Search Results (1,411)

Inspired by the hovering, diving, and cooperative hunting behaviors of the pied kingfisher, the Pied Kingfisher Optimizer (PKO) has demonstrated competitive performance in optimization tasks. However, it exhibits several phase-specific limitations, including uneven population distribution caused by random initialization, insufficient use of historical information during exploration, over-reliance on the global best during exploitation, and weakly guided perturbation in the symbiosis phase. To address these issues, this study proposes an Improved Pied Kingfisher Optimizer (IPKO), which incorporates biologically inspired adaptive strategies. Drawing inspiration from the kingfisher’s diverse perching, gaze adjustment during hovering, evasive diving after failed strikes, and territory shifting based on flock position, four mechanisms are developed. Specifically, sine chaotic opposition-based initialization enhances population diversity; adaptive directional search regulates the exploration–exploitation balance; stochastic perturbation-based information fusion improves the ability to escape local optima; and centroid-based adaptive boundary handling strengthens constraint adaptability. The performance of IPKO is evaluated on the CEC2017 benchmark suite (10, 30, 50, and 100 dimensions) and two real-world engineering problems. Experimental results show that IPKO achieves superior overall performance compared with eleven state-of-the-art algorithms, with statistical significance confirmed by the Friedman test and Holm’s post-hoc procedure. Ablation studies further verify the contribution of each strategy. In engineering applications such as cold chain logistics and dynamic multi-UAV cooperative path planning, the IPKO algorithm demonstrates superior solution quality, robustness, and constraint-handling capability compared with competing algorithms. These results demonstrate that IPKO is a robust and effective bio-inspired optimization approach for solving complex, high-dimensional constrained engineering problems. Full article

Recognizing human actions from static images in complex industrial environments remains challenging due to insufficient feature representation and high computational complexity. This issue is particularly critical in power-grid safety monitoring, where improper worker postures (e.g., bending, climbing, falling) can lead to severe accidents and personal injuries, necessitating automated monitoring systems that operate reliably on resource-constrained edge devices. This study proposes a bio-inspired lightweight recognition framework that integrates an improved YOLO-Pose model with a gated recurrent unit (GRU) network. The scientific motivation is grounded in the observation that the human musculoskeletal system achieves highly efficient motion perception through three key mechanisms: hierarchical muscle coordination providing intrinsic rotation invariance, proprioceptive feedback enabling real-time error correction, and selective neural gating reducing redundant information transmission. These biological principles directly inspire our technical contributions: polar-coordinate encoding provides rotation invariance, three-stage filtering mimics proprioceptive feedback, and GRU gating mirrors selective information propagation. Unlike prior approaches that treat pose-based action recognition as a generic computer vision problem, this work explicitly incorporates anatomical structural constraints into the computational pipeline. The framework addresses three research gaps: (1) existing methods lack biomechanically derived invariance properties; (2) GCN-based approaches use fixed topologies that fail to adapt to occlusion patterns; (3) the trade-off between model complexity and accuracy remains unsatisfactory for edge deployment. Experiments on the self-constructed SKPose dataset demonstrate that the proposed method achieves 95.04% accuracy, outperforming ST-GCN by 3.67 percentage points and 2s-AGCN by 1.94 percentage points, with an inference speed of 48 FPS on 8.7 M parameters in underground power-grid environments and provides practical support for biomimetic perception systems and industrial safety monitoring. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Understanding pathological processes remains challenging, because clinical descriptions still rely predominantly on phenotypic observations, while the underlying dynamical mechanisms that generate, maintain, and transform pathological conditions often remain implicit. This Perspective article introduces forms dynamics as a physically grounded framework for interpreting pathology as the dynamical evolution of structured configurations sustained by continuous exchanges of energy, mass, and regulatory information with the environment. The proposed perspective integrates concepts from non-equilibrium thermodynamics, complex systems modeling, systems ecology, and Gestalt-inspired structural reasoning. Within this framework, pathological systems are represented through physically meaningful state variables and fluxes whose interactions can be expressed through coupled balance equations or equivalent graphical schematizations. Empirical observations, including clinical data, diagnostic measurements, and network-based representations of biological interactions, guide the identification of relevant variables, pathways, and couplings. Calibration and validation are discussed as procedures through which admissible dynamical regimes are constrained using physiological ranges, characteristic timescales, observed trajectories, and responses to perturbations. In this perspective, physiological and pathological conditions are interpreted as dynamically maintained regimes emerging from the coupling of variables and fluxes rather than as purely static structural states. As a foundational contribution, this article does not present a disease-specific case study but establishes the conceptual basis, illustrative mathematical structure, and operational workflow through which future disease-specific implementations may be developed. In this sense, forms dynamics is proposed as a unifying modeling perspective for complex diseases and as a possible foundation for future translational applications, including physics-informed digital twins and more interpretable computational tools for biomedical research and clinical support. Full article

