ABSTRACT There is a growing body of literature on critical military education on the one hand, and posthuman politics and its extension to posthuman warfare on the other. This article aims to bridge these two areas of scholarship to explore possible connections between professional military education (PME) and posthumanism. Incorporating a posthuman framework into the management of organized violence through PME can help limit violence towards humans and nonhumans. We therefore focus on integrating posthuman ideals into military education. We believe that PME is one of the most effective and appropriate ways to transmit posthuman ideas to the next generation of soldiers. The article argues that this goal can be achieved by integrating posthumanism into the PME curriculum through military ethics courses and updating pedagogical methods, following an onto-epistemological and ethical turn in PME. By introducing a preliminary framework, we seek to contribute to a process that promotes the long-term survival of humans, as well as other biological and artificial life forms.

Mimicking intercellular communication by dynamically presenting signaling molecules to cells is central to understanding biological processes and building artificial life systems. However, precise replication and control of such communication at the molecular level in synthetic environments remain largely unexplored. Here, we present a polyrotaxane-based, molecular machine-like platform that regulates shuttling motions of signaling ligands within a mechanically interlocked network. This molecularly dynamic presentation of topological constrained signals directs T cell differentiation and significantly enhances therapeutic efficacy in vivo. Mechanistic studies reveal how T cells sense and transduce dynamic molecular motions into distinct intracellular responses, demonstrating that identical ligands can produce divergent outcomes depending on their molecular dynamics. These findings establish molecular motion as a critical determinant of intercellular signaling and provide a framework for engineering artificial dynamic systems that precisely control cell fate, opening new avenues for therapeutic applications based on molecular machine principles.

In the intelligent era, where technologies such as artificial intelligence, big data, and virtual reality are deeply permeating, the image expression of digital media art is undergoing a paradigm shift from "human-dominated" to "human-machine collaborative," and the aesthetic system is also undergoing a structural reconstruction. Taking the integration of intelligent technology and digital media art as a starting point, this article, drawing on practical examples of image creation, analyzes how intelligent technology reshapes the production logic, presentation form, and dissemination path of images. It explores the changes in aesthetic subjects, objects, and standards in this process, revealing the innovative directions and potential difficulties of digital media art image expression under the empowerment of technology. The study finds that generative AI and intelligent interactive technologies have not only expanded the boundaries of image expression, enabling immersive images that interweave virtual and real life, and dynamically generated personalized images, but also promoted the shift of aesthetic appreciation from "passive acceptance" to "active participation," and from "single aesthetic" to "multi-faceted symbiosis." At the same time, problems such as image homogeneity and the weakening of humanistic values caused by the misuse of technology also need to be urgently addressed. This article argues that the development of digital media art in the intelligent era must be driven by both technological empowerment and humanistic guidance. While breaking through the limitations of visual expression, a new aesthetic system combining technological innovation and spiritual depth should be constructed, providing insights for the sustainable development of digital media art.

In recent years, DNA origami technology has advanced rapidly as a groundbreaking method for nanomanufacturing. This technology takes advantage of the unique base-pairing characteristics of DNA, and has significant advantages in constructing spatially ordered and programmable nanostructures. This capability aligns with synthetic biology's core principle of mimicking, extending, and reconstructing natural biological processes by modularly assembling artificial systems. This article provides a comprehensive overview of DNA origami's innovative applications across various domains, including cell membrane surfaces, intercellular communication, intelligent biosensing, and precise gene editing, progressing from the extracellular to the intracellular environment. Finally, this review highlights the synergistic interaction between this technology and cell-free synthetic biology, achieved through the integration of in vitro assembly and cellular regulation, thereby opening new pathways for the rational design of artificial life systems.

Abstract We introduce $${\text {\texttt {openIRM}}}$$ , the Internal Risk Model of an artificial life insurer, designed to allow an easy benchmarking of nested simulation techniques for Solvency Capital Requirement ( $${{\,\textrm{SCR}\,}}$$ ) estimation under Solvency II and other actuarial methods. $${\text {\texttt {openIRM}}}$$ integrates an economic scenario generator and a cash flow projection model, enabling the computation of the available capital (basic own funds) through both the direct and indirect method. Leveraging a two-factor Gaussian model for stochastic short rates and a generalized Black-Scholes model for stock dynamics, the framework supports policyholder investments via guaranteed minimum-income benefit contracts. We extend the asset-liability management model by Diehl et al. (EAJ 13(1), 2022), and prove the theoretical convergence of the direct and indirect method under appropriate assumptions. Calibrated using interest rate caps from 2016 to the end of 2023, $${\text {\texttt {openIRM}}}$$ allows estimation of available capital distributions and $${{\,\textrm{SCR}\,}}$$ dynamics for each trading day in that range. The source code of $${\text {\texttt {openIRM}}}$$ written in is publicly available on at https://gitlab.cc-asp.fraunhofer.de/itwm-fm-lv-public/openirm . We also provide standalone executables that, after installation, can be accessed via the command line interface or with the provided wrappers in , and .

