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Macroalgae seaweeds such as Ulva lactuca and Gracilaria verrucosa cause problems on the northern coast of the Italian Adriatic Sea because their overabundance hinders the growth of cultivated clams, Rudatapes philippinarum. This study focused on the green synthesis of CuO nanoparticles from U. lactuca and G. verrucosa. The biosynthesized CuO NPs were successfully characterized using FTIR, XRD, HRTEM/EDX, and zeta potential. Nanoparticles from the two different algae species are essentially identical, with the same physical characteristics and almost the same antimicrobial activities. We have not investigated the cause of this identity, but it seems likely to arise from the reaction of Cu with the same algae metabolites in both species. The study demonstrates that it is possible to obtain useful products from these macroalgae through a green synthesis approach and that they should be considered as not just a cause of environmental and economic damage but also as a potential source of income.

Recent technological advances have made it possible to produce tiny robots equipped with simple sensors and effectors. Micro-robots are particularly suitable for scenarios such as exploration of hostile environments, and emergency intervention, e.g., in areas subject to earthquakes or fires. A crucial desirable feature of such a robot is the capability of adapting to the specific environment in which it has to operate. Given the limited computational capabilities of a micro-robot, this property cannot be achieved by complicated software but it rather should come from the flexibility of simple control mechanisms, such as the sensory–motor loop. In this work, we explore the possibility of equipping simple robots controlled by Boolean networks with the capability of modulating their sensory–motor loop such that their behavior adapts to the incumbent environmental conditions. This study builds upon the cybernetic concept of homeostasis, which is the property of maintaining essential parameters inside vital ranges, and analyzes the performance of adaptive mechanisms intervening in the sensory–motor loop. In particular, we focus on the possibility of maneuvering the robot’s effectors such that both their connections to network nodes and environmental features can be adapted. As the actions the robot takes have a feedback effect to its sensors mediated by the environment, this mechanism makes it possible to tune the sensory–motor loop, which, in turn, determines the robot’s behavior. We study this general setting in simulation and assess to what extent this mechanism can sustain the homeostasis of the robot. Our results show that controllers made of random Boolean networks in critical and chaotic regimes can be tuned such that their homeostasis in different environments is kept. This outcome is a step towards the design and deployment of controllers for micro-robots able to adapt to different environments.

The conditions that allow for the sustained growth of a protocell population are investigated in the case of asymmetrical division. The results are compared to those of previous studies concerning models of symmetrical division, where synchronization (between duplication of the genetic material and fission of the lipid container) was found under a variety of different assumptions about the kinetic equations and about the place where molecular replication takes place. Such synchronization allows a sustained proliferation of the protocell population. In the asymmetrical case, there can be no true synchronization, since the time to duplication may depend upon the initial size, but we introduce a notion of homogeneous growth that actually allows for the sustained reproduction of a population of protocells. We first analyze Surface Reaction Models, defined in the text, and we show that in many cases they undergo homogeneous growth under the same kinetic laws that lead to synchronization in the symmetrical case. This is the case also for Internal Reaction Models (IRMs), which, however, require a deeper understanding of what homogeneous growth actually means, as discussed below.

The paper presents the Line Start Synchronous Reluctance Motor (LSSynRM) technology as a promising avenue to bring IE3 motor portfolios to the IE4 efficiency class at contained manufacturing costs. Indeed, the achieving of IE4 efficiency class is concerning many motor manufacturers, where the small-medium manufacturers seem to face the higher efforts in converting their product portfolio. To this extent, a methodology to improve the IE3 Induction Motor (IM) efficiency by introducing the Line Start Synchronous Reluctance motor technology in the IM motor is presented. The new motor design guidelines and constraints are figured out to reduce the impact on the manufacturing process. The paper presents a detailed experimental validation of the realized prototype in steady-state and transient operation referring to a target IE3 IM. The prototype proved to be a cost-effective, mass production-ready solution for super-premium efficiency IE4 motors. The LSSynRM technology demonstrated to be an effective high-efficiency avenue at reduced manufacturing costs in a broad panorama of low-inertia industrial applications.