The rapid growth of digital communication and large-scale data exchange has increased the demand for advanced cryptographic techniques capable of resisting emerging computational threats. Conventional encryption methods primarily rely on mathematical complexity, which may become vulnerable with the advancement of high-performance computing and future quantum technologies. Biological molecules such as deoxyribonucleic acid (DNA) and RiboNucleic Acid (RNA) provide unique properties, including extremely high storage density, massive parallelism, and complex nucleotide structures that can inspire novel cryptographic mechanisms. This study proposes a bio-inspired cryptographic framework that integrates DNA encoding and RNA-based transformations to enhance data security. In the proposed framework, digital information is first converted into binary format and mapped to nucleotide sequences using a predefined encoding scheme. The encryption process incorporates multiple molecular transformations, including complementary base pairing, sequence permutation, and transcription-inspired DNA-to-RNA conversion to generate a highly randomized ciphertext. Decryption reverses these transformations to reconstruct the original plaintext. Security evaluation demonstrates that the proposed framework produces high entropy outputs, a substantially large key space, and enhanced resistance to statistical and brute-force attacks. The results indicate that DNA and RNA-inspired cryptographic systems can substantially enhance encryption complexity while maintaining reliable data recovery. This research highlights the potential of molecular cryptography as a promising interdisciplinary approach for future secure communication and biological data storage systems. Full article

Odor source localization in turbulent environments remains a major challenge for autonomous robots, as odor plumes are highly intermittent, spatially fragmented, and often lack stable concentration gradients. Here, we propose a bio-inspired navigation framework that translates key principles of bumblebee olfactory cognition into robotic decision-making. First, classical conditioning and olfactorily triggered spatial memory experiments demonstrated that bumblebees could form robust odor memories and that training frequency is positively correlated with both proboscis extension response retention and spatial directional preference. Based on these biological findings, a bio-inspired navigation framework, termed Bio-Nav, is constructed by integrating a Partially Observable Markov Decision Process, a Hidden Markov Model, short-term memory, long-term directional reference memory, fuzzy inference, and value iteration. High-fidelity two-dimensional turbulent simulations show that the proposed algorithm substantially outperforms moth-inspired search, Infotaxis, and standard POMDP-based navigation. In 100 Monte Carlo trials, Bio-Nav achieved a success rate of 96.0%, an average of 20.3 search steps, an average path length of 155.1 cm, and a path-to-straight-line distance ratio of 1.6. Even under strong turbulence, the success rate remained above 91%. These results indicate that memory–perception coupling, inspired by bumblebee navigation, provides an effective and robust strategy for odor source localization in complex turbulent environments, offering a generalizable principle for bio-inspired robotic search under uncertainty. Full article

Effective management of hazardous materials (hazmat) and chemical, biological, radiological, and nuclear (CBRN) incidents depends not only on technical capabilities but also on human behaviour. A critical challenge in mass decontamination operations is the potential for casualties to leave the scene before receiving treatment, increasing personal risk and the likelihood of secondary contamination. Despite its operational significance, little is known about the behavioural variables that influence whether casualties remain on scene. This paper presents a structured scenario development methodology, grounded in expert elicitation, to identify the key factors affecting casualty compliance during mass decontamination. A modified Delphi-inspired approach was used to design realistic scenarios that will inform future behavioural studies. The findings contribute to a more robust evidence base for emergency planning by integrating psychosocial variables into operational assumptions for hazmat/CBRN response. Full article

