Multi-Armed Droplets as Shape-Changing Protocells

Emergence in Artificial Life

Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement "life is complex." Thus understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understanding living systems? Artificial Life (ALife) has been developed in recent decades to study life using a synthetic approach: Build it to understand it. ALife systems are not so complex, be they soft (simulations), hard (robots), or wet(protocells). Thus, we can aim at first understanding emergence in ALife, to then use this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but present at another. This perspective avoids problems of studying emergence from a materialist framework and can also be useful in the study of self-organization and complexity.

Vibrational spectroscopic analysis of critical micelle concentration in sodium decanoate solutions

The presented study is devoted to the investigation of the micellization-induced liquid-liquid fluctuations in sodium decanoate (NaD) aqueous solutions, based on the vibrational spectroscopic study of NaD and the determination of critical micelle concentration (CMC) of this system. At the same time, we focused on monitoring the effect of the addition of decanol to this system and changing its basic parameters from the point of view of CMC. CMC is an important parameter from a practical point of view and a characteristic feature of each micelle-forming compound. Upon analyzing the spectroscopic data we focus our attention mainly on the intensity and band position variations of both the symmetrical and antisymmetrical vibrational modes of CH₂ groups situated in the high-frequency part of the spectrum. The study used normal (non-enhanced) Raman spectroscopy with excitation wavelength 785 nm, surface-enhanced Raman spectroscopy (SERS) on large-scaled gold-coated SERS-active substrates and infrared spectral measurements. The results of spectroscopic measurements were supported by tensiometry and potentiometry.

Self-Organization and Artificial Life

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.