Research on the effects of engineered nanomaterials (ENMs) on mitochondria, which represent one of the main actors in cell function, highlighted effects on ROS production, gametogenesis and organellar genome replication. Specifically, the mitochondrial effects of cadmium sulfide quantum dots (CdS QDs) exposure can be observed through the variation in enzymatic kinetics at the level of the respiratory chain and also by analyzing modifications of reagent and products in term of the bonds created and disrupted during the reactions through Fourier-transform infrared spectroscopy (FTIR). This study investigated both in intact cells and in isolated mitochondria to observe the response to CdS QDs treatment at the level of electron transport chain in the wild-type yeast Saccharomyces cerevisiae and in the deletion mutant Δtom5, whose function is implicated in nucleo–mitochondrial protein trafficking. The changes observed in wild type and Δtom5 strains in terms of an increase or decrease in enzymatic activity (ranging between 1 and 2 folds) also differed according to the genetic background of the strains and the respiratory chain functionality during the CdS QDs treatment performed. Results were confirmed by FTIR, where a clear difference between the QD effects in the wild type and in the mutant strain, Δtom5, was observed. The utilization of these genetic and biochemical approaches is instrumental to clarify the mitochondrial mechanisms implicated in response to these types of ENMs and to the stress response that follows the exposure.

The Theory of the Adjacent Possible (TAP) equation has been proposed as an appropriate description of super-exponential growth phenomena, where a phase of slow growth is followed by a rapid increase, leading to a “hockey stick” curve. This equation, initially conceived to describe the growth in time of the number of new types of artifacts, has also been applied to several natural phenomena. A possible drawback is that it may overestimate the number of new artifact types, since it does not take into account the fact that interactions, among existing types, may produce types which have already been previously discovered. We introduce here a Binary String World (BSW) where new string types can be generated by interactions among (at most two) already existing types. We introduce a continuous limit of the TAP equation for the BSW; we solve it analytically and show that it leads to divergence in finite time. We also introduce a criterion to distinguish this type of behavior from the familiar exponential growth, which diverges only as t → ∝. In the BSW, it is possible to directly model the generation of new types, and to check whether the newborns are actually novel types, thus discarding the rediscoveries of already existing types. We show that the type of growth is still TAP-like, rather than exponential, although of course in simulations one never can observes true divergence. We also show that this property is robust with respect to some changes in the model, as long as it deals with types (and not with individuals).

Hyperaccumulator plant species growing on metal-rich soils can accumulate high quantity of metals and metalloids in aerial tissues, and several proteomic studies on the molecular mechanisms at the basis of metals resistance and hyperaccumulation have been published. Hyperaccumulator are also at the basis of the phytoremediation strategy to remove metals more efficiently from polluted soils or water. Arabidopsis halleri and Noccea caerulescens are both hyperaccumulators of metals and nano-metals. In this study, the change in some proteins in A. halleri and N. caerulescens was assessed after the growth in soil with cadmium and zinc, provided as sulphate salts (CdSO4 and ZnSO4) or sulfide quantum dots (CdS QDs and ZnS QDs). The protein extracts obtained from plants after 30 days of growth were analyzed by 2D-gel electrophoresis (2D SDS-PAGE) and identified by MALDI-TOF/TOF mass spectrometry. A bioinformatics analysis was carried out on quantitative protein differences between control and treated plants. In total, 43 proteins resulted in being significatively modulated in A. halleri, while 61 resulted in being modulated in N. caerulescens. Although these two plants are hyperaccumulator of both metals and nano-metals, at protein levels the mechanisms involved do not proceed in the same way, but at the end bring a similar physiological result.

In the last decades, nanotechnology-based tools have attracted attention in the scientific community, due to their potential applications in different areas from medicine to engineering, but several toxicological effects mediated by these advanced materials have been shown on the environment and human health. At present, the effects of engineered nanomaterials on gametogenesis have not yet been well understood. In the present study, we addressed this issue using the yeast Saccharomyces cerevisiae as a model eukaryote to evaluate the effects of cadmium sulfide quantum dots (CdS QDs) on sporulation, a process equivalent to gametogenesis in higher organisms. We have observed that CdS QDs cause a strong inhibition of spore development with the formation of aberrant, multinucleated cells. In line with these observations, treatment with CdS QDs down-regulates genes encoding crucial regulators of sporulation process, in particular, the transcription factor Ndt80 that coordinates different genes involved in progression through the meiosis and spore morphogenesis. Down-regulation of NDT80 mediated by CdS QDs causes a block of the meiotic cell cycle and a return to mitosis, leading to the formation of aberrant, multinucleated cells. These results indicate that CdS QDs inhibit gametogenesis in an irreversible manner, with adverse effects on cell-cycle progression.

