observed in literature. First, the postulates of the QQ model are presented. Second, an a priori, parameter-free, and precise prediction, called the QQ equality, is derived from these mathematical principles, and six empirical data sets are used to test the prediction. Third, a new index is derived from the model to measure similarity between questions. Fourth, we show that in contrast to the QQ model, Bayesian and Markov models do not generally satisfy the QQ equality and thus cannot account for the reported empirical data that support this equality. Finally, we describe the conditions under which order effects are predicted to occur, and we review a broader range of findings that are encompassed by these very same quantum principles. We conclude that quantum probability theory, initially invented to explain order effects on measurements in physics, appears to be a powerful natural explanation for order effects of self-report measures in social and behavioral sciences, too

"Rarely does a reviewer or publisher encounter a milestone: this is it. It is the first major study of the development of Chinese feminism in what is arguably the most formative period in the history of modern China. In its women-centered approach, the book challenges the official women's history authored by the Chinese Communist Party and long accepted by Euro-American scholars. This book will set the agenda for future scholars researching the relationship between feminism and nationalism in China."--Dorothy Ko, author of Teachers of the Inner Chambers.