Bio-inspired Approach Research Articles

Biocompatible neem gum-modified polyvinyl alcohol composite as dielectric material for flexible energy devices

In our pursuit of a flexible energy storage solution, we have developed biocompatible (bc)-NG/PVA composite polymers by combining neem tree gum (NG) with polyvinyl alcohol (PVA). This innovative bio-inspired approach harnesses NG's unique properties for both the bio-electrolyte and bio-electrode components. The resulting bc-NG/PVA composites exhibit superior dielectric strength and versatility, surpassing traditional inorganic ceramic dielectrics in advanced electronics and pulsed power systems. Our study investigates the dielectric characteristics, conductivities, electric modulus, and impedance parameters of Pure PVA and NG-doped PVA composites. Adding 5 % NG to PVA significantly boosts its conductivity from 10−8 S cm−1 to 10−4 S cm−1, while the dielectric constant of PVA/5 % NG composite jumps to 104.5 compared to pure PVA. These improvements position the composite films of 5 % NG added PVA as promising materials for diverse applications. The heightened performance of these NG-blended PVA composite materials underscores their potential as a valuable resource for flexible energy storage solutions.

Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation

Insects are good examples of ground locomotion because they can adapt their gait pattern to propel them in any direction, over uneven terrain, in a stable manner. Nevertheless, replicating such locomotion skills to a legged robot is not a straightforward task. Different approaches have been proposed to synthesize the gait patterns for these robots; each approach exhibits different restrictions, advantages, and priorities. For the purpose of this document, we have classified gait pattern generators for multi-legged robots into three categories: precomputed, heuristic, and bio-inspired approaches. Precomputed approaches rely on a set of precalculated motion patterns obtained from geometric and/or kinematic models that are performed repeatedly whenever necessary and that cannot be modified on-the-fly to adapt to the terrain changes. On the other hand, heuristic and bio-inspired approaches offer on-line adaptability, but parameter-tuning and heading control can be difficult. In this document, we present the K3P algorithm, a real-time kinematic gait pattern generator conceived to command a legged robot. In contrast to other approaches, K3P enables the robot to adapt its gait to follow an arbitrary trajectory, at an arbitrary speed, over uneven terrain. No precomputed motions for the legs are required; instead, K3P modifies the motion of all mechanical joints to propel the body of the robot in the desired direction, maintaining a tripod stability at all times. In this paper, all the specific details of the aforementioned algorithm are presented, as well as different simulation results that validate its characteristics.

Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach

In recent years, the power quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonics level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work, the Spider Monkey Optimization convolutional neural network (SM-CNN)-based 31-level multilevel inverter (MLI) is used. This method balances the reactive power demands and enhances real power in the grid-tied photovoltaic (PV) system. The maximum power point tracking (MPPT) algorithm depending on radial basis function neural networks (RBFNNs) is used to maximize PV power. For strengthening the voltage level of the PV and to generate higher DC voltage with a minimized switching loss, an integrated boost fly back converter (IBFC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the total harmonic distortion (THD) value to 2.45% with an improved power factor.

Hybrid parallel photonic reservoir computing with accelerated data processing speed

Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. In this work, we propose a hybrid reservoir computing scheme based on parallel photonic time-delay reservoirs with optical injection. The state of virtual nodes are evaluated from the combination of temporal outputs of parallel lasers. It is demonstrated that our proposed hybrid scheme enables photonic reservoir computer with shortened time interval θ and less virtual nodes number N, which could break through the limitation of short external cavity length in miniaturization of reservoir computing with photonic integrated circuits. The computational efficiency of photonic reservoir is numerically evaluated by chaotic time-series prediction task and memory capacity test. State-of-the-art speed performances reach 20 GSa/s with prediction error rate below 0.01. Our scheme bridges the areas of photonic integrated circuits and photonic information processing.

Bioinspired Nanozymes as Nanodecoys for Urinary Tract Infection Treatment.