The article analyses the qualitative change in the institutional environment under the influence of the digitalisation of the economy, which is a condition and factor for the consolidation and concentration of capital as a strategic economic resource for development. At the same time, research into the nature of the origin of capital has shown that no single form of capital dominates. The successful combination of all forms of capital leads to the emergence of digital innovations, which, in turn, contribute to the formation of digital competencies at the level of economic entities and their implementation in the justification of management decisions. It is noted that these processes underlie significant changes in the promotion of innovative products and information and communication technologies in developed and developing countries. It has been proven that an unfavourable institutional environment stands out as the main reason for the slowdown in the digitalisation of the Ukrainian economy and the consolidation and concentration of business capital. Given this, there is a pressing scientific interest in the rationale, development and solutions in the digital economy with appropriate institutional support from state policy to improve the structure and quality of capital. The focus is on the peculiarities of the formation of digital business structures, in particular platform-type structures, as well as on the institutional design that determines the rules for the consolidation and concentration of capital in such structures. It has been found that digital technologies are changing the very nature of business structures and their behaviour in virtual life, with a transition to digital platforms, virtual business mobility and data exchange as a new form of capital. This is evidence that the digital economy is transferring capital and the activities of business structures from the real to the virtual world. The object of this study is the consolidation and concentration of capital, and the subject of the study is the essential characteristics of the digital economy, the institutional design that determines the rules of capital consolidation and concentration, and methods of managing them. Methods for managing the processes of capital consolidation and concentration are outlined, taking into account the specifics of the formation of digital business structures. The impact of new digital technologies on the consolidation and concentration of capital to ensure the financing of innovative renewal and restructuring of the economy is shown.

A computational perspective on rapid reacquisition: Phenotype-specific effects of reinforcement history.

Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.

Embedding of X-ray computed tomography data of cultural heritage objects in interactive web applications – old technical instruments brought back to novel virtual life

The evolutionary theory of behavior dynamics (ETBD) has predicted that under concurrent random-ratio (RR) schedules, preference for the denser schedule becomes more extreme with (a) larger differences between the concurrent ratio requirements and (b) smaller absolute values of the ratio requirement for the denser alternative. In this study, we tested ETBD's predictions by evaluating human participants' choice under various concurrent RR schedules. Sixty-three undergraduate students participated and were presented with two concurrently available response options on a touchscreen monitor. The difference between the concurrently available ratio requirements was manipulated across conditions, and the absolute value of the ratio requirement for the denser alternative was manipulated across groups. As predicted by the ETBD, participants' preference for the denser alternative increased as the difference between the concurrent ratio requirements increased and groups with smaller absolute ratio requirements tended to display more extreme preference. However, a high level of heterogeneity was observed across human participants within each group that was not evident in the behavior of artificial organisms animated by the ETBD. Our findings demonstrate the importance of focusing on individual behavior and suggest directions for future research investigating choice under concurrent ratio schedules and evaluating the ETBD.

The field of artificial life studies how life-like phenomena such as agency and self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open question is whether it is possible to find environment rules that self-organize robust “individuals” from an initial state with no prior existence of things like “bodies,” “brain,” “perception,” or “action.” Here, we leverage recent advances in machine learning, combining algorithms for diversity search, curriculum learning, and gradient descent, to automate the search of such “individuals.” We show that this approach enables us to systematically find environmental conditions in CA leading to self-organization of basic forms of agency, i.e., localized structures that move around and react in a coherent and highly robust manner to external obstacles, maintain their integrity, and have strong capabilities to generalize to new environments. We discuss how this approach opens new perspectives in artificial intelligence and synthetic bioengineering.

Gestaltology Encoded is an experimental research article in the form of an exposition, centered around the development of an artificial organism, Arcana, coming to life through the artistic research project Gestaltology (2020–2023). The project aims to explore AI and robotics in a way that challenges Cartesian dualism, by investigating the interplay between mind, body, and environment. By engaging a transdisciplinary team, the project delves into evolutionary biology, AI, and robotics, creating an artificial organism that evolves through symbiosis with its environment. Arcana’s artistic process, rooted in these conditions, emerges through its ability to create visual outputs that transcend human-centric perspectives. This research proposes a posthuman approach to AI development, emphasizing an ecocentric and performative perspective that reshapes the relationship between technology, art, and ecology. The article reflects on the project's iterative nature, where failure is viewed as an inevitable and creative catalyst, guiding the project toward unforeseen futures. keywords: Artificial Intelligence, evolutionary biology, Gestaltology, artificial creativity, transdisciplinary collaboration, posthumanism

The self-optimization (SO) model can be considered as the third operational mode of the classical Hopfield network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been argued to express characteristics of minimal agency, which renders it useful for the study of Artificial Life. In this article, we draw attention to another facet of the SO model: its capacity for creativity. Drawing on creativity studies, we argue that the model satisfies the necessary and sufficient conditions of a creative process. Moreover, we show that learning is needed to find creative outcomes above chance probability. Furthermore, we demonstrate that modifying the learning parameters in the SO model gives rise to four different regimes that can account for both creative products and inconclusive outcomes, thus providing a framework for studying and understanding the emergence of creative behaviors in artificial systems that learn.

