Search Results (767)

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

Tunneling nanotubes (TNTs) are dynamic cell surface conduits that enable direct transfer of ions, signaling molecules, and organelles. They have emerged as a key mechanism of intercellular communication, complementing classical pathways such as synapses and paracrine signaling. In the central nervous system (CNS), TNTs exhibit a functional duality, particularly under aging and stress, where TNT-mediated exchange may shift from protective to maladaptive. On one hand, TNTs support homeostatic functions, ranging from mitochondrial transfer to stem cell-mediated rescue and astrocyte–neuron metabolic support. On the other hand, they facilitate the spread of prions and neurodegenerative protein aggregates, such as Tau and α-synuclein, with astrocytes playing a regulatory role. Despite rapid advances, TNT research faces challenges from conceptual heterogeneity and experimental standardization, especially in complex tissues such as the CNS. Recent mechanobiological and bio-inspired approaches, including force-based assays and three-dimensional culture models, provide new insights into TNT formation, stability, and cargo transport, extending beyond neural systems. This review offers an integrative synthesis of molecular, structural, and mechanobiological principles underlying TNT-mediated communication, emphasizing astrocyte–neuron crosstalk, while proposing validation criteria to support rigor, reproducibility, and cross-study comparability. TNTs thus emerge as dynamic, context-dependent interfaces with broad relevance to neurodegeneration, cancer, and biomedical applications. Full article

Neuromorphic computing, an interdisciplinary field combining neuroscience and computer science, aims to create efficient, bio-inspired systems. Different from von Neumann architectures, neuromorphic systems integrate memory and processing units to enable parallel, event-driven computation. By simulating the behavior of biological neurons and networks, these systems excel in tasks like pattern recognition, perception, and decision-making. Neuromorphic computing chips, which operate similarly to the human brain, offer significant potential for enhancing the performance and energy efficiency of bio-inspired algorithms. This review introduces a novel five-dimensional comparative framework—process technology, scale, power consumption, neuronal models, and architectural features—that systematically categorizes and contrasts neuromorphic implementations beyond existing surveys. We analyze notable neuromorphic chips, such as BrainScaleS, SpiNNaker, TrueNorth, and Loihi, comparing their scale, power consumption, and computational models. The paper also explores the applications of neuromorphic computing chips in artificial intelligence (AI), robotics, neuroscience, and adaptive control systems, while facing challenges related to hardware limitations, algorithms, and system scalability and integration. Full article

Regenerative medicine is increasingly leveraging the synergies between bioinspired conductive biomaterials and 3D/4D bioprinting to replicate the native electroactive and hierarchical microenvironments essential for functional tissue restoration. However, a critical gap remains in the intelligent integration of these technologies to achieve dynamic, responsive tissue regeneration. This review introduces a “bioinspired material–printing–function” triad framework to systematically synthesize recent advances in: (1) tunable conductive materials (polymers, carbon-based systems, metals, MXenes) designed to mimic the electrophysiological properties of native tissues; (2) advanced 3D/4D printing technologies (vat photopolymerization, extrusion, inkjet, and emerging modalities) enabling the fabrication of biomimetic architectures; and (3) functional applications in neural, cardiac, and musculoskeletal tissue engineering. We highlight how bioinspired conductive scaffolds enhance electrophysiological behaviors—emulating natural processes such as promoting axon regeneration cardiomyocyte synchronization, and osteogenic mineralization. Crucially, we identify multi-material 4D bioprinting as a transformative bioinspired approach to overcome conductivity–degradation trade-offs and enable shape-adaptive, smart scaffolds that dynamically respond to physiological cues, mirroring the adaptive nature of living tissues. This work provides the first roadmap toward intelligent electroactive regeneration, shifting the paradigm from static implants to dynamic, biomimetic bioelectronic microenvironments. Future translation will require leveraging AI-driven bioinspired design and organ-on-a-chip validation to address challenges in vascularization, biosafety, and clinical scalability. Full article