This paper investigates the impact of the wire selection on the performance of induction motors for automotive applications. The section of wire and the material are evaluated at a high speed of 200 kW in an induction motor designed for premium vehicle applications. The proposed solutions have the same electromagnetic and thermal constraints, as well as the same final encumbrance. The various wire and winding types differ in terms of slot design, phase resistance, end-winding overhanging portion, skin and proximity effects, and equivalent slot thermal conductivity. Their impacts are analyzed in terms of the operating area motor efficiency and they are tested in an automotive drive cycle, highlighting the advantages and disadvantages of each configuration.

The data included in this paper are associated with a research article entitled 'Differences in toxicity, mitochondrial function and miRNome in human cells exposed in vitro to Cd as CdS quantum dots or ionic Cd' [1]. The article concerns the use of miRNAs as biomarkers for engineered nanomaterials (ENMs) risk assessment. Two different type of human cells, HepG2 and THP-1, were exposed to different forms of Cadmium: nanoscale, as CdS quantum dots (CdS QDs), and ionic, as CdSO4 8/3 –hydrate (Cd(II)). The cells were treated with sub-toxic doses of CdS QDs; 3 µg ml−1 in HepG2 and 6.4 µg ml−1 and 50 µg ml−1 in THP-1, as well as equivalent cadmium doses as Cd(II). In this dataset, changes in expression levels of miRNAs are reported. In addition, GO enrichment analyses of target genes of miRNAs modulated by Cd stress, network analysis of the microRNome and an in silico pathway analysis are also reported. These data enhance and also summarize much of the data independently presented in the research article and therefore, must be considered as supplementary.

Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena. We define the “common sea” (CS) as the set of nodes that take the same value in all the attractors of a given network realization, and the “specific part” (SP) as the set of all the other nodes, and we study their properties in different ensembles, generated with different parameter values. Both the CS and of the SP can be composed of one or more weakly connected components, which are emergent intermediate-level structures. We show that the study of these sets provides very important information about the behavior of the model. The distribution of distances between attractors is also examined. Moreover, we show how the notion of a “common sea” of genes can be used to analyze data from single-cell experiments.

In many complex systems one observes the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its better understanding. We have developed in the past a powerful method to achieve this goal, which however requires a heavy computational cost in several real-world cases. In this work we introduce a modified version of our approach, which reduces the computational burden. The design of the new algorithm allowed the realization of an original suite of methods able to work simultaneously at the micro level (that of the binary relationships of the single variables) and at meso level (the identification of dynamically relevant groups). We apply this suite to a particularly relevant case, in which we look for the dynamic organization of a gene regulatory network when it is subject to knock-outs. The approach combines information theory, graph analysis, and an iterated sieving algorithm in order to describe rather complex situations. Its application allowed to derive some general observations on the dynamical organization of gene regulatory networks, and to observe interesting characteristics in an experimental case.

The Axial Flux Permanent Magnet (AFPM) motor represents a valid alternative to the traditional radial flux motor due to its compact structure; it is suitable for in-wheel applications so that the transmission gear can be suppressed. The modeling of the motor is a purely Three-Dimensional (3D) problem and the use of 3D finite element tools allows the attainment of accurate results taking also into account the effects of the end-windings. Moreover, a 3D multiphysics analysis is essential to evaluate not only the motor performance and its thermal behavior, but also the electromagnetic forces acting on the surfaces of the stator teeth and of the magnets that face the air gap. Moreover, as the vehicle’s motors often work in variable-speed conditions, the prediction of vibrations and noise for electric motors over a wide speed range is usually necessary. The paper presents a double-sided AFPM motor for a small pure electric vehicle; the basic drive architecture includes four axial flux motors installed directly inside the vehicle’s wheels. The aim is to propose advanced and integrated electromagnetic, vibroacoustic and thermal analyses that allow the investigation of the axial flux motor behavior in a detailed and exhaustive way.

