Search Results (371)

One of the most important parameters in fetal monitoring is the fetal heart rate (fHR). However, due to the nature of this signal, several non-invasive techniques have been developed for fetal monitoring, including Cardiotocography (CTG), Doppler Echocardiography (Doppler Echo), fetal electrocardiography (fECG), fetal phonocardiography (fPCG), fetal Magnetocardiography (fMCG), and fetal Photoplethysmography (fPPG). Fetal phonocardiography is a passive, safe, and non-invasive method for estimating fHR, in which an acoustic signal is acquired through a transducer placed on the mother’s abdomen to capture sounds associated with fetal cardiac activity. In this paper, we propose a hybrid framework for fHR estimation that integrates digital signal processing techniques with a deep learning approach. The method employs a Hermite Transform (HT)-based bio-inspired vision model for fPCG signal denoising, the Hilbert transform to extract the signal envelope, and Mel-Frequency Cepstral Coefficients (MFCCs) to generate spectrogram-based features. Then, these features, represented as image-like, are used to train and evaluate a hybrid architecture that synergistically combines convolutional neural networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM is a deep learning model that processes sequential information in both forward and backward directions, thereby extending the memory capabilities of conventional Recurrent Neural Networks (RNNs). Thus, CNNs are used to capture intricate spatial morphologies of the fPCG signals, whereas BiLSTM captures their temporal evolution across consecutive time windows. Additionally, to assess its generalization capability, the proposed method is evaluated on two publicly available fPCG datasets and benchmarked against several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieves competitive performance across different datasets, highlighting its effectiveness and robustness. Full article

Despite the rapid proliferation of unmanned aerial vehicles (UAVs) across industrial, agricultural, and scientific domains, their deployment remains constrained by limited endurance, aerodynamic inefficiency, and acoustic emissions, all mediated by a shared onboard energy budget. Existing biomimetic UAV reviews have generally treated aerodynamics, structures, sensing, control, and energy systems as parallel topics rather than as interacting components of a unified aerial architecture. Drawing primarily on literature published between 2015 and June 2026 and identified through searches of Web of Science, Scopus, and Google Scholar, this review addresses this gap by examining bio-inspired technologies across six principal domains: aeroacoustic and passive flow control, aerodynamic efficiency, multifunctional structural composites, neuromorphic sensing and control, ionic energy storage, and energy harvesting. Its principal contribution is a cross-domain synergy analysis identifying five performance couplings and one structural enabling architecture through which these domains interact physically and functionally. Representative examples include serration-based propeller geometries that can simultaneously reduce noise and power demand; morphing wing surfaces that serve as both aerodynamic structures and triboelectric harvesting substrates; and neuromorphic spiking neural networks that have been reported, in specific event-vision inference benchmarks, to reduce inference energy by three to four orders of magnitude relative to embedded graphics processing unit (GPU)-based implementations. Mechanical harvesting outputs nonetheless remain orders of magnitude below propulsion requirements and are thus positioned as supplementary. Four systemic barriers (unquantified mass–energy balance, undocumented durability, aeroelastic co-design gaps, and heterogeneous metrics) are evaluated, and the resulting synthesis indicates that advancing bio-inspired UAVs requires a transition from structural imitation to functional, system-level biomimetics. Full article

