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This work explores the complexity and nonlinearity of seven different colloidal suspensions-Au, ferrofluid, TiO2}, ZnO, g-C3N4, MXene, and PEDOT:PSS-when electrically stimulated with fractal, chaotic, and random binary signals. The recorded electrical responses were analyzed using entropy, file compression, fractal dimension, and Fisher information measures to quantify complexity. The nonlinearity introduced by each colloid was evaluated by the deviation of the output from the best-fit hyperplane of the input-output mapping. The results showed that TiO2 was the most complex colloid across all inputs, exhibiting high entropy, poor compressibility, and an unpredictable response pattern. The colloids also exhibited significant nonlinearity, making them promising candidates for reservoir computation, where the mapping of inputs into high-dimensional nonlinear states is advantageous. This study provides insight into the dynamics of colloids and their potential for unconventional computational applications that exploit their inherent complexity and nonlinearity, and it provides a rapid method for assessing the suitability of a particular material for use as a computational substrate before others.

There is evidence that magnetism can potentially increase the thermopower of materials, most likely due to magnon scattering, suggesting the incorporation of intrinsic magnetic semiconductors in non-magnetic thermoelectric materials. Here, samples of $\textit{p}$-type Bi$_{0.5}$Sb$_{1.5}$Te$_{3}$ with 10 at.% excess Te are ball-milled with varying ratio of the antiferromagnetic semiconductor CrSb (0, 0.125, 0.5, and 1 wt.%) to prepare bulk samples by spark plasma sintering technique. The thermopower of samples containing CrSb is increased due to an increase in the effective mass of the charge carriers, indicating that there is a drag effect originating from the magnetic particles. However, this was at the expense of reduced electrical conductivity caused by reduced charge carrier mobility. While overall only marginal improvements in power factors were observed, these samples exhibited significantly lower thermal conductivity compared to the single-phase material. As a result, a peak $\textit{zT}$ value of $\sim$1.4 was achieved at 325 K for the sample with 0.125 wt.% CrSb. These results highlight the potential of incorporating magnetic secondary phases to enhance the thermoelectric performance of materials.

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from the colloids. We analyse the space-time complexity of Boolean circuits derived from the electrical responses of colloids: ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and combinations thereof to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and thereby infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata (arrays of binary state machines that update their states synchronously according to the same rule) -- the colloid cellular automata. We then analyse the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

Liquid computers use incompressible fluids for computational processes. Here we present experimental laboratory prototypes of liquid computers using colloids composed of zinc oxide (ZnO) nanoparticles and microspheres containing thermal proteins (proteinoids). The choice of proteinoids is based on their distinctive neuron-like electrical behaviour and their similarity to protocells. In addition, ZnO nanoparticles are chosen for their non-trivial electrical properties. Our research demonstrates the successful extraction of 2-, 4- and 8-bit logic functions in ZnO proteinoid colloids. Our analysis shows that each material has a distinct set of logic functions, and that the complexity of the expressions is directly related to each material present in a mixture. These findings provide a basis for the development of future hybris liquid devices capable of general purpose computing.

In this paper, we discuss a method to find the most influential power system parameters to the probabilistic transient stability assessment problem---finding the probability distribution of the critical clearing time. We perform the parameter selection by employing a sensitivity analysis combined with a principal component analysis. First, we determine the sensitivity of the machine angles with respect to all system parameters. Second, we employ the principal component analysis algorithm to identify the most influential parameters in the transient stability problem. By identifying such parameters, we can reduce the number of uncertain parameters to only the influential ones in the probabilistic assessment of transient stability, providing a significant speed-up in the probabilistic analysis of large power systems. The proposed algorithm was tested in the IEEE 14 bus systems and the results obtained show that our method can effectively find the most influential parameters., Comment: 5 pages