In recent years, the deployment of robots in human-centric environments has necessitated the development of artificial skins that integrate safety and durability. Traditional damage detection often relies on raw signal thresholds, lacking the functional integration of touch, pain, and damage found in biological systems. This study proposes a bio-inspired artificial skin model that separately evaluates these three states through a spatio-temporal anomaly detection framework. We developed an unsupervised model combining a Convolutional Autoencoder (CAE) and Convolutional LSTM (ConvLSTM) to learn the latent representations of tactile maps from intact skin. By quantifying spatial reconstruction and temporal prediction errors, the system generates individual scores for touch, pain, and damage. Pain is defined as an abstract signal of instantaneous abnormality, while damage is identified as a persistent structural deviation. We implemented a dynamic thresholding mechanism mimicking biological sensitization and recovery, with damage detection gated by a pain-flag constraint to minimize false positives. Experimental results across various conditions—including incisions (3–6 cm) and abrasions (10–30 times)—demonstrate that the model can distinguish between momentary noxious stimuli and sustained structural degradation. Quantitative evaluation shows that the proposed model achieves an Area Under the Curve (AUC) of 0.653, outperforming a threshold-based baseline and maintaining zero false positives under strong, non-damaging contact. Specifically, the system successfully mimics biological aftereffects and the pain-gating mechanism, where damage is only assessed in the presence of a pain-related trigger. This research provides a scalable, software-driven foundation for robot self-protection that overcomes the implementation constraints of hardware-dependent neuromorphic systems. Full article

Mechanical engineering systems must sense, inspect, and navigate constrained environments and operate adaptively under uncertainty—requirements that map structurally onto capabilities achieved by biological systems through distributed sensing, morphology-driven locomotion, multimodal perception, and decentralised control. Biomimetics can therefore be interpreted not merely as a source of design inspiration but also as a functional engineering framework relevant to industrial monitoring, inspection, maintenance, and autonomous operation. This study presents a PRISMA 2020-guided systematic mapping review of the biomimetics literature explicitly relevant to mechanical-engineering functions over the decade 2016–2026. A Scopus corpus of 11,114 records was screened through a two-stage abstract-level process. After deduplication and broad relevance filtering, a stricter eligibility audit retained 505 studies assignable to five predefined functional clusters: robotics and access (235 records; 46.5%), mechanical surfaces and tribology (141; 27.9%), sensing and monitoring (106; 21.0%), vision and inspection (14; 2.8%), and control and computation (9; 1.8%). Publication output accelerated markedly after 2022, with 2025 yielding the highest annual count. The principal gap identified is not a shortage of biomimetic concepts, but their limited consolidation into deployable industrial inspection and maintenance architectures. A translational taxonomy connecting biological principles, engineering abstractions, enabling technologies, and mechanical use cases is proposed as an interpretive structuring tool for future research prioritisation and technology-readiness discussion. Full article

Biological cellular structures exhibit a high degree of systematic organization in both morphological configuration and functional coordination, providing important biomimetic insights for urban spatial organization. To address issues in traditional high-density residential areas, such as homogeneous spatial structures and insufficient accessibility of public spaces, this study proposes a planning method for five-minute living circle residential areas based on a biomimetic cellular structure within the framework of space syntax theory. Taking a residential area in Wuhan, China, as a case study, a cell-like spatial structure model was constructed. Convex space analysis, axial analysis, and visibility analysis were conducted using Depthmap software to quantitatively evaluate key syntactic indicators, including integration, connectivity, mean depth, and choice. The results show that, compared with the original planning scheme, the biomimetic cellular planning model significantly optimized the spatial structure of the residential area by relying on the functionally synergistic mechanisms of selective permeability of the cell membrane, whole-area permeation of the cytoplasm, central regulation of the nucleus, distributed coordination of organelles, and efficient transport through cellular microfilaments. In the sample living circle, the overall integration increased from 1.27 to 1.64, the mean depth decreased from 3.79 to 3.18, and spatial connectivity increased from 3.74 to 5.44. Meanwhile, the synergy of the road network increased from 0.44 to 0.86, indicating marked improvements in spatial accessibility, connectivity, and the degree of coordination within the spatial structure. In addition, the visibility analysis showed that the pedestrian aggregation capacity of the public core space was enhanced, and the spatial vitality of public activity spaces in the residential area was improved. The findings demonstrate that the spatial organization model based on biomimetic cellular principles can effectively enhance spatial efficiency and social vitality in five-minute living circle residential areas, providing a quantifiable design method and theoretical framework for bio-inspired urban planning. Full article