Automatic semantic parsing of architectural floor plans provides a two-dimensional semantic layer for building information modeling (BIM)-related workflows, intelligent plan checking, renovation, and large-scale drawing management. Unlike natural images, floor plans contain sparse textures, dense linework, small architectural symbols, and strong axis-aligned geometric regularities. These properties make conventional segmentation networks vulnerable to small-symbol dilution during downsampling, noisy skip-feature fusion, and fragmented predictions along long wall boundaries. Inspired by principles of hierarchical visual processing, selective attention, and spatial encoding, this study presents PCP-Net, an end-to-end Planar Component Parsing Network for room, icon, and boundary-aware floor plan parsing. PCP-Net uses a Grouped Residual Encoder to extract multi-scale local patterns, a Cross-Scale Feature Recalibration (CSFR) pipeline to recalibrate skip features through Adaptive Channel Gating, Spatial Response Amplification, and Axis-Decompositional Positional Embedding, and a Structural Gradient Propagation branch to provide training-time boundary regularization. Experiments on CubiCasa5K and three external datasets (R3D, CVC-FP, and ROBIN) evaluate PCP-Net under in-domain and zero-shot cross-domain protocols. On CubiCasa5K, PCP-Net attains 71.3% mIoU, 90.1% overall accuracy, and 78.9% mean accuracy; in the current ablation setting, ADPE improves mIoU by 0.8 percentage points over the ADPE-ablated configuration. These results indicate that cross-scale feature recalibration with axis-aware positional cues can improve floor plan semantic parsing within the evaluated datasets and pixel-level metrics, while downstream BIM generation still requires additional vectorization, topology graph construction, and attribute extraction. Full article

Background/Objectives: Tissue bio-adhesives have gained significant attention as efficient alternatives to conventional wound closures, which are often hindered by insufficient adhesion and poor biocompatibility. Methods: Inspired by nature’s robust wet-adhesion strategies that use dynamic covalent interactions, we have reported a facilely fabricated hydrogel bio-adhesive based on lactobionic acid-conjugated polyethylenimine (LA-PEI) and oxidized dextran (ODex) via Schiff base linkages. Results: The prepared hydrogels exhibited three-dimensional interconnected porous networks, regulated swelling ratios, typical viscoelasticity, shear-thinning behavior, and self-healing ability. Notably, the swelling ratios of the hydrogels depended on composition, and OLP11 displayed the highest swelling ratio of over 1500%. The hydrogel bio-adhesives exhibited strong adhesion to various surfaces, including glass, metal, plastic, rubber, and wood, as well as to different chicken organs, including the heart, liver, spleen, and stomach. Furthermore, the hydrogels exhibited excellent ABTS radical-scavenging activity, effective intracellular reactive oxygen species (ROS) scavenging, and good hemocompatibility, with hemolysis ratios of all hydrogels close to 0%, below the threshold of 5%. After culturing with NIH 3T3 fibroblasts, the hydrogels demonstrated good cytocompatibility and promoted cell proliferation, with cell viabilities on day 3 reaching over 90%. Conclusions: This design yields multifunctional hydrogel bio-adhesives, showing strong promise for wound care and tissue repair applications. Full article

Detecting naval vessels at extreme distances using infrared search and track (IRST) systems presents severe physical challenges, notably the complete loss of geometric texture and the non-linear submersion of weak target signals within high-dynamic-range sea clutter. Traditional pure data-driven convolutional neural networks (CNNs) rely heavily on visual appearances and suffer from critical feature blind spots under such extreme physical degradation. To overcome this, we propose a Physics-Aware Deep Coupling Network that shifts the detection paradigm from appearance-based feature extraction to physics-guided attribute recognition. Our method deconstructs the degraded infrared signal into three complementary physical domains: an adaptive radiation energy mapping, corresponding to the energy domain, to rescue weak targets; a bio-inspired spatial saliency filtering mechanism, corresponding to the frequency domain, to maximize the signal-to-clutter ratio; and a PSF-coherent gradient topology framework, corresponding to the gradient domain, to discriminate genuine point targets from chaotic sun glints and island edges. These processed priors, alongside the raw image, are integrated into a 4-channel tensor and fused via a Cross-Domain Attention Module, ensuring deep network coupling. To evaluate this architecture, we conduct extensive experiments on the real-world Maritime-SIRST dataset. Since the original dataset provides only pixel-level segmentation masks, we generate axis-aligned bounding-box detection labels from these masks and retrain both the proposed method and a suite of state-of-the-art YOLO detectors under a unified detection paradigm. Extensive benchmarking demonstrates that our physics-aware methodology consistently outperforms these detectors, achieving a mAP50 of 0.923 and an F1 score of 89.92%, thus providing a highly interpretable and robust solution for maritime domain awareness under extreme physical constraints. Full article

