Bio-inspired Information and Communication Technologies

In this paper, we focus in an artificial mechanism to detain the beginning of the type-2 diabetes disease in those identified patients which might to be developing a phase of prediabetes. From purely electrical interactions or Coulomb forces between a deployed nano sensor around of beta cells and Calcium\(^{2+}\) ions, we propose an artificial entrance of Calcium ions inside the beta-cells allowing them to segregate insulin. The electrical interactions between positively charged insulin inside beta cells is the main assumption of this paper. The permanent segregation of insulin fits well inside of the architecture of advanced networks engineering that contemplates the usage of a bio cyber interface. Therefore, the artificial releasing of granules with repulsive electric forces of insulin becomes a manner to cheat beta cells. This might be also seen as an option to avoid the intake of prediabetes y diabetes pharmacology for large periods. Although the view of this work is theoretical and prospective, it is based entirely in closed-form physics equations that sustain the main claim of this paper: electric interactions driven by charged nano particles would be a window to stop the progress of diseases based on the induced or spontaneous deficit of proteins, hormones and cells that are crucially needed to maintain the human homeostasis.

Commonly the type-2 diabetes complications are imminent in those organs where is a substantial dependence of the microvascularity such as for instance the renal apparatus that it might be substantially affected. One of the points related to this is the degradation of the kidney functions fact that is accompanied without any symptomatology or some signals that allow the identification of the beginning of the so-called diabetes kidney disease. It is noteworthy that for large periods, clinicians have reported that type-2 diabetes patients might be potential candidates to use the dialysis machines. Therefore, to attack the problem of how to tackle down the beginning of the renal disease in type-2 diabetes would require to understand the phenomenon that is carried out in the kidney, particularly in the renal glomerulus, or glomerulus in short. In this paper we take advantage of the physics-based phenomenology to develop closed-form expressions that would describe the different scenarios by which the glomerulus is invaded by giant proteins like the albumin. Under this scenario, albumin proteins that are pushed out by the glucose dipoles in blood are expected to exert repulsion as well as attraction forces inside the glomerulus. Thus, there is a large probability as to expect that the departure of the bunches of albumin from sensitive microvascularity inside the kidneys can reach the Bowman’s space and the urine formation zone. In this paper we present a study of the physics interactions inside the renal glomerulus. Essentially we use physics equations to derive the laws that govern the pass of proteins such as albumin through the layers of glomerulus. Once the physics equations are established the albumin excretion ratio is estimated. Basically, proteins do interact with glomerulus through the remaining charges along the layers and podocytes. This is crucial to determine the volume of albumin that goes to the Bowmam’s space. Our study uses physics equations inside of the framework of charge electric density. The fact of measure accurately the quantity of excreted albumin with physics equations, provides capabilities to apply precise strategies in the side of the clinicians to improve the treatment in the cases where there is a potential risk to acquire the well-known diabetes kidney disease. All these methodologies encompass with the prospective Internet of Bio-Nano Things that engages an Internet network with human organs in order to anticipate any eventual abnormality or wrong functionality of organs in real-time.

The possibility of cyberattacks in Networked Control Systems (NCS), along with the growing use of networked controllers in industry and critical infrastructures, is motivating studies about the cybersecurity of these systems. The literature on cybersecurity of NCSs indicates that accurate and covert model-based attacks require high level of knowledge about the models of the attacked system. In this sense, recent works recognize that Bio-inspired System Identification (BiSI) attacks can be considered an effective tool to provide the attacker with the required system models. However, while BiSI attacks have obtained sufficiently accurate models to support the design of model-based attacks, they have demonstrated loss of accuracy in the presence of noisy signals. In this work, a noise processing technique is proposed to improve the accuracy of BiSI attacks in noisy NCSs. The technique is implemented along with a bio-inspired metaheuristic that was previously used in other BiSI attacks: the Backtracking Search Optimization Algorithm (BSA). The results indicate that, with the proposed approach, the accuracy of the estimated models improves. With the proposed noise processing technique, the attacker is able to obtain the model of an NCS by exploiting the noise as a useful information, instead of having it as a negative factor for the performance of the identification process.

