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Bioinspired skin adhesives are promising alternatives to traditional glue‐based skin patches for use in various skin‐attachable devices. However, the typical nano‐/microfabrication of these bioinspired microstructures provides limited manufacturing freedom over their performance, constraining their versatility and customization for widespread use. In this study, a hybrid 3D printing approach for fabricating a permeable skin patch is developed with adhesive miniaturized octopus‐like suckers (AMOS) for biosignal monitoring. The design and geometrical influences are investigated on the adhesion strength under different skin conditions (i.e., dry, wet, and hairy), permeability, and durability as well as proposed a biocompatible, stretchable, highly permeable, reusable, and conformable biopatch. Unlike commercially available biopatches that use chemical adhesives, the adhesion strength of an AMOS patch is customizable, does not cause skin irritation, and is comfortable for the wearer when worn for extended durations. The biopatch is tested in electromyogram, electrooculogram, and electrocardiogram signal measurement during a hand‐cycling experiment over extended durations that confirms its potential in extreme sport events. In addition to biosignal monitoring, 3D‐printed AMOS patches have broad applicability across various skin‐interfacing devices, including glucose and sweat monitoring, drug delivery, and wound healing. This approach offers a promising and highly customizable manufacturing solution for unlocking next‐generation skin‐attachable technologies. [ABSTRACT FROM AUTHOR]

Biomimetic materials have become a promising alternative in the field of tissue engineering and regenerative medicine to address critical challenges in wound healing and skin regeneration. Skin-mimetic materials have enormous potential to improve wound healing outcomes and enable innovative diagnostic and sensor applications. Human skin, with its complex structure and diverse functions, serves as an excellent model for designing biomaterials. Creating effective wound coverings requires mimicking the unique extracellular matrix composition, mechanical properties, and biochemical cues. Additionally, integrating electronic functionality into these materials presents exciting possibilities for real-time monitoring, diagnostics, and personalized healthcare. This review examines biomimetic skin materials and their role in regenerative wound healing, as well as their integration with electronic skin technologies. It discusses recent advances, challenges, and future directions in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

The built environment faces significant challenges in managing energy demands amidst rising temperatures and increasing concerns linked to climate change. Meeting carbon emissions targets and resource management goals necessitates urgent innovation in more energy-efficient cooling solutions. Nature offers a large database of adaptive and efficient thermal solutions that can be harnessed through biomimetic methods in building design and systems. Emerging biomimetic and computational approaches hold promise in facilitating practical application efforts. This paper investigates the translation of morphological features from elephant skin to building facades, optimizing their inherent cooling capabilities through computational design using evolutionary algorithms. Through this exploration, we propose a set of generalized evolutionary principles, offering a foundational framework for the development of textured facade tiles with the aim of mitigating heat gain from solar radiation. This study provides an in-depth analysis of how assembly, texture depth, and orientation impact thermal performance, enabling the design of more effective passive cooling systems through an understanding of the relationship between morphological variations in textured surfaces and environmental performance. Future research may involve studying natural convection dynamics, optimizing capillary networks, evaluating materials for water adhesion and cleaning, assessing impacts on biological growth, and exploring biodiversity integration within textured facade panels. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman's test post-hocked by Holm's test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems. [ABSTRACT FROM AUTHOR]

Articular cartilage is devoid of nerves and blood vessels, and its nutrients must be obtained from the joint fluid; therefore, its ability to repair itself is limited. Manufactured materials such as artificial cartilage or synthetic materials are typically used in traditional approaches for knee cartilage repair. However, durability, postimplant rejection, and tissue incompatibility are the problems associated with these materials. In recent decades, tissue engineering and regenerative medicine have focused on the development of functional substitutes, particularly those based on naturally inspired biopolymers. This review focuses on sustainably produced biopolymers based on materials derived from natural sources. Furthermore, these materials have many advantages, including low antigenicity, biocompatibility, and degradability. Of course, there are also many challenges associated with natural materials, such as the lack of clinical studies and long‐term follow‐up data, unstable mechanical properties of the materials, and high demands placed on preparation and molding techniques. In this review, an overview of natural and nature‐inspired polymers that are the subject of research to date, as well as their structural designs and product performances is provided. This review provides scientific guidance for enhancing the development of naturally inspired materials for treating cartilage injuries. [ABSTRACT FROM AUTHOR]