Living nervous systems sense the cardiovascular rhythm with a parsimony that engineered monitors cannot match: sensory receptors encode changes rather than absolute levels, neurons communicate through sparse all-or-none events, retinal circuits sharpen salient features through lateral inhibition, and attention is allocated by surprise. We translate these four principles into BioSpike-Net, a fully event-driven spiking neural network for cardiac-rhythm classification from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. A sensory-adaptive spike encoder (SASE) converts analogue waveforms into ON/OFF spike trains through a mechanoreceptor-inspired gain-control law; adaptive-threshold leaky integrate-and-fire layers integrate these events; a lateral-inhibition spiking convolution emphasises locally salient morphology; and a novelty-gated temporal attention mechanism concentrates computation on the most surprising portions of each beat. Evaluated on the MIT-BIH Arrhythmia Database, PTB-XL, CPSC-2018, and a PhysioNet-derived PPG corpus, BioSpike-Net achieved 97.6 ± 0.3% accuracy and 95.8 ± 0.4% macro-F1 on MIT-BIH five-class arrhythmia classification, and 0.982 ROC-AUC on PPG atrial-fibrillation detection, matching or exceeding strong recurrent, convolutional, and transformer baselines while requiring an estimated 6.4 µJ per inference—approximately 27-fold below the transformer baseline—owing to a mean activation density below 0.10 spikes per neuron per time step. Ablations show that each biological principle contributes a measurable and interpretable accuracy-versus-energy benefit, and the network degrades gracefully under additive noise and motion artefact. By grounding architecture in the economy of biological sensing, this work offers a route to sustainable, always-on cardiac monitoring. Full article

This paper presents a structured review of antenna and electromagnetic spectrum monitoring techniques for non-invasive fault detection in electrical and electronic equipment. Electromagnetic emissions from partial discharges, arc faults, insulation degradation, and component ageing carry diagnostic signatures detectable through remote radio-frequency sensing. We review (1) antenna technologies spanning magnetic-field loops to ultra-high-frequency electric-field sensors, including fractal, Vivaldi, spiral, and bio-inspired designs; (2) data acquisition platforms ranging from laboratory oscilloscopes to software-defined radio receivers and IoT edge nodes; (3) signal processing methods including time–frequency analysis, adaptive decomposition, and statistical techniques; and (4) machine learning approaches from classical classifiers to deep learning architectures such as convolutional neural networks, recurrent neural networks, and Transformer-based models. Unlike prior surveys focusing on individual fault types or specific equipment classes, this review connects all five layers of the sensing pipeline—from electromagnetic emission physics through antenna selection, signal acquisition, processing, and intelligent classification—for partial-discharge, arc, and insulation faults and analyses the cross-layer constraints that couple them. Design optimisation techniques based on computational electromagnetic methods (FDTD, FEM) and sensitivity calibration challenges are discussed. Open challenges, including the lack of standardised UHF calibration, cross-equipment generalisation, and the scarcity of open electromagnetic fault datasets, are identified, along with emerging directions in flexible antennas, edge AI, and digital twin integration. Full article