Traditional pharmacotherapy is often constrained by suboptimal bioavailability and systemic toxicity. Biomolecularly inspired nano-drug delivery systems (nano-DDS) have emerged as precise platforms to overcome these barriers by orchestrating molecular interactions at the bio-nano interface. This review systematically evaluates the molecular recognition mechanisms and biochemical principles governing nano-DDS performance. We systematically evaluate how passive targeting relies on the EPR effect—dictated by the nanocarrier’s physicochemical properties—and how active targeting exploits ligand-receptor affinity to enhance cellular uptake. Special emphasis is placed on bioresponsive strategies that utilize pathological cues—such as pH gradients, redox potential, and enzymatic activity—for intelligent, on-demand drug release. Furthermore, we discuss structure-function relationships in lipid, polymeric, and biologically derived systems, highlighting their roles in modulating therapeutic signaling in oncology and inflammatory diseases. Finally, translational hurdles and emerging AI-driven molecular design strategies are critically examined. Full article

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system. Full article

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm. Full article

Nature-inspired optimization algorithms (NIOAs) have attracted enormous attention thanks to their great capabilities in solving complex problems. This paper presents the novel Actiniaria optimization algorithm (ACTOA), inspired by the behavior and biological characteristics of Actiniaria (sea anemones). Actiniaria are known to have unique abilities to survive and interact with various marine environments. Therefore, they can provide an appropriate model for designing an optimization algorithm. This study aimed to balance the exploration and exploitation phases using Actiniaria’s two biological mechanisms: hunting and spawning. The exploration phase is developed with a hunting mechanism as a normal distribution of the searching particles with a reduced standard deviation (SD) around the best searching particle. Next, the dispersal of Actiniaria’s eggs in the exploitation phase under forces such as wind and ocean waves is simulated. The performance of ACTOA is assessed using a set of optimization parameters. The advantages of the algorithm’s performance were also examined by 59 test functions, and ACTOA outperformed modern algorithms. Ultimately, optimization of the three dams of Sariyar, Shafaroud, and Pine Flat was put on the agenda and the proposed algorithm showed that optimal solutions were found by the 700th, 840th, and 985th iterations, which resulted in savings of 28.2, 30, and 3.5 percent in concrete volume, respectively. Full article

Ischemic stroke, a major cause of global disability, is characterized by the blockage of an artery leading to reduced cerebral blood flow and subsequent brain injury. Automatic segmentation of ischemic stroke lesions in Computed Tomography Perfusion (CTP) maps is critical for accurate diagnosis, treatment planning, and outcome assessment. However, the accuracy of traditional methods remains limited, with Dice Similarity Coefficient (DSC) values around 68%. To address this challenge, we propose a deep learning-based model inspired by biological systems and brain mechanisms, which emulates natural information processing to enhance ischemic stroke lesion segmentation. The proposed network architecture consists of five graph convolutional layers that automatically extract and classify features from CTP images. We evaluated the model using the ISLES 2018 database, achieving a DSC of 75.41% and a Jaccard Index of 74.52%, representing significant improvements over previous methods. Notably, the proposed approach performs robustly in noisy environments, maintaining accuracy above 60% even at SNR = −4. These results demonstrate the potential of biomimetic-inspired networks for automatic ischemic stroke segmentation. Full article