Search results

The purpose of this conceptual paper is to demonstrate how memes perpetuate trauma with a schematic. This conceptual paper uses the “begin with the end in mind” meme to format the presentation of the necessary components for the schematic of how trauma persists across generations. It is the third paper in a series of applications of the recursive, test-operate-test schematic to the systemic effects of the information processes involved in trauma. The schematic presented permits evaluations of solutions to interrupt the generational trauma cycle.

The required schematic components are described. Trauma (actual or perceived threat to survival) will be briefly discussed. Evolutionary processes that create the psychophysiology necessary to support nominal social expectations (NSEs) memes and metaphors will be summarized. The development of NSEs will be discussed. Metaphors and memes necessary for the creation of the schematic and esoteric events at level Learning IV will be briefly described. Finally, Figure 3, which illustrates the maintenance of NSEs and attempts to prevent their violation, will be explained.

It is asserted that functional human social behavior requires NSEs. Trauma is found to persist through the presence of anti-nominal NSE memes that are transduced by the individual into anti-nominal metaphors, which then damage grid, place and dentate gyrus cell (GPDG) neurophysiology. The damaged neurocircuits allow the use of anti-nominal NSE metaphors within our individual neurophysiology. Furthermore, anti-NSE memes interfere with the self-organized criticalities (SOCs) of genetic-epigenetic processes necessary for the intergenerational transfer of functional social behavior. When anti-NSE nominal metaphors are transduced back into anti-NSEs, social niche memes, the trauma process is reiterated. Anti-NSE memes and metaphors are found to be inappropriate criteria central to the maintenance of persistent trauma. Therefore, anti-NSE memes have hijacked our epigenetics and our social niches. Solutions are available because during our evolution, the Homo clade developed esoteric capabilities and the ability to bring back what information we can from those encounters. This physiology operates around the 5HT2A neural receptors that process hallucinogens, such as psilocybin. Accessing this resource system, either through naturally occurring altered states of consciousness or through micro-dose pharmaceutical psilocybin and related neurotransmitters, produces a significant structural change in the GPDG system to reset the NSE system illustrated in the schematic to its nominal status so that we can maintain nominal NSE relationships within our meme niche(s).

The source of persistent trauma in our social niche(s), the means by which the trauma is maintained and the means to mitigate and perhaps eliminate persistent trauma are identified. Based on these three conclusions, it is difficult to make decisions regarding corrective actions because of ubiquitous anti-NSE memes and because of the limitations of our ordinary consciousness.

If we wish to survive as a species, we will need to discover the criteria necessary to maintain our niche(s) congruent SOCs and use them instead of tyrannical memes described by Dawkins (1989) to make decisions about ourselves and our niche(s).

Significant courage is required to identify the memes that maintain trauma because many of them are culturally sacred cows. Unfortunately, we have known since Bremner's (1995) MRI study of posttraumatic stress disorder that trauma causes brain damage. Fortunately, our NSE genes compel us to pursue restitution of the memes that maintain our trauma-inducing cultures.

Several original assertions are presented. While the Homo clade ancestors began the creation of the social niche(s) that led to Homo sapiens sapiens, it is asserted that the australopiths created the NSE memes which are the foundation behaviors that permit our social niche(s). Furthermore, NSEs were produced by enhanced intentionality skills and NSEs were created by both genetic and memetic processes. The evolution of intentionality-NSE neural networks is asserted as the source of intentional material manipulation. While anti-NSE memes are claimed as the source of persistent trauma, the practice of esoteric technologies is presented as a solution to persistent trauma.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The call for a new paradigm in politics and governance has become a planetary imperative. Humanity is at a critical juncture; unless we mature as a species and become net-positive to nature the human experiment may (soon) end. We have become our own biggest threat. This chapter explores the foundations, as well as systemic barriers, for the shift to a new and life-centred paradigm in politics and governance. Offering a systemic exploration of the root causes of our sustainability crises and how to address this, based on the cosmology and evolutionary principles of complex living systems. Applying Living Systems Protocols from the EARTHwise Constitution for a Planetary Civilization, and its framework of five Future Archetypes, for developing our transformative capacities to address the systemic thrivability barriers of mechanistic systems and worldviews. With case-study examples of new paradigm tools, systems and technologies that enable a decentralization of governance and democratization of ownership. As such empowering the systemic conditions and maturation pathways for a thriving planetary civilization. The chapter completes with a brief practice for developing our future human capacities and inner consciousness shifts for a new paradigm in politics and governance.

