The amorphous matter can self-organize into complex forms without external influence. In biology, the diversity of forms and their function raises the question of whether morphogenesis follows a similar self-organization process or results from the interaction between a morphogenetic field and biostructure.

We explore: 

– Self-organization and pattern formation
– Natural object geometry and properties
– Electromagnetic fields of fractal structures and their effects on living matter
– Bio-mimetic engineering, applying natural form-function studies to new technologies

Describing Earth as a living planet highlights its complex, hierarchical system of interrelated subsystems that exchange energy, matter, and information, maintaining a state of dynamic stability (Geostasis).

This perspective on the life-environment relationship and the balance between negentropic and entropic processes calls for:
– A specialised methodology
– A rethinking of investigative tools
– A reassessment of empirical observations and philosophical insights on life in context

While computers can increasingly model cognitive processes, a fundamental distinction may still separate living intelligence from artificial systems.

The brain is often viewed as a complex biochemical machine that processes sensory information to interpret reality, yet its role may extend beyond that. Understanding what sets human cognition apart remains a key question in the study of intelligence.

We focus on:

– The complexity of mental processes and how they can be replicated using artificial neural architectures
– Chaotic phenomena and nonlinear dynamics
– Stochastic resonance
– Physiological signals and how they can be measured and analysed to develop new biomedical applications

The study of nonlinear phenomena has led to a coherent set of concepts, theories, and models applicable to the analysis of complex systems. These open systems evolve far from thermodynamic equilibrium and are characterized by a hierarchical structure, exhibiting distinct behaviours at different organizational levels.

Studying the linear vs. nonlinear and complicated vs. complex relationship requires:

– Adapting experimental setups to the characteristics of complex systems.
– Modifying measurement and control instruments accordingly.
– Refining experimental protocols to capture essential system dynamics.
– Developing new methods to objectively assess system evolution in context.

The research aims to prototype new automatic and semi-automatic prediction algorithms and methodologies. As an initial focus, the team analyzes a significant dataset of sales information from the Romanian economy to enhance sales forecasting accuracy.

The study integrates classical predictive models (linear regressions, data mining, Bayesian networks, multidimensional correlation analysis) with complexity science approaches (fractal geometry, chaos theory, agent-based models, genetic algorithms, constructal theory).