Biomimicry is the application of existing features in nature to human technologies, such as the invention of aircraft inspired by bird flight. In the development of medical solutions, biomimicry is a growing field of research, where a holistic understanding of nature can inspire cutting-edge design. The purpose of this study was to create an educational, visual resource exemplifying up-and-coming medical applications of biomimicry. A website was created to present 2D motion graphics (animations) and illustrations. Animation is an established and useful method of communicating health information to the public. This presents an accessible interface for the public to interact with and learn about this area of research, bridging the gap between the two. Increasing public knowledge, engagement, and interest can expand the reach and thereby influence future research. A survey was conducted to assess public engagement and opinions on both the resource and the topic of biomimicry and medical design. The results suggested that participants positively engaged with the resource; 95.7% strongly agreed/agreed that the animations were beneficial for learning. All responding participants agreed that biomimicry could provide useful solutions in medical design. This study suggests that graphic motions are effective at communicating complex ideas for public outreach. [ABSTRACT FROM AUTHOR]

The built environment faces significant challenges in managing energy demands amidst rising temperatures and increasing concerns linked to climate change. Meeting carbon emissions targets and resource management goals necessitates urgent innovation in more energy-efficient cooling solutions. Nature offers a large database of adaptive and efficient thermal solutions that can be harnessed through biomimetic methods in building design and systems. Emerging biomimetic and computational approaches hold promise in facilitating practical application efforts. This paper investigates the translation of morphological features from elephant skin to building facades, optimizing their inherent cooling capabilities through computational design using evolutionary algorithms. Through this exploration, we propose a set of generalized evolutionary principles, offering a foundational framework for the development of textured facade tiles with the aim of mitigating heat gain from solar radiation. This study provides an in-depth analysis of how assembly, texture depth, and orientation impact thermal performance, enabling the design of more effective passive cooling systems through an understanding of the relationship between morphological variations in textured surfaces and environmental performance. Future research may involve studying natural convection dynamics, optimizing capillary networks, evaluating materials for water adhesion and cleaning, assessing impacts on biological growth, and exploring biodiversity integration within textured facade panels.

The electrocatalytic reduction reaction of carbon dioxide (CO2RR) has gained significant attention as a promising approach to mitigate carbon dioxide emissions and generate valuable chemicals and fuels. However, the practical application of CO2RR has been hindered by the lack of efficient and selective electrocatalysts, particularly to produce multi‐carbon (C2+) products. Nature serves as an ideal source of inspiration for the development of CO2RR electrocatalysts, as biological organisms can efficiently catalyze the same reaction and possess robust structures that are inherently scaling. In this review, recent advances in the nature‐inspired design of electrocatalysts for CO2RR to C2+ products are summarized and categorized based on their inspiration source, including the coordination sphere of metalloenzymes and the cascade reactions within the enzyme, as well as the local environment. The importance of understanding the fundamental mechanisms and the different contexts between nature and technological application in the design process is highlighted, with the aim to improve the nature‐inspired design of electrocatalysts for CO2RR to C2+ products. [ABSTRACT FROM AUTHOR]

Biomimetic materials have become a promising alternative in the field of tissue engineering and regenerative medicine to address critical challenges in wound healing and skin regeneration. Skin-mimetic materials have enormous potential to improve wound healing outcomes and enable innovative diagnostic and sensor applications. Human skin, with its complex structure and diverse functions, serves as an excellent model for designing biomaterials. Creating effective wound coverings requires mimicking the unique extracellular matrix composition, mechanical properties, and biochemical cues. Additionally, integrating electronic functionality into these materials presents exciting possibilities for real-time monitoring, diagnostics, and personalized healthcare. This review examines biomimetic skin materials and their role in regenerative wound healing, as well as their integration with electronic skin technologies. It discusses recent advances, challenges, and future directions in this rapidly evolving field.