The two-dimensional Turing machine is a promising but under used simulation tool for Artificial Life. Single-state 2-D Turing machines exhibit a variety of interesting behaviors, some of which have already been explored. Multistate 2-D Turing machines, despite their potential for simulating even more diverse behaviors, have received little attention to date. We demonstrate the potential of such automata for studying biological phenomena by showing how they can be used to simulate self-similar growth, the spread of disease, and self-reproduction. Some of the results presented here are from investigations that were performed around the time of Dewdney (1989), but they have not been published until now.

The digital era has brought about major transformations in various aspects of social, economic and cultural life. Although it provides several conveniences, this era also provides challenges in the form of the spread of hoaxes, social media addiction, and a decline in moral values, especially among the younger generation. This essay aims to examine how Pancasila can act as a social filter in dealing with the negative impacts of the digital era. The noble values in Pancasila are able to become an ethical foundation that guides people's behavior in real and virtual life.

Abstract The United States (U.S.) is a major beef producing and consuming country and increasing demand for beef is expected to apply additional pressure on non-renewable water resources. Water footprints of beef production are widely documented in the literature and are a contentious issue. The sustainability of water use in beef production is typically assessed through virtual water footprints (VWF) and life cycle assessments (LCAs). However, ongoing debates persist regarding whether water footprints should be volumetric or impact-based assessments. Distinct differences in the methodologies of VWF and LCA introduce challenges for policymakers who aim to redistribute water resources along the beef supply chain to improve sustainability. Here, we summarize the challenges and opportunities associated with water use in the U.S. beef supply chain by synthesizing findings from 71 studies spanning multiple disciplines, including geology, hydrology, water resources, sustainability, livestock production, economics, and policy from 1993 to 2024. The primary challenge identified was the lack of comparability between VWF and LCA studies. Inconsistencies in accounting for green, blue, and grey water footprints, varying functional units, and different system boundaries complicate across-study comparisons. The discrepancies observed between studies are often related to data deficiencies, requiring the implementation of some generalized assumptions. Furthermore, VWF tends to quantify water footprints on a regional or national basis, while LCAs are usually conducted locally. Consequently, resource management initiatives informed by regional-level figures may not have the desired effect on mitigating local water resource stressors. An essential step in addressing these challenges is improved data collection in the form of standardized methodology, functional units, and system boundaries to ensure targeted and meaningful data acquisition. An additional opportunity to address some of the data deficiencies is technology adoption. For example, on-farm camera sensors or stand-alone water monitoring devices can measure direct water consumption. Additionally, the installation of real-time water flow monitoring systems in beef processing plants has the potential to reduce water footprint through improved plant management. The adoption of technologies such as remote sensing and improved irrigation applicators has reduced the water footprint of crop production and, subsequently, the beef water footprint as well due to the impact of irrigation on cattle feed footprints. Continued improvements in water use efficiency and management through technology adoption, as well as the implementation of standardized methods for assessing water footprints, are critical for generating comparable data that can inform policy-making decisions in the U.S. beef industry.

The questions of how life forms, whether life is an inevitable outcome and how diverse its presentation could be remain some of the most profound in science. Investigations into the origin of life confront key issues such as uncovering key constraints and universal features of life, the plausibility of alternative biochemistries and the transition from purely chemical systems to information-bearing, evolvable entities. Many of these issues can be associated with early cell formation and evolution. Thus, protocellular systems have emerged as a key focus of study. Here, the community can ask questions about physical constraints and the co-evolution of energy, matter and information. The pursuit of these answers spans a wide range of disciplines, including geochemistry, statistical physics, systems and evolutionary biology, artificial life, synthetic biology and information theory, and reflects the inherently interdisciplinary nature of origin-of-life research. This article surveys key theoretical frameworks and experimental approaches that have shaped our current understanding, while outlining the major unresolved challenges that continue to drive the field forward. It also summarizes and contextualizes the articles in this special issue that address these questions.This article is part of the theme issue ‘Origins of life: the possible and the actual’.

Decoding herbal medicine: AI-powered omics and network pharmacology.

Evaluation of carbon emission reduction and sustainable carbon finance potential in Photovoltaic power generation by Coupling Artificial Intelligence and life cycle model

The Simulation of Adaptive Behavior (SAB) conference has a long tradition of bringing together researchers in computer science, artificial intelligence, artificial life, control, robotics, neurosciences, ethology, and evolutionary biology. Starting in 1990 and meeting every 2 years, it recently convened the 17 th meeting in Irvine, California, USA. The goal of this meeting, which is called From Animals to Animats , is to further our understanding of the behaviors and underlying mechanisms that allow natural and artificial animals to adapt and survive in uncertain environments. We asked several contributors of highly regarded presentations to submit an extended version of their work to this Special Issue of Adaptive Behavior. The articles included in this volume reflect the interdisciplinary nature of SAB, as well as the overarching theme of SAB to draw inspiration from biology.