Accurate correction of gridded numerical weather prediction precipitation forecasts remains challenging because many end-to-end deep learning correction models treat meteorological variables as undifferentiated data channels and therefore provide limited physical interpretability. Inspired by general principles of biological environmental sensing, selective information processing, and regulatory constraint learning, this study proposes PCPNet, a bio-inspired and physically constrained precipitation correction framework. The framework does not imitate a specific biological organ or species; instead, it abstracts three information-processing principles into a meteorological correction task. First, key weather-system cues, including low-level shear lines, trough-ridge effects, upper-level jet-stream forcing, vorticity-divergence-related vertical motion, and water-vapor flux convergence, are quantified as structured diagnostic fields. This transforms the subjective synoptic diagnosis of forecasters into automated grid-based sensing features. Second, these diagnostic cues are fused with numerical weather prediction variables and terrain descriptors in an encoder–attention–decoder network, allowing the model to emphasize dynamically important precipitation-triggering regions. Third, water-vapor conservation and terrain-forcing relationships are embedded as differentiable constraint losses, providing training-time constraint-based regulation that guides the corrected precipitation field toward physically consistent solutions. The method is evaluated from 2021 to 2023 in Hunan Province, China, using hourly numerical weather prediction model outputs as input features, China Meteorological Administration Land Data Assimilation System gridded analysis data as the training target, and independent meteorological station observations for strict cross-validation. PCPNet reduces the mean absolute error by 22.1% compared with the uncorrected China Meteorological Administration Land Data Assimilation System gridded precipitation products and outperforms Linear Regression, Bagging, Boosting, Multi-Layer Perceptron, TabNet, and Tree-based Progressive Regression Models by 12.9%, 13.5%, 16.9%, 10.8%, 14.9%, and 15.9%, respectively. The single-day event analysis provides an initial demonstration of heavy precipitation recovery capability, while comprehensive validation across long-term continuous weather events is planned for future operational deployment to further verify model stability. These results indicate that bio-inspired sensing and regulatory constraint learning can improve both the accuracy and interpretability of precipitation nowcasting correction. Full article

Natural landscape morphology emerges from continuous, reciprocal interactions between biological agents and their physical environment. Despite its broad application across diverse scientific fields, agent-based modeling remains underexplored in the context of non-human geomorphological change. This paper presents a bio-inspired multi-agent framework to investigate how individual animal behaviors, specifically those of the North American beaver, shape adaptive landscapes. To capture dynamic task specialization, we introduce an architecture that abstracts alternating behavioral preferences into two operational states: Explorers (focused on resource identification) and Builders (focused on localized engineering). Deployed in a dynamic environment characterized by seasonal vegetation mean-reversion and a dynamic hydrological proxy, our targeted parameter sweeps and Monte Carlo replications demonstrate that decentralized stigmergic heuristics drive emergent spatial patterns. Quantitative metric analysis across varying colony sizes shows that while smaller swarms maintain a stable ecological equilibrium, larger populations trigger an apparent non-linear expansion of the hydrological network via active bank erosion. By establishing this foundational framework, this work provides an open-source tool to further explore non-human agency and regenerative strategies in landscape architecture and environmental design. Full article

Programmable hydrogel actuators represent an innovative group of adaptive soft matter systems, which are able to respond to external stimuli with controllable mechanical movements for biomedical and bioengineering purposes. Natural silk fibroin (SF) is known to be a peculiar biomaterial, since it can exhibit controllable β-sheet-induced structural transitions, hierarchical self-assemblies, high biocompatibility, and mechanical adaptability, thus representing an ideal candidate for the development of dynamic hydrogels. In contrast to earlier reviews which focused more on SF hydrogel synthesis or biomedical applications, this review presents a mechanism-based understanding of programmable bioactuation by carefully correlating molecular design, network formation, stimuli responsiveness, and macroscopic deformation. Recent developments in SF hydrogel actuators are critically compared in terms of actuation principles, deformation behaviors, response dynamics, mechanical robustness, and functionalization, noting the natural compromise between fast response, strength generation, and durability in such materials. Novel concepts like nanocomposite materials, bioinspired designs, shape memory systems, and 4D printing are described as efficient ways to improve programmable deformation and functionality in soft materials. In addition, the biomedical opportunities of responsive SF hydrogels in wound healing, drug delivery, tissue engineering, wearable biosensors, and soft robots are critically discussed in relation to existing barriers for translation into practice. Combining mechanistic understanding with the comparative assessment of the performance of hydrogels is a basis for developing a complete rationale for the design of the next generation of SF hydrogel actuators and smart shape deformations. Full article