Slip and Fall (SF) on slippery icy/snowy surfaces during winters is evident worldwide, especially in Nordic regions. Every year millions of people slip and fall due to ice accretion on the roads, streets, and pavements causing traumatic injuries, loss of limbs, and sometime loss of lives, costing billions in hospitals and recovery. An efficient anti-slipping winter shoe-sole could prevent these accidents and save lives. Footwear industries came up with solutions such like crampons, cleats, anti-skidding materials and tread pattern designs, but with limited success because of their ineffectiveness on wet ice, quick rate of wearing. The inspiration from nature like polar bear, seal, arctic fox, penguin, snake, octopus, frog, and gecko where this problem is elegantly solved through evolution process can address these limitations and design advanced anti-slippery surfaces. The review presents a comprehensive understanding of biological designs of the footpads (polar bear, penguin, arctic fox, frog, gecko) and skins (seal, snake, octopus’ suction cups) and recent progress on their translation for practical applications. The review emphasises on the mechanisms of icy slippery surfaces and the contact surfaces (shoe-sole and ice/snow) to mimic anti-slipping mechanism of animals and their movement on ice enabling to design the finest anti-slipping winter shoe-soles.

This article offers an efficient isosceles layout model for node deployment, and a parameter-less Jaya algorithm is also proposed as a solution to the sensor node localization issue in wireless sensor networks (WSNs). The proposed method bases its range measuring on the receive signal strength indicator (RSSI) method. The proposed layout ensures that target nodes are always within the anchor nodes' transmission range, reducing the influence of RSSI interference and improving node localization accuracy. Compared to the earlier proposed square and equilateral layout models, the proposed model shows significant improvement. The performance of the earlier layout models and the proposed isosceles layout model for node localization is analyzed using a Jaya algorithm and compared with particle swarm optimization (PSO) and salp swarm algorithm (SSA)-based node localization algorithm. The proposed algorithm's performance is evaluated using scalability and localization accuracy assessments. We investigate the influence of the degree of irregularity on localization accuracy for each layout. The proposed isosceles layout performs 14.16% and 8.56% better than the square and equilateral layout models for the proposed Jaya-based node localization algorithm. The proposed algorithm performs 7.16% and 4.24% better than the PSO- and SSA-based node localization algorithms for the isosceles layout model in WSNs for the same parameters. MATLAB simulation evaluates the proposed layout model and node localization algorithm. [ABSTRACT FROM AUTHOR]

A novel approach for sensing and characterising the flow over an aerofoil is introduced. Arrays of flexible wind-hair-like sensors distributed over an aerofoil, which are tracked remotely using high-speed imaging and processing, acting as "digital tufts", are used to provide real-time readings of local flow information with high temporal resolution. The use case presented in this paper has the sensors embedded within the suction side of a NACA0012 aerofoil and tested in a wind tunnel for varying angles of attack in static and dynamic tests. The time-averaged signals were able to provide information pertaining to the free-stream velocity and instantaneous angle of attack. The capability of the sensor type to provide temporal flow information is also explored. The sensors were used to detect low-frequency oscillations, which are pre-cursory to stall. These are hypothesised to be linked to breathing modes of the laminar separation bubble, causing a shear-layer flapping observed on the sensors. Such low-frequency oscillations were also detected shortly before separation in the ramp-up studies. [ABSTRACT FROM AUTHOR]

Cloud computing provides different types of resources to users on-demand which are hosted in cloud data centers. Aforesaid services are provided at the expense of large energy consumption. Energy consumption increases the expenditure budget, greenhouse gases, and CO2 emissions. To handle this issue, researchers have come up with various server-level energy-efficient techniques. Though the proposed techniques attempt to reduce energy consumption, they only consider the energy consumption of the CPU during the task placement process. However, researchers have recently noted that memory is also one of the higher energy consumption components and it should be considered in task placement. Moreover, existing techniques ignore the SLA violations that are encountered due to workload. To address the aforementioned issues, we propose two novel nature-inspired techniques which consider the energy consumption of both CPU and memory during the VM placement process. Proposed novel techniques are based on artificial bee colony and particle swarm optimization which haven't been used to place VM while considering energy consumption of CPU and memory. Moreover, to handle the issue of resultant SLA violations, we also provide the SLA-aware variants of the proposed energy-efficient techniques, which try to lower SLA violations faced because of excessive task consolidation. The results depict that the proposed energy-efficient techniques perform better than the existing state-of-the-art techniques, whereas proposed SLA variants also reduce the SLA violations. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) stand at the forefront of energy harvesting innovation, transforming mechanical energy into electrical power through triboelectrification and electrostatic induction. This groundbreaking technology addresses the urgent need for sustainable and renewable energy solutions, opening new avenues for self-powered systems. Despite their potential, TENGs face challenges such as material optimization for enhanced triboelectric effects, scalability, and improving conversion efficiency under varied conditions. Durability and environmental stability also pose significant hurdles, necessitating further research towards more resilient systems. Nature inspired TENG designs offer promising solutions by emulating biological processes and structures, such as the energy mechanisms of plants and the textured surfaces of animal skins. This biomimetic approach has led to notable improvements in material properties, structural designs, and overall TENG performance, including enhanced energy conversion efficiency and environmental robustness. The exploration into bio-inspired TENGs has unlocked new possibilities in energy harvesting, self-powered sensing, and wearable electronics, emphasizing reduced energy consumption and increased efficiency through innovative design. This review encapsulates the challenges and advancements in nature inspired TENGs, highlighting the integration of biomimetic principles to overcome current limitations. By focusing on augmented electrical properties, biodegradability, and self-healing capabilities, nature inspired TENGs pave the way for more sustainable and versatile energy solutions.

The metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the last few decades. Nature-inspired metaheuristic algorithms contribute significantly to tackling many standing complex problems (such as the combinatorial t-way testing problem) and achieving optimal results. One challenge in this area is the combinatorial explosion problem, which is always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation has been selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-Time Hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization, whilst others were not. This research paper outlines a hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021), such as the Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out an up-to-date review of these distinguished algorithms with their respective current states of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics have a simple structure that can be adopted in different areas to become more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters settings to achieve an optimal solution.

A novel approach for sensing and characterising the flow over an aerofoil is introduced. Arrays of flexible wind-hair-like sensors distributed over an aerofoil, which are tracked remotely using high-speed imaging and processing, acting as “digital tufts”, are used to provide real-time readings of local flow information with high temporal resolution. The use case presented in this paper has the sensors embedded within the suction side of a NACA0012 aerofoil and tested in a wind tunnel for varying angles of attack in static and dynamic tests. The time-averaged signals were able to provide information pertaining to the free-stream velocity and instantaneous angle of attack. The capability of the sensor type to provide temporal flow information is also explored. The sensors were used to detect low-frequency oscillations, which are pre-cursory to stall. These are hypothesised to be linked to breathing modes of the laminar separation bubble, causing a shear-layer flapping observed on the sensors. Such low-frequency oscillations were also detected shortly before separation in the ramp-up studies.

Even though hard, low friction coatings such as diamond like carbon (DLC) would be beneficial for the performance and longevity of rubber seals, a crucial challenge remains. The elastic mismatch of rubber substrate and DLC coating prevents a fracture free coating application. In this work, a nature inspired approach is applied to render the stiff coating flexible and resilient to delamination at the same time by direct patterning. Rubber substrates were laser structured with tile patterns and subsequently DLC coated. Tensile and tribology tests were performed on structured and unstructured samples. Unstructured DLC coatings showed a crack pattern induced by the coating process, which was further fragmented by tensile stress. Coatings with tile patterns did not experience a further fragmentation under load. During continuous tribological loading, less heterogenous damage is produced for tile structured samples. The findings are ascribed to the relief of induced coating stress by the tile structure, meaning a more resilient coating. [ABSTRACT FROM AUTHOR]

This paper presents an approach to overcome economic load dispatch (ELD) using a metaheuristic algorithm. Economic load dispatch (ELD) is one of the most important problems in a power system, and solving it quickly is extremely important. The main problem that will be addressed in this paper is how to optimize the economy of the power grid with various operational limitations, the loss in transmission line power, and consider minimizing the fuel costs produced. In this study, some of the newest metaheuristics inspired by nature will be explored, namely Seagull Optimization Algorithm (SOA), Marine Predator Algorithm (MPA), Sine Tree-Seed Algorithm (STSA), Chimp Optimization Algorithm (ChOA), Equilibrium Optimizer (EO), and Giza Pyramids Construction (GPC). The performance appraisal of the method applied in this study was tested using 2 case studies, namely a system with 3 and 6 power system units. The results are presented by comparing between metaheuristic and mathematical methods. The experimental results is showed that the Sine Tree-Seed Algorithm (STSA) is presented the best performance with various case studies with constraints. [ABSTRACT FROM AUTHOR]