Urinary tract infections (UTIs), common bacterial infections in communities and medical facilities, are mainly mediated by FimH. The glycan sites of the uromodulin protein play a crucial role in protecting against UTIs by interacting with FimH. A bioinspired approach using glycan-FimH interactions may effectively reduce bacteria through an antiadhesive mechanism, thereby curbing bacterial resistance. However, typical antiadhesive therapy alone fails to address the excessive reactive oxygen species and inflammatory response during UTIs. To bridge this gap, antioxidant nanozymes with antiadhesive ability were developed as nanodecoys to counter bacteria and inflammation. Specifically, ultrasmall dextran-coated ceria (DEC) was engineered to address UTIs, with dextran blocking FimH adhesion and ceria exhibiting anti-inflammatory properties. DECs, metabolizable by the kidneys, reduced bacterial content in the urinary tract, mitigating inflammation and tissue damage. In murine models, DECs successfully treated acute UTIs, repeated infections, and catheter-related UTIs. This dual approach not only highlights the potential of nanozymes for UTIs but also suggests applicability to other FimH-induced infections in the lungs and bowels, marking a significant advancement in nanozyme-based clinical approaches.

Bio-Inspired Jumping Spider Optimization for Controller Tuning/ Parameter Estimation of an Uncertain Aerodynamic MIMO System

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others (optimization methods).

Iterative optimisation in dynamic environments via local pursuit

We discuss an iterative optimal control approach in which a multi-agent group of control systems discovers locally optimal trajectories in environments with time-varying terrain geometry and moving, deforming obstacles. Works in literature have addressed only special cases of this problem with time-invariant terrain geometry. One such bio-inspired optimal control approach, applicable in static environments, is ‘local pursuit’, a method that iteratively solves partially-constrained final state problems by having agents follow one another, each agent moving optimally towards its predecessor. We show that if, instead, agents evolve in their own time-delayed ‘copy’ of the environment and act based on the most recent time history of their predecessors local neighbourhoods, then the original method can be extended to the dynamic environments described above. Furthermore, we discuss the computational complexity of our approach and demonstrate its effectiveness and computational advantages in a series of simulations.

The Preparation of Curcumin-Loaded Pickering Emulsion Using Gelatin-Chitosan Colloidal Particles as Emulsifier for Possible Application as a Bio-Inspired Cosmetic Formulation.

In the field of preparing cosmetic formulations, recent advances recommend the usage of excipients derived from biocompatible materials. In this context, the present study aimed to prepare and characterize the curcumin-loaded Pickering emulsion for possible applications in cosmetic formulation. The coconut oil which is often the component of skin care formulations is used as the oily phase. Curcumin, which is well known for absorbing solar radiation, is expected to work synergistically with coconut oil towards improving the sun protection factor (SPF) of the formulation. Additionally, curcumin can also protect the intracellular components through its well-known antioxidant mechanisms. The Pickering emulsion of coconut oil into water was prepared using the composite colloidal particles derived from β-carboxymethyl chitosan (CMC) and Gelatin-A (GA) as the emulsifying agent. The reaction conditions in terms of the weight ratios of CMC and GA, the pH of the reaction medium, the oil volume fraction, and the homogenization speed were optimized to obtain the most stable Pickering emulsion. The obtained systems were physico-chemically characterized by dynamic light scattering, zeta potential, optical microscopy, and rheometric measurements. The final CMC-GA-stabilized emulsion demonstrated an oil droplet size of 100 µm and a SPFspectrophotometric (290-320 nm) value of 8.5 at a curcumin loading of 4 mg/mL. Additionally, the final formulation facilitated the uptake of curcumin into fibroblast (WI26) cells under in vitro conditions. Together, the investigation demonstrates a bio-inspired approach to prepare a curcumin-loaded green Pickering emulsion using biocompatible pharmaceutical grade excipients, which may find utility in cosmetic applications.

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.