Influencer marketing mediated by social media is prevalent in social commerce. Micro-, meso- and macro-influencers all play an irreplaceable role in marketing. The purpose of this paper is to explore how companies with limited budgets choose influencers according to products' various levels of brand familiarity.

This study constructs an evolutionary game model of influencer marketing based on evolutionary game theory on complex networks. This model initiates various networks to demonstrate how influencers disseminate information and constructs update mechanisms to depict how individuals react to this information based on individuals' information utility and friends' strategies.

Simulation results suggest that companies should invest more in macro-influencers than in meso-influencers, however investing all in macro-influencers is not a good choice. The investment in meso-influencers will increase as brand familiarity decreases, whereas it will not exceed investment in macro-influencers. Furthermore, the accumulation of micro-influencers can accelerate the marketing process.

This study examines the combined effects of micro-influencers, meso-influencers and macro-influencers in marketing by simulating the marketing process initiated by influencers on social media.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

How does the interaction between time and knowledge affect the evolution of organizations? Past research in organizational evolution has mostly investigated time and knowledge as two separate variables. In contrast, theoretical perspectives integrating these variables are still seemingly scant. The authors believe that filling this literature gap needs attention. Thus, this study aims to contribute by developing a conceptual framework.

This is a conceptual study. The framework is centred on the concept of “co-evolutionary time”, which the authors explain through a business example from the tourism industry. Supported by a narrative-based style, from a methodological point of view the framework is featured by the attempt to synthesize specific, extant literature into new theoretical development.

As its main theoretical contribution, the co-evolutionary time suggests how firms can adapt in a way that, from an evolutionary perspective, proves fitting both in terms of contents and methods, thus opening possibilities for new long-term social construction and reconstruction. As its main practical contribution, co-evolutionary time can constitute not only a temporary source of organizational success and competitive advantage but also an agent of enduring change and long-term business survival.

As its main novelty, the framework is developed through merging two literature streams. In particular, the authors first consider the literature about time, with a focus on its objective and subjective dimensions. The authors then consider the literature about organizational evolution, with a focus on the co-evolutionary nature of the firm/environment relationship.

The COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.

Based on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.

The research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government’s punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.

The study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.

To the best of the authors’ knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

This chapter presents findings from a qualitative study conducted among students from Germany, Hungary, Portugal, France, and Poland on their expectations and experiences of learning in two types of international mobility (physical and virtual) during the pandemic of 2020. The authors identified imprints of internationalization onto the students’ learning. They are of a binary nature and manifest themselves in the following dualities: Duality 1: Expectations (new life experiences) versus reality requirements (empowerment); Duality 2: Formal learning (at university) versus non-formal learning (outside formal environments as a part of daily life); Duality 3: Designing (the digital environment) versus reconstruction (the “old order” of university learning); Duality 4: Latitudes (choice of method, place, and time of learning) versus restrictions (staying at home and family responsibilities). Moreover, internationalization through student mobility programs simultaneously show features of some revolutionary changes and evolutionary transformations that have come about during the COVID-19 pandemic.

Healthcare education is a huge industry with a significant social footprint and resilient impact on well-being and on the quality of life. It integrates diverse scientific domains and needs to continuously update its value proposition to reflect the need for preparing top-quality health professionals. It also has to support professional development and to manage effectively the accreditation of programs and the certification of skills and knowledge. In this chapter, the authors expand a theoretical framework about Active and Transformative Learning (ATL) that has introduced in the volume of ATL for STEAM disciplines and also discussed how artificial intelligent (AI) tools, such as OPENAI Chat GPT, can serve as transformers and value carriers for the implementation of ATL activities and use cases in healthcare education.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.