As a critical infrastructure of urban water systems, sewer networks suffer from serious corrosion and odor problems, which can be controlled by chemical dosing. It is a challenging task to optimize chemical distribution in such a hybrid system with continuous hydraulic flow, discrete pump operations, and dynamic constraints. In this article, we study real-time control of multiple pumps to achieve the desired chemical distribution in a sewer network. A novel hybrid optimization approach is developed, which involves an event-triggered scheme triggered by predicting proper pumping events at uncontrolled pumping stations, and an improved nature-inspired elephant herding optimization (iEHO) algorithm for scheduling pumping at controllable pumping stations. The proposed method is validated through simulation studies of a real-life sewer network using real measured data. Our strategy significantly improves chemical distribution with reduced costs, despite an astronomic searching space. The iEHO algorithm outperforms the genetic algorithm in terms of the quality of solutions and convergence efficiency. [ABSTRACT FROM AUTHOR]

Nature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

Strengthening of metals as a function of metal working and thermal treatment is being utilized effectively in steel structures. Effect on strength beyond thermomechanical treatment has been the focus of this research by imprinting a nature inspired pattern on one side of the sheet metal. Pangolin scale pattern overlapped on AISI 1040 sheet metal while preserving its base treatment. Microstructure, impact toughness, hardness, and tensile strength with notch opposite to worked side was investigated after forging and post forging heat-treatment. After heating at 800°C, overlapping patterned forging was done on one side of two plates and then subsequently cooled in two ways, air cooled, and water quenched. To analyze the effect of forging and heat treatment, Charpy impact, UTS, hardness tests, and metallography were carried out on the heat treated (forged + quenched), untreated (forged + air cooled) and base (as received) specimens. The Room temperature Charpy test showed that the untreated specimen has the highest impact toughness of 120 J as that of the base and heat-treated specimen, 44 J and 60 J respectively. The heat-treated specimen showed an increase in macro and micro hardness than for the base and untreated specimens. Whereas the UTS has dropped in untreated and heat-treated specimens as compared to the base specimen, because in tensile test load is applied uniformly throughout the specimen so the stress concentrations in the unforged side of the metal higher than the side which is forged with overlapping patterns.

Topology optimization has been an interesting area of research in recent years. The main focus of this paper is to use an evolutionary swarm intelligence algorithm to perform Isogeometric Topology optimization of continuum structures. A two-dimensional plate is analyzed statically and the nodal displacements are calculated. The nodal displacements using Isogeometric analysis are found to be in good agreement with the nodal displacements acquired by standard finite element analysis. The sizing optimization of the beam is then performed. In order to determine the stress at each point in the beam a formulation is presented. The optimal cross-section dimensions by performing Isogeometric analysis are acquired and verified with the cross-section dimensions achieved by hiring bending stress and shear stress criteria, as well. The topology optimization of a two-dimensional simply supported plate continuum and a problem on three-dimensional continuum are optimized and presented. The results show that the minimum weight which is found by applying Isogeometric topology optimization gives better results compared to the traditional finite element analysis.

In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains. [ABSTRACT FROM AUTHOR]

Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems.

The preparation of a novel polymer (poly(dopamine quinone-vanadyl) (polyDQV)) bearing dopaminequinone and VOIV redox groups is described. PolyDQV was characterized using field emission scanning electron microscopy (FESEM), energy dispersive x-ray spectroscopy, x-ray photoelectron spectroscopy (XPS), Fourier transform infra-red (FTIR) spectroscopy, UV-Vis spectroscopy as well as electrochemical methods such as differential pulse voltammetry, cyclic voltammetry, and electrochemical impedance spectroscopy (EIS). The electrocatalytic activity of polyDQV was studied toward electrooxidation of uric acid using differential pulse voltammetry as well as cyclic voltammetry. PolyDQV presents interesting electrocatalytic activity toward UA oxidation in phosphate buffer solution (0.1 M, pH 2) to a well-defined oxidation peak at 0.65 V (vs. Ag/AgCl). The polyDQV-modified carbon paste electrode (CPE/polyDQV) presents a precise linear signal-concentration relationship in the ranges of 0.3–5 μM and 5 to 200 μM with a detection limit (S/N = 3) of 0.02 μM. The %RSD values for ten replicate measurements of 0.5 and 50 μM UA were 1.8 and 3%, respectively, indicating good repeatability of analytical signals. Appropriate recovery values (in the range 96 to 103%) and good selectivity for UA over common coexisting species (such as ascorbic acid and dopamine) exhibit that CPE/polyDQV is a promising novel platform for sensing UA in human blood serum and urine samples. [ABSTRACT FROM AUTHOR]