With the ever increasing number of insider attacks (data breaches) and security incidents it is evident that the traditional manual and standalone access control models for cyber-security are unable to defend complex and large organizations. The new access control models must focus on auto-resiliency, integration and fast response-time to timely react against insider attacks. To meet these objectives, even after decades of development of cyber-security systems, there still exist inherent limitations (i.e., understanding of behavioral anomalies) in current cyber-security architecture that allow adversaries to not only plan and launch attacks effectively but also learn and evade detection easily. In this research we propose a bio-inspired integrated access control policy regulation framework which not only allows us to understand anomalous behavior of an insider but also provides theoretical background to link behavioral anomalies to the access control regulation. To demonstrate the effectiveness of our proposed framework we use real-life threat dataset for the evaluation purposes.

In his pioneering paper on neuromorphic systems, Carver Mead conveyed that: “Biological information-processing systems operate on completely different principles from those with which most engineers are familiar” (Mead 1990: 1629). This paper challenges his assertion. While honoring Mead’s exceptional contributions, specific purposes, and correct conclusions, I will use a different line of argumentation. I will make use of a debate on the classification and ordering of natural phenomena to illustrate how background notions of causality permeate particular theories in science, as in the case of cognitive brain sciences. This debate shows that failures in accounting for concrete scientific phenomena more often than not arise from (1) characterizations of the architecture of nature, (2) singular conceptions of causality, or (3) particular scientific theories – and not rather from (4) technology limitations per se. I aim to track the basic bio-inspiration and show how it spreads bottom-up throughout (1) to (4), in order to identify where bioinspiration started going wrong, as well as to point out where to intervene for improving technological implementations based on those bio-inspired assumptions.

For solving multi-objective optimization problems with evolutionary algorithms, the decomposing the Pareto front by using a set of weight vectors is a promising approach. Although an appropriate distribution of weight vectors depends on the Pareto front shape, the uniformly distributed weight vector set is generally employed since the shape is unknown before the search. This work proposes a simple way to control the weight vector distribution appropriate for several Pareto front shapes. The proposed approach changes the distribution of the weight vector set based on the intermediate objective vector in the objective space. A user-defined parameter determines the intermediate objective vector in the static method, and the objective values of the obtained solutions dynamically determine the intermediate objective vector in the dynamic method. In this work, we focus on MOEA/D as a representative decomposition-based multi-objective evolutionary algorithm and apply the proposed static and dynamic methods for it. The experimental results on WFG test problems with different Pareto front shapes show that the proposed static and dynamic methods improve the uniformity of the obtained solutions for several Pareto front shapes and the dynamic method can find an appropriate intermediate objective vector for each Pareto front shape.

Commonly used computational and analytical tools for fitness landscape analysis of optimization problems require identifying a distance metric that characterizes the similarity of different solutions to the problem. For example, fitness distance correlation is Pearson correlation between solution fitness and distance to the nearest optimal solution. In this paper, we survey the available distance metrics for permutations, and use principal component analysis to classify the metrics. The result is aligned with existing classifications of permutation problem types produced through less formal means, including the A-permutation, R-permutation, and P-permutation types, and has also identified subtypes. The classification can assist in identifying appropriate metrics based on optimization problem feature for use in fitness landscape analysis. Implementations of all of the permutation metrics, and the code for our analysis, are available as open source.

We utilize a type of encryption scheme known as a Fully Homomorphic Encryption (FHE) scheme which allows for computation over encrypted data. Our encryption scheme is more efficient than other publicly available FHE schemes, making it more feasible. We conduct simulations based on common scenarios in which this ability is useful. In the first simulation we conduct time series analysis via Recursive Least Squares on both encrypted and unencrypted data and compare the results. In simulation one, it is shown that the error from computing over plaintext data is the same as the error for computing over encrypted data. In the second simulation, we compute two known diagnostic functions over publicly available data in order to calculate computational benchmarks. In simulation two, we see that computation over encrypted data using our method incurs relatively lower costs as compared to a majority of other publicly available methods. By successfully computing over encrypted data we have shown that our FHE scheme permits the use of machine learning algorithms that utilize polynomial kernel functions.