Cellular structures are well-established in biological materials and are often mimicked in many kinds of structural designs applicable to engineering. This results from their lightweight designs and good mechanical properties. Cellular designs in nature have extremely complex configurations. As a result, the deformation behaviour models for bioinspired hollow parts based on these geometries, that are presently available in the literature, are limited in their capacity to provide detailed descriptions of the mechanisms resulting in deformation. Extensions to the existing deformation behaviour mechanisms of cellular parts are proposed in this paper. First, a general outlook on cellular designs is given. This is followed by a review of the commonly recognised two-stage stress–strain curve for cellular parts and its comparison with the new curve suggested in this paper, which incorporates suggestions more fully accounting for the deformation mechanisms of these structures. Further, analytical models that are available in the literature, outlining the behaviour of cellular parts, are highlighted, together with new models developed here for predicting failure of lattice structures based on the Tresca and von Mises criterion. Next follows a discussion of proposed strategies that could be adopted in deformation behaviour models for optimising the design of hollow structures to improve their mechanical properties. Finally, the anticipated challenges for and future insights into the incorporation of the cellular behaviour models suggested here, in cutting-edge structural design for additive manufacturing (AM), are highlighted. Full article

The increasing challenges of urbanization and environmental degradation have led to a greater need for built environments that minimize ecological consequences while actively contributing to ecosystem services (ES). Bio-Inspired Design (BID) is a promising approach that translates natural-system ideas into architectural and urban solutions. This study investigates how BID can be used to deliver and improve ecosystem services, like climate regulation, air purification, and energy, in the built environment, focusing on applications in hot climates and at the meso scale. The study conducts a qualitative and integrative analysis of bio-inspired concepts derived from existing research and innovative practices. It examines specific ecosystem services—selected based on previous studies—and illustrates how these strategies can improve environmental performance in urban contexts. A conceptual framework for linking biological analogies to urban functions is proposed. The framework emphasizes the interdisciplinary relationships between architecture, urban design, material science, and environmental engineering. This provides a helpful guide for researchers and practitioners on how to use BID to enhance sustainability results. The study suggests that incorporating BID principles into urban design procedures can potentially transform built environments into active contributors to ecosystem functioning, enabling them to provide ES rather than merely consuming resources. While this conclusion is conceptual, the framework highlights pathways for more resilient and sustainable urban futures. Full article

Triply Periodic Minimal Surface (TPMS) structures, with their zero average curvature, excellent energy absorption properties, high specific strength and high surface-to-volume ratio, could be used in a wide range of applications, such as the creation of lightweight and durable structures, grafts and implants. In this study, an internal TPMS structure inspiring trabecular bone and an external TPMS structure inspiring cortical bone were combined with infill density and topologically functionally graded to obtain hybrid structures. The aim of the study was to investigate the effects of functional grading on mechanical properties, energy absorption capacity and surface/volume (S/V) ratio and to determine the best combination. The novelty of the study is to obtain hybrid structures close to bone structures with a functional grading approach. The experimental design of the study was performed using the Design of Experiment (DoE) approach and the Taguchi method. Specimens were created according to the established experimental design and fabricated using a Masked Stereolithography (mSLA)-type 3D printer with bio-resin. The fabricated structures were subjected to compression tests; the results were examined in terms of deformation behavior, first peak, maximum force, energy absorption, specific energy absorption and S/V ratio. The optimal structures for defined input parameters were determined using signal-to-noise (S/N) ratios and ANOVA results. Deformations for diamond and primitive specimens began as shear band formation. Deformations for Neovius structures were mostly as brittle fracture. The highest first peak of 18.96 kN was obtained with the DN specimens, while the highest maximum force of 23.77 kN was obtained with the ND specimens. The best energy absorption property was also obtained with ND. The highest S/V ratio was 5.65 in the GP specimens. The statistical analyses were in accordance with the experimental results. Infill density increases decreased the S/V ratio while increasing all other parameters. This demonstrated the importance of mechanical-strength/porosity optimization for bone scaffold surrogate applications in this study. Full article