In previous work, two independent Arabidopsis thaliana Ac/Ds transposon insertional mutant lines, atnp01 and atnp02, were identified that showed a higher level of tolerance than the wild type (wt) line to cadmium sulfide quantum dots (CdS QDs). The tolerance response was characterized at physiological, genetic and transcriptomic levels. In this work, a comparative analysis was performed on protein extracts from plantlets of the two mutants and of wt, each treated with 80 mg L−1 CdS QDs. A comparative protein analysis was performed by 2D-PAGE, and proteins were characterized by MALDI-TOF/TOF mass spectrometry. Of 250 proteins identified from all three lines, 98 showed significant changes in relative abundance between control and CdS QD-treated plantlets. The wt, atnp01, and atnp02 control-treated pairs respectively showed 61, 31, and 31 proteins with differential expression. The two mutants had a different response to treatment in terms of type and quantity of up- and downregulated proteins. This difference became more striking when compared to wt. A network analysis of the proteins differentially expressed in atnp01 and atnp02 included several of those encoded by putative genes accommodating the transposons, which were responsible for regulation of some proteins identified in this study. These included nifu-like protein 3 (Nfu3), involved in chloroplast assembly, elongator complex 3 (Elo3), involved in transcriptional elongation, magnesium-chelate subunit-2 (Chli2), involved in chlorophyll biosynthesis, and protein phosphatase 2C (PP2C) which mediates abiotic stress response.

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.

The properties of most systems composed of many interacting elements are neither determined by the topology of the interaction network alone, nor by the dynamical laws in isolation. Rather, they are the outcome of the interplay between topology and dynamics. In this paper, we consider four different types of systems with critical dynamic regime and with increasingly complex dynamical organization (loosely defined as the emergent property of the interactions between topology and dynamics) and analyze them from a structural and dynamic point of view. A first noteworthy result, previously hypothesized but never quantified so far, is that the topology per se induces a notable increase in dynamic organization. A second observation is that evolution does not change dramatically the size distribution of the present dynamic groups, so it seems that it keeps track of the already present organization induced by the topology. Finally, and similarly to what happens in other applications of evolutionary algorithms, the types of dynamic changes strongly depend upon the used fitness function.

The use of cadmium sulphide quantum dot (CdS QD)-enabled products has become increasingly widespread. The prospect of their release in the environment is raising concerns. Here we have used the yeast model Saccharomyces cerevisiae to determine the potential impact of CdS QD nanoparticles on living organisms. Proteomic analyses and cell viability assays performed after 9 h exposure revealed expression of proteins involved in oxidative stress and reduced lethality, respectively, whereas oxidative stress declined, and lethality increased after 24 h incubation in the presence of CdS QDs. Quantitative proteomics using the iTRAQ approach (isobaric tags for relative and absolute quantitation) revealed that key proteins involved in essential biological pathways were differentially regulated over the time course of the experiment. At 9 h, most of the glycolytic functions increased, and the abundance of the number of heat shock proteins increased. This contrasts with the situation at 24 h where glycolytic functions, some heat shock proteins as well as oxidative phosphorylation and ATP synthesis were down-regulated. It can be concluded from our data that cell exposure to CdS QDs provokes a metabolic shift from respiration to fermentation, comparable to the situation reported in some cancer cell lines.

Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). The reasons why this should be the case and experimental evidence are briefly discussed, observing that gene regulatory networks are indeed often found on, or close to, the critical boundaries. Therefore, assuming that criticality provides an edge, it is important to ascertain whether systems that are critical can further evolve while remaining critical. In order to explore the possibility of achieving such “always-critical” evolution, we resort to simulated evolution, by suitably modifying a genetic algorithm in such a way that the newly-generated individuals are constrained to be critical. It is then shown that these modified genetic algorithms can actually develop critical gene regulatory networks with two interesting (and quite different) features of biological significance, involving, in one case, the average gene activation values and, in the other case, the response to perturbations. These two cases suggest that it is often possible to evolve networks with interesting properties without losing the advantages of criticality. The evolved networks also show some interesting features which are discussed.

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to states that are dynamically critical. In previous papers, we showed that a well-known model of genetic regulatory networks, namely, that of random Boolean networks, allows one to study in depth the relationship between the dynamical regime of a living being’s gene network and its response to permanent perturbations. In this paper, we analyze a huge set of new experimental data on single gene knockouts in S. cerevisiae, laying down a statistical framework to determine its dynamical regime. We find that the S. cerevisiae network appears to be slightly ordered, but close to the critical region. Since our analysis relies on dichotomizing continuous data, we carefully consider the issue of an optimal threshold choice.