Evolutionary robotics is an approach for optimizing a robotic control system and structure based on the bio-inspired mechanism of adaptiogenesis. Conventional evolutionary robotics assigns a task and an evaluation to a virtual robot and acquires an optimal control system. In many cases, however, the robot is composed of a few rigid primitives and the morphology imitates that of real animals, insects, and artifacts. This paper proposes a novel approach to evolutionary robotics combining morphological evolution and soft robotics to optimize the control system of a soft robot. Our method calculates the relational dynamics among morphological changes and autonomous behavior for neuro-evolution (NE) with the development of a complex soft-bodied robot and the accomplishment of multiple tasks. We develop a soft-bodied robot composed of heterogeneous materials in two stages: a development stage and a locomotion stage, and we optimize these robotic structures by combining an artificial neural network (ANN) and age-fitness pareto optimization (AFP). These body structures of the robot are determined depending on three genetic rules and some voxels for evolving the ANN. In terms of our experimental results, our approach enabled us to develop some adaptive structural robots that simultaneously acquire behavior for crawling both on the ground and underwater. Subsequently, we discovered an unintentional morphology and behavior (e.g., walking, swimming, and crawling) of the soft robot through the evolutionary process. Some of the robots have high generalization ability with the ability to crawl to any target in any direction by only learning a one-directional crawling task.

Precision agriculture represents a very promising domain for swarm robotics, as it deals with expansive fields and tasks that can be parallelised and executed with a collaborative approach. Weed monitoring and mapping is one such problem, and solutions have been proposed that exploit swarms of unmanned aerial vehicles (UAVs). With this paper, we move one step forward towards the deployment of UAV swarms in the field. We present the implementation of a collective behaviour for weed monitoring and mapping, which takes into account all the processes to be run onboard, including machine vision and collision avoidance. We present simulation results to evaluate the efficiency of the proposed system once that such processes are considered, and we also run hardware-in-the-loop simulations which provide a precise profiling of all the system components, a necessary step before final deployment in the field.

As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the thermodynamic and kinetics based models used to describe traditional bulk phase active syntheses. Like the statistical mechanics underpinning these latter theories, the framework we propose uses state probabilities as its fundamental objects; but, contrary to the Gibbsian paradigm, our theory directly models the transition probabilities between the initial and final states of a trajectory, foregoing the need to assume ergodicity. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We demonstrate the application of this framework to a simple toy model in which three identical molecules can assemble in one of two ways and conclude with a discussion of how the high computational cost of such a fine-grained model can be overcome through approximation when extending it to larger, more complex systems.

Many bacteria dwell in micro-habitats, e.g., animal or plant tissues, waste matter, and soil. Consequently, bacterial space searching and partitioning is critical to their survival. However, the vast majority of studies regarding the motility of bacteria have been performed in open environments. To fill this gap in knowledge, we studied the behaviour of E. coli K12-wt in microfluidic channels with sub-10 µm dimensions, which present two types of geometries, namely a diamond-like network and a maze. The velocity, average time spent, and distance required to exit the networks, have been calculated to assess the intelligent-like behaviour of bacteria.

Certain species of bacteria are capable of communicating through a mechanism called Quorum Sensing (QS) wherein they release and sense signaling molecules, called autoinducers, to and from the environment. Despite stochastic fluctuations, bacteria gradually achieve coordinated gene expression through QS, which in turn, help them better adapt to environmental adversities. Existing sequential approaches for modeling information exchange via QS for large cell populations are time and computational resource intensive, because the advancement in simulation time becomes significantly slower with the increase in molecular concentration. This paper presents a scalable parallel framework for modeling multicellular communication. Simulations show that our framework accurately models the molecular concentration dynamics of QS system, yielding better speed-up and CPU utilization than the existing sequential model that uses the exact Gillespie algorithm. We also discuss how our framework accommodates evolving population due to cell birth, death and heterogeneity due to noise. Furthermore, we analyze the performance of our framework vis-á-vis the effects of its data sampling interval and Gillespie computation time. Finally, we validate the scalability of the proposed framework by modeling population size up to 2000 bacterial cells.

MCA (Membrane computing aggregation is experimental computational frame. It is inspired by the inner properties of membrane cells (Bio-inspired system). It is capable of problem solving activities by maintaining a special, “meaningful” relationship with the internal/external environment, integrating its self-reproduction processes within the information flow of incoming and outgoing signals. Because these problem solving capabilities, MCA admits a crucial evolutionary tuning by mutations and recombination of theoretical genetic “bridges” in a so called “aggregation” process ruled by a hierarchical factor that enclosed those capabilities. Throughout the epigenetic capabilities and the cytoskeleton and cell adhesion functionalities, MCA model gain a complex population dynamics specifics and high scalability. Along its developmental process, it can differentiate into meaningful computational tissues and organs that respond to the conditions of the environment and therefore “solve” the morphogenetic/configurational problem. MCA, above all, represents the potential for a new computational paradigm inspired in the higher level processes of membrane cells, endowed with quasi universal processing capabilities beyond the possibilities of cellular automata of and agent processing models.