Materials providing high resistance to wear and macro-deformations or impacts are needed for many applications (material handling hoses or flexible ducting systems, snow plow cutting edges, human joint prostheses, Mars rover wheels, etc.). Production of wear-resistant materials using the promising approach of additive manufacturing (AM) is currently of great interest. Specific nature-inspired or -mimicking AM-printed topographies are allowing the reduction and stabilization of friction coefficients and improved tribo-properties. Moreover, they have multifaceted features such as self-healing, supply of lubricants, improved adhesion, etc. The materials, inspired by mole pelts (fur) and fish, snake and pangolin scales, combine three to four phases, namely hard reinforcement, a skeleton supporting the reinforcement and elastic rubber between elements of the skeleton for flexibility and/or damping of impacts. Brazing can be used to fix the reinforcement if required. Three types of composite materials [CBN brazed into steel pipes/rubber, diamond-coated steel wire/rubber and Ti6Al4V (with or without surface treatment) scales/rubber] were produced and tested by a centrifugal erosion device at 30, 50 and 80 m/s speeds and 30º impact angle. The wear rates were determined, and the discussion of the wear mechanisms is supported by optical, scanning electron microscope and 3D profilometry images. [ABSTRACT FROM AUTHOR]

A neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system using approximate techniques of neural networks. Both neural network and fuzzy system have common features. These can solve problems that have no mathematical models. Adaptive neuro-fuzzy inference system (ANFIS) is an adaptive network that uses supervised learning on learning algorithm. To achieve effective results with ANFIS, selecting the optimization method in training is very important. Heuristics and metaheuristics algorithms attempt to find the best solution out of all possible solutions to an optimization problem. ANFIS training can be based on nonderivative algorithms. Heuristics and metaheuristics are nonderivative algorithms that can lead to better performance in ANFIS training. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. The newly released emperor penguins colony (EPC) algorithm is a population-based and nature-inspired metaheuristic algorithm. This algorithm has much potential for solving various problems. In this article, an optimized ANFIS based on the new EPC algorithm is proposed. The optimized ANFIS is compared with other nonderivative algorithms on benchmark data sets. Eventually, the proposed algorithm is used to solve the classical inverted pendulum problem. The results show that the proposed ANFIS based on the EPC algorithm has less error and better performance than other state-of-the-art algorithms in both training and testing phase. [ABSTRACT FROM AUTHOR]

Meta heuristic algorithms are very important methods, they used mainly in solving combinatorial optimization problems. They are stochastic in manner and simulate the behavior of any population of particles. Meta heuristics algorithms try to find optimal or near to optimal solution when solving complex optimization problem. In this paper two modified emperor penguins colony optimization algorithms MEPC1 and MEPC2 were developed. Original Emperor Penguins Colony (EPC) algorithm simulates the behavior of emperor of penguins, two modifications are done to the original EPC, Archimedes and hyperbolic spiral like movement are used instead of logarithmic spiral like movement. The two modified algorithms are compared with the original algorithm through ten test functions; the results show that the modified algorithms are achieved better than the original one. [ABSTRACT FROM AUTHOR]

In a neural network, theweights act as parameters to determine the output(s) from a set of inputs. Theweights are used to find the activation values of nodes of a layer from the values of the previous layer. Finding the ideal set of these weights for training aMulti-layer Perceptron neural network such that it minimizes the classification error is a widely known optimization problem. The presented article proposes a Hybrid Wolf-Bat algorithm, a novel optimization algorithm, as a solution to solve the discussed problem. The proposed algorithm is a hybrid of two already existing nature-inspired algorithms, Grey Wolf Optimization algorithm and Bat algorithm. The novel introduced approach is tested on ten different datasets of the medical field, obtained from the UCI machine learning repository. The performance of the proposed algorithm is compared with the recently developed nature-inspired algorithms: Grey Wolf Optimization algorithm, Cuckoo Search, Bat Algorithm, and Whale Optimization Algorithm, along with the standard Back-propagation training method available in the literature. The obtained results demonstrate that the proposed method outperforms other bio-inspired algorithms in terms of both speed of convergence and accuracy. [ABSTRACT FROM AUTHOR]