Systems that exhibit complex behaviours often contain inherent dynamical structures which evolve over time in a coordinated way. In this paper, we present a methodology based on the Relevance Index method aimed at revealing the dynamical structures hidden in complex systems. The method iterates two basic steps: detection of relevant variable sets based on the computation of the Relevance Index, and application of a sieving algorithm, which refines the results. This approach is able to highlight the organization of a complex system into sets of variables, which interact with one another at different hierarchical levels, detected, in turn, in the different iterations of the sieve. The method can be applied directly to systems composed of a small number of variables, whereas it requires the help of a custom metaheuristic in case of systems with larger dimensions. We have evaluated the potential of the method by applying it to three case studies: synthetic data generated by a nonlinear stochastic dynamical system, a small-sized and well-known system modelling a catalytic reaction, and a larger one, which describes the interactions within a social community, that requires the use of the metaheuristic. The experiments we made to validate the method produced interesting results, effectively uncovering hidden details of the systems to which it was applied.

In this paper a new model of growing and dividing protocells is described, whose main features are (i) a lipid container that grows according to the composition of the molecular milieu (ii) a set of “genetic memory molecules” (GMMs) that undergo catalytic reactions in the internal aqueous phase and (iii) a set of stochastic kinetic equations for the GMMs. The mass exchange between the external environment and the internal phase is described by simulating a semipermeable membrane and a flow driven by the differences in chemical potentials, thereby avoiding to resort to sometimes misleading simplifications, e.g., that of a flow reactor. Under simple assumptions, it is shown that synchronization takes place between the rate of replication of the GMMs and that of the container, provided that the set of reactions hosts a so-called RAF (Reflexive Autocatalytic, Food-generated) set whose influence on synchronization is hereafter discussed. It is also shown that a slight modification of the basic model that takes into account a rate-limiting term, makes possible the growth of novelties, allowing in such a way suitable evolution: so the model represents an effective basis for understanding the main abstract properties of populations of protocells.

The growth of a population of protocells requires that the two key processes of replication of the protogenetic material and reproduction of the whole protocell take place at the same rate. While in many ODE-based models such synchronization spontaneously develops, this does not happen in the important case of quadratic growth terms. Here we show that spontaneous synchronization can be recovered (i) by requiring that the transmembrane diffusion of precursors takes place at a finite rate, or (ii) by introducing a finite lifetime of the molecular complexes. We then consider reaction networks that grow by the addition of newly synthesized chemicals in a binary polymer model, and analyze their behaviors in growing and dividing protocells, thereby confirming the importance of (i) and (ii) for synchronization. We describe some interesting phenomena (like long-term oscillations of duplication times) and show that the presence of food-generated autocatalytic cycles is not sufficient to guarantee synchronization: in the case of cycles with a complex structure, it is often observed that only some subcycles survive and synchronize, while others die out. This shows the importance of truly dynamic models that can uncover effects that cannot be detected by static graph theoretical analyses.

The aim of this work was to use the yeast Saccharomyces cerevisiae as a tool for toxicogenomic studies of Engineered Nanomaterials (ENMs) risk assessment, in particular focusing on cadmium based quantum dots (CdS QDs). This model has been exploited for its peculiar features: a short replication time, growth on both fermentable and oxidizable carbon sources, and for the contextual availability of genome wide information in the form of genetic maps, DNA microarray, and collections of barcoded mutants. The comparison of the whole genome analysis with the microarray experiments (99.9% coverage) and with the phenotypic analysis of 4688 barcoded haploid mutants (80.2% coverage), shed light on the genes involved in the response to CdS QDs, both in vivo and in vitro. The results have clarified the mechanisms involved in the exposure to CdS QDs, and whether these ENMs and Cd2+ exploited different pathways of response, in particular related to oxidative stress and to the maintenance of mitochondrial integrity and function. Saccharomyces cerevisiae remains a versatile and robust alternative for organismal toxicological studies, with a high level of heuristic insights into the toxicology of more complex eukaryotes, including mammals.