Bioinspired stormwater control measure for the enhanced removal of truly dissolved polycyclic aromatic hydrocarbons and heavy metals from urban runoff

Stormwater harvesting (SWH) addresses the UN's Sustainable Development Goals (SDGs). Conventional stormwater control measures (SCMs) effectively remove particulate and colloidal contaminants from urban runoff; however, they fail to retain dissolved contaminants, particularly substances of concern like polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs), thereby hindering the SWH applicability. Here, inspired by protein folding in nature, we reported a novel biomimetic SCM for the efficient removal of dissolved PAHs and HMs from urban runoff. Lab-scale tests were conducted together with a more mechanistic investigation on how the contaminants were removed. By integrating hydrophobic organic chains with low-cost hydrophilic flocculant matrixes, our biomimetic flocculants achieved a 1.4–9.5 times removal of all detected dissolved PAHs and HMs, while enhancing the removal of a wide-spectrum of particulate and colloidal contaminants, compared to existing SCMs. Ecotoxicity, as indicated by newborn Daphnia magna as experimental organisms, was reduced from “acute toxicity” of the original runoff sample (toxic unit of ∼2.6) to “non-toxicity” (toxic unit < 0.4) of the treated water. The improved performance is attributed to the protein-folding-like features of the bioinspired flocculants providing: (i) stronger binding to PAHs (via hydrophobic association) and HMs (via coordination), and (ii) the ability of spontaneous aggregation. The bio-inspired approach in this work holds strong promise as an alternative or supplementary component in SCM systems, and is expected to contribute to sustainable water management practices in relation to SDGs.

A Bio-Inspired Approach to Improve the Toughness of Brittle Bast Fibre-Reinforced Composites Using Cellulose Acetate Foils.

Bast fibre-reinforced plastics are characterised by good strength and stiffness but are often brittle due to the stiff and less ductile fibres. This study uses a biomimetic approach to improve impact strength. Based on the structure of the spicules of a deep-sea glass sponge, in which hard layers of bioglass alternate with soft layers of proteins, the toughness of kenaf/epoxy composites was significantly improved by a multilayer structure of kenaf and cellulose acetate (CA) foils as impact modifiers. Due to the alternating structure, cracks are deflected, and toughness is improved. One to five CA foils were stacked with kenaf layers and processed to composite plates with bio-based epoxy resin by compression moulding. Results have shown a significant improvement in toughness using CA foils due to increased crack propagation. The unnotched Charpy impact strength increased from 9.0 kJ/m2 of the pure kenaf/epoxy composite to 36.3 kJ/m2 for the sample containing five CA foils. The tensile and flexural strength ranged from 74 to 81 MPa and 112 to 125 MPa, respectively. The tensile modulus reached values between 9100 and 10,600 MPa, and the flexural modulus ranged between 7200 and 8100 MPa. The results demonstrate the successful implementation of an abstract transfer of biological role models to improve the toughness of brittle bast fibre-reinforced plastics.

Indigenous Bacteria-Driven Eco-Friendly Soil Stabilization: A Bioinspired Approach Through Microbially Induced Calcite Precipitation for Sustainable Infrastructure Development

Indigenous Bacteria-Driven Eco-Friendly Soil Stabilization: A Bioinspired Approach Through Microbially Induced Calcite Precipitation for Sustainable Infrastructure Development

An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings.

A hybrid bio-inspired sandwich structures for high strain rate energy absorption applications

Due to its advantages in terms of enhancing the performance of structures in the desired applications, the bio-inspired design approach has recently attracted the interest of researchers in a number of engineering disciplines. A hybrid bio-inspired design is suggested for the sandwich structures to absorb the energy of the blast loads in the current study. The sandwich structure's core, which often has a regular grid pattern resembling a honeycomb structure, is crucial to how well the panel absorbs energy. In order to achieve the best results, we first chose the structure of the core grid by taking into account potential 2D grids (polygons and multi-pointed stars) through Genetic Algorithm optimization. Next, we combined a bio-inspired bi-tubular thin-walled structure with the core grid to take advantage of its high energy absorption capacity. Finally, the performance of the suggested design is compared with four frequently implemented ones. The results show that the hybrid design has better energy absorption characteristics compared with the bionic and conventional designs presented in the literature.