Tissue adhesives are gaining increasing attention as efficient alternatives to conventional wound closure methods, yet their clinical translation is often hindered by insufficient wet adhesion and inadequate biocompatibility. Drawing inspiration from nature’s robust wet-adhesion strategies, particularly dynamic covalent interactions and reversible crosslinking, we report a family of bioinspired composite hydrogels fabricated from 4-carboxyphenylboronic acid-grafted polyethylenimine (4-CPBA-PEI) and polyvinyl alcohol (PVA) that serve as versatile bioadhesives. The polyethylenimine with different molecular weights (18,000, 70,000, and 100,000 Da) was used to prepare the 4-CPBA-PEI derivatives via EDC/NHS-mediated amidation. The resulting hydrogels exhibited three-dimensional interconnected porous networks with tunable pore dimensions and equilibrium swelling ratios (ranging from 400% to 700%), closely correlated with the PEI molecular weight. Rheological measurements confirmed typical viscoelasticity, shear-thinning behavior, and outstanding self-healing performance, which are mainly attributed to hydrogen bonds and dynamic borate ester bonds in the network. The hydrogels firmly adhered to the surfaces of diverse matrices, such as glass, rubber, metal, plastic, wood, human skin, and wet mouse organs. Additionally, the obtained hydrogels exhibited high 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical-scavenging activity (>85%), excellent hemocompatibility (hemolysis rate < 0.5%), and potent intracellular reactive oxygen species (ROS) scavenging activity. Cytocompatibility studies using NIH 3T3 fibroblasts demonstrated low cytotoxicity and favorable cytocompatibility. This biomimetic design yields multifunctional hydrogels that integrate tunable physical properties, wet-surface attachment, self-healing, antioxidant activity, and good biocompatibility, suggesting their potential as wound dressing candidates. Full article

Bio-inspired cellular metamaterials manufactured using additive manufacturing technologies provide a promising route for controlling thermal transport properties through architecture rather than through the intrinsic properties of the constituent material. This study investigates steady-state heat transfer in open-cell lattice structures comprising 20 different lattice metamaterial specimens representing various classes of cellular architecture. These include Kelvin, auxetic, BCCZ, BCC, cube, Z-cuboctahedron, diamond, FCC, FBCCXYZ, FCCZ, FBCC, G7, isostructure, octahedron, octet structure, rhombohedral dodecahedron, truncated cuboctahedron and truncated cube, all of which are made from polymer materials. The investigated architectures were inspired by functional principles observed in natural cellular systems, including cancellous bone, wood, coral skeletons, and other biological porous materials, where efficient transport processes are achieved through optimized material distribution and interconnected cellular networks. A theoretical model combining conduction through the lattice skeleton, radiative heat transfer within pores and potential convective contributions was developed using homogenization theory and representative volume element analysis. The experiment confirmed the main hypothesis of this study as described by the mathematical model. Experimental validation also confirmed that the homogenization model correctly predicts the thermal conductivity of open-cell lattice structures in highly porous materials with a porosity of around 0.95. The results demonstrate the potential of biomimetic cellular design for the development of lightweight thermal-management materials with programmable thermal transport properties. Full article

We present cystamine-modified polydopamine (PDA) coatings as tunable gatekeepers for mesoporous silica nanoparticles (MSNs) in drug delivery. Unlike conventional post-functionalization strategies, cystamine moieties were incorporated directly into the PDA network, enabling tunable shell composition and redox responsiveness by simply adjusting the dopamine-to-cystamine ratio. By varying the cystamine:dopamine ratio, pH- and redox-responsive release of doxorubicin (DOX) and sorafenib (SO) was achieved, with release kinetics following the Higuchi model. Cystamine-modified PDA nanoparticles with varying disulfide bridge content were synthesized and comprehensively characterized using SEM, TGA, FTIR, and zeta potential measurements. The cystamine content was found to influence thermal stability, coating performance, and protective properties. Importantly, increasing disulfide content did not necessarily improve release performance, suggesting that excessive crosslinking may partially restrict shell permeabilization and drug diffusion. These findings reveal important structure–property relationships in catechol-based coatings and underline the significance of disulfide linkages in the design of bioinspired stimuli-responsive drug delivery systems. Full article