Over the last years, analyses performed on a stochastic model of catalytic reaction networks have provided some indications about the reasons why wet-lab experiments hardly ever comply with the phase transition typically predicted by theoretical models with regard to the emergence of collectively self-replicating sets of molecule (also defined as autocatalytic sets, ACSs), a phenomenon that is often observed in nature and that is supposed to have played a major role in the emergence of the primitive forms of life. The model at issue has allowed to reveal that the emerging ACSs are characterized by a general dynamical fragility, which might explain the difficulty to observe them in lab experiments. In this work, the main results of the various analyses are reviewed, with particular regard to the factors able to affect the generic properties of catalytic reactions network, for what concerns, not only the probability of ACSs to be observed, but also the overall activity of the system, in terms of production of new species, reactions and matter.

In this work we introduce some preliminary analyses on the role of a semi-permeable membrane in the dynamics of a stochastic model of catalytic reaction sets (CRSs) of molecules. The results of the simulations performed on ensembles of randomly generated reaction schemes highlight remarkable differences between this very simple protocell description model and the classical case of the continuous stirred-tank reactor (CSTR). In particular, in the CSTR case, distinct simulations with the same reaction scheme reach the same dynamical equilibrium, whereas, in the protocell case, simulations with identical reaction schemes can reach very different dynamical states, despite starting from the same initial conditions.

ZnO nanotetrapods have recently been exploited for the realization of high-sensitivity gas sensors, but they are affected by the typical drawbacks of metal-oxides, i.e., poor selectivity and a relatively high working temperature. On the other hand, it has been also demonstrated that the combined use of nanostructured metal oxides and organic molecules can improve the gas sensing performance sensitivity or selectivity, even at lower temperatures. A gas sensor device, based on films of interconnected ZnO nanotetrapods properly functionalized by titanyl phthalocyanine (TiOPc), has been realized in order to combine the high surface to volume ratio and structural stability of the crystalline ZnO nanostructures with the enhanced sensitivity of the semiconducting TiOPc molecule, especially at low temperature. The electronic properties of the resulting nanohybrid material are different from those of each single component. The response of the hybrid nanostructure towards different gases has been compared with that of ZnO nanotetrapod without functionalization in order to highlight the peculiar properties of the hybrid interaction(s). The dynamic response in time has been studied for different gases and temperatures; in particular, an increase in the response to NO2 has been observed, even at room temperature. The formation of localized p-n heterojunctions and the possibility of exchanging charge carriers at the hybrid interface is shown to be crucial for the sensing mechanism.

The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality.

In the sensors field the active sensing material frequently needs a controlled temperature in order to work properly. In microsystems technology, micro-machined hotplates represent a platform consisting of a thin suspended membrane where the sensing material can be deposited, usually integrating electrical stimuli and temperature readout. The micro-hotplate ensures a series of advantages such as miniaturized size, fast response, high sensitivity, low power consumption and selectivity for chemical sensing. This work compares the coplanar and the buried approach for the micro-hotplate heaters design with the aim to optimize the fabrication process and to propose a guideline for the choice of the suitable design with respect to the applications. In particular, robust Finite Element Method (FEM) models are set up in order to predict the electrical and thermal behavior of the micro-hotplates. The multiphysics approach used for the simulation allows to match as close as possible the actual device to the predictive model: geometries, materials, physics have been carefully linked to the fabricated devices to obtain the best possible accuracy. The materials involved in the fabrication process are accurately selected in order to improve the yield of the process and the performance of the devices. The fabricated micro-hotplates are able to warm the active region up to 400 °C (with a corresponding power consumption equal to 250 mW @ 400 °C) with a uniform temperature distribution in the buried micro-hotplate and a controlled temperature gradient in the coplanar one. A response time of about 70 ms was obtained on the virtual model, which perfectly agrees with the one measured on the fabricated device. Besides morphological, electrical and thermal characterizations, this work includes reliability tests in static and dynamic modes.

A mathematical model is proposed which is able to describe the most important features of cell differentiation, without requiring specific detailed assumptions concerning the interactions which drive the phenomenon. On the contrary, cell differentiation is described here as an emergent property of a generic model of the underlying gene regulatory network, and it can therefore be applied to a variety of different organisms. The model points to a peculiar role of cellular noise in differentiation and leads to non trivial predictions which could be subject to experimental testing. Moreover, a single model proves able to describe several different phenomena observed in various differentiation processes.