Scalable Task Allocation with Communications Connectivity for Flying Ad-Hoc Networks

Task allocation enables heterogeneous agents to execute heterogeneous tasks in the domain of unmanned aerial vehicles, while responding to dynamic changes in the environment and available resources to complete complex, multi-objective missions, leading to swarm intelligence. We propose a bio-inspired approach using digital pheromones to perform scalable task allocation when the number of agents, tasks, and the diameter of the communications graph increase. The resulting emergent behaviour also enables idle agents in the swarm to provide periodic or continuous connectivity between disconnected parts of the swarm. We validate our results through simulation and demonstrate the feasibility of our approach by applying it to the 3D coverage and patrol problem.

A knowledge graph-based bio-inspired design approach for knowledge retrieval and reasoning

Bio-inspired Design (BID) is a method that draws principles from biological systems to solve complex real-world problems. While diverse knowledge-based tools have served BID, the retrieval and reasoning capabilities of knowledge graphs have not been explored in BID. This study introduces a novel knowledge graph-based BID approach, exploiting the power of knowledge graphs to support BID. In the approach, a comprehensive ontology is defined and then applied to construct a BID-specific knowledge graph, enabling efficient representation of the diverse and rich biological knowledge. The knowledge graph supports BID by facilitating knowledge retrieval and reasoning. Retrieval in BID is accomplished by finding potential links between biological systems and relevant design applications. Reasoning in BID is supported by a link prediction model that follows the design process of mapping from biological systems to design applications. Two case studies are conducted to demonstrate the effectiveness of the approach. The first case shows that our approach outperforms other benchmarks in retrieving related biological knowledge, and the second case presents how the link prediction model aids in generating relevant and inspirational design ideas.

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques.

Biomimetic Approach for Sustainable Magnetite Nanoparticle Synthesis Using Polycations

AbstractMagnetotactic bacteria produce magnetite nanoparticles called magnetosomes at ambient conditions via a protein‐stabilized transient amorphous precursor to obtain precise control over particle size and morphology. In a bioinspired approach, such biomineralization processes are emulated, mimicking proteins involved in magnetosome formation using the positively charged analog poly‐L‐arginine. While the additive is expensive, it remains elusive whether the change in magnetite formation mechanism arises solely from the polymer's cationic nature. This study uses different mass‐produced and sustainably sourced polycations to induce the biomineralization‐reminiscent formation of magnetite nanoparticles. These findings present how to achieve control over nanoparticle size (from 10 to 159 nm) and morphology (compact and sub‐structured) as well as magnetic properties (superparamagnetic, stable‐single‐domain, vortex state) at ambient temperature and pressure using these additives. Furthermore, the formation of large nanoparticles with the addition of poly(diallyldimethylammonium chloride) (PDADMAC) at low alkalinity highlights how magnetotactic bacteria may produce magnetite nanoparticles under similar conditions. Confirming the polycations' ability to electrostatic stabilize amorphous ferrihydrite, it is anticipated that parametric in vitro studies on polymer properties will provide valuable insights into magnetite biomineralization and aid in rationally designing magnetic nanomaterials.

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

AbstractWith more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.

Smart self-defensive coatings with bacteria-triggered antimicrobial response for medical devices.

Bacterial colonization and biofilm formation on medical devices represent one of the most urgent and critical challenges in modern healthcare. These issues not only pose serious threats to patient health by increasing the risk of infections but also exert a considerable economic burden on national healthcare systems due to prolonged hospital stays and additional treatments. To address this challenge, there is a need for smart, customized biomaterials for medical device fabrication, particularly through the development of surface modification strategies that prevent bacterial adhesion and the growth of mature biofilms. This review explores three bioinspired approaches through which antibacterial and antiadhesive coatings can be engineered to exhibit smart, stimuli-responsive features. This responsiveness is greatly valuable as it provides the coatings with a controlled, on-demand antibacterial response that is activated only in the presence of bacteria, functioning as self-defensive coatings. Such coatings can be designed to release antibacterial agents or change their surface properties/conformation in response to specific stimuli, like changes in pH, temperature, or the presence of bacterial enzymes. This targeted approach minimizes the risk of developing antibiotic resistance and reduces the need for continuous, high-dose antibacterial treatments, thereby preserving the natural microbiome and further reducing healthcare costs. The final part of the review reports a critical analysis highlighting the potential improvements and future evolutions regarding antimicrobial self-defensive coatings and their validation.