Forecasting cryptocurrency prices remains difficult because market dynamics are highly volatile, non-stationary, and regime-dependent. This study investigates whether combining a spiking-inspired recurrent architecture with the Grey Wolf Optimizer (GWO) can improve one-step-ahead Bitcoin forecasting within a controlled model family. We compare four configurations, LSTM, SLSTM, GWO-LSTM, and GWO-SLSTM, on 4039 daily BTC–USD closing prices from 17 September 2014 to 9 October 2025 using Min–Max normalization, strict chronological splitting, windowed regime-based robustness analysis across three distinct market regimes, and repeated-run testing. The proposed SLSTM replaces the conventional hidden-state recurrence with leaky integrate-and-fire-inspired synaptic, membrane, and adaptive-threshold dynamics, functioning as a spiking-inspired recurrent model with thresholded event gating (reset = `none’, learnable threshold). On the primary hold-out split, GWO-SLSTM achieved a test RMSE of 1840.97 and a test MAPE of 1.76%, compared with 2217.24 and 2.46% for GWO-LSTM, 3501.48 and 3.86% for SLSTM, and 4030.10 and 4.40% for LSTM. Both GWO-optimized models exhibited substantial improvements over their non-optimized counterparts, while the SLSTM baseline also outperformed the plain LSTM, indicating gains from both spiking-inspired recurrence and evolutionary hyperparameter optimization. Both optimized models exhibited near-zero bias (PBIAS 0.11% for GWO-LSTM and 0.36% for GWO-SLSTM). Within the present implementation, GWO-SLSTM also trained faster than GWO-LSTM (39.71 s vs. 137.28 s), although this runtime difference should be interpreted as setup-specific because the model families were implemented in different frameworks and stopped after different numbers of epochs. Overall, within the expanded univariate BTC–USD setting, the results support GWO-SLSTM as a strong within-family candidate for one-step-ahead forecasting under non-stationary conditions. Full article

Meeting the rigorous performance standards of modern electrified transit necessitates the deployment of high-performance outer rotor PMSMs with elevated power-to-volume ratios. However, their unique internal heat source topology inherently restricts heat dissipation. This limitation risks permanent magnet demagnetization and winding insulation failure. To address these thermal bottlenecks, this paper proposes internal bio-inspired cooling channels. These channels feature micro-scale V-shaped ribs. This design targets a 60 kW outer rotor PMSM. The motor uses a fractional-slot concentrated winding. The analytical procedure commences with the formulation of a transient 2D numerical model utilizing the Time-Stepping Finite Element approach (TS-FEM). It is coupled with the Bertotti model to compute electromagnetic losses. This approach accurately determines losses under high-frequency rated conditions. Results reveal that stator iron loss constitutes the dominant heat source. It accounts for 76.4 percent of the total electromagnetic loss. Furthermore, these losses show severe spatial concentration at the stator teeth. Subsequently, a three-dimensional fluid-solid coupled CFD model is developed. This model evaluates the proposed internal cooling channels. The design integrates bio-inspired vein networks and V-shaped ribs. These internal ribs disrupt the near-wall thermal boundary layer. This disruption enhances the local convective heat transfer. Comparative multiphysics analyses indicate improved hydraulic and thermal performance of the bio-inspired design under the same numerical boundary conditions. The bio-inspired channel achieves a more uniform static pressure distribution and reduces severe fluid stagnation zones. In the numerical model, the maximum stator and permanent magnet temperatures are reduced to 48 °C and 42 °C, respectively. This work provides a numerical design reference for thermal management in high-performance electric aviation. Full article