Lignocellulosic bio-based materials, such as wood, biocomposites, and natural fibers, exhibit desirable structural properties. This comprehensive review emphasizes the foundational and latest advancements in bioinspired improvement strategies, such as direct mineralization, biomineralization, lignocellulosic nanomaterials, protein-based treatments, and metal-chelating processes. Significant focus was placed on biomimetics, emulating natural protective mechanisms, with discussions on relevant topics including hierarchical mineral deposition, free-radical formation and quenching, and selective metal ion binding, and relating them to lignocellulosic bio-based material property improvements, particularly against fire and fungi. This review evaluates the effectiveness of different bioinspired processes: mineralized and biomineralized composites improve thermal stability, nanocellulose and lignin nanoparticles provide physical, thermal, and chemical barriers, proteins offer biochemical inhibition and mineral templating, and chelators interfere with fungal oxidative pathways while simultaneously improving fire retardancy through selective binding with metal ions. Synergistic approaches integrating various mechanisms could potentially lead to long-lasting and multifunctional protection. This review also highlights the research gaps, challenges, and potential for future applications. Full article

Water scarcity constitutes a major global challenge. Biomimetic water collection materials, which mimic the efficient water capture and transport mechanisms, offer a crucial approach to addressing the water crisis. This review summarizes the research progress on biomimetic water collection materials, focusing on biological prototypes, operational mechanisms, and core aspects of biomimetic design. Typical water-collecting biological surfaces in nature exhibit distinctive structure–function synergy: spider silk achieves directional droplet transport via periodic spindle-knot structures, utilizing Laplace pressure difference and surface energy gradient; the desert beetle’s back features hydrophilic microstructures and a hydrophobic waxy coating, forming a fog-water collection system based on heterogeneous wettability; cactus spines enhance droplet transport efficiency through the synergy of gradient grooves and barbs; and shorebird beaks enable rapid water convergence via liquid bridge effects. These biological prototypes provide vital inspiration for the design of biomimetic water collection materials. Drawing on biological mechanisms, researchers have developed diverse biomimetic water collection materials. This review offers a theoretical reference for their structural design and performance enhancement, highlighting bio-inspiration’s core value in high-efficiency water collection material development. Additionally, this paper discusses challenges and opportunities of these materials, providing insights for advancing the engineering application of next-generation high-efficiency biomimetic water collection materials. Full article

Biological decision-making under uncertainty relies on somatic markers, which are affective signals that bias choices without exhaustive computation. This study biomimetically translates the Somatic Marker Hypothesis (SMH) into synthetic somatic markers (SSMs), a minimal and interpretable evaluative mechanism that assigns a scalar valence to compressed environmental states in the high-dimensional discrete grid-world Pixelverse, without modelling subjective feelings. SSMs are implemented as a lightweight Python routine in which agents accumulate valence from experience and use a simple threshold rule (θ = −0.5) to decide whether to keep the current trajectory or reset the environment. In repeated simulations, agents perform few resets on average and spend a higher proportion of time in stable “good” configurations, indicating that non-trivial adaptive behaviour can emerge from a single evaluative dimension rather than explicit planning in this small stochastic grid-world. The main conclusion is that, in this minimalist 3 × 3 Pixelverse testbed, SMH-inspired SSMs provide an economical and transparent heuristic that can bias decision-making despite combinatorial state growth. Within this toy setting, they offer a conceptually grounded alternative and potential complement to more complex affective and optimisation model. However, their applicability to richer environments remains an open question for future research. The ethical implications of deploying such bio-inspired evaluative systems, including transparency, bias mitigation, and human oversight, are briefly outlined. Full article

Spherical caps exploit their intrinsic curvature to achieve efficient stress distribution, delivering exceptional strength-to-weight ratios. This advantage renders them indispensable for aerospace systems, pressurized containers, architectural domes, and structures operating in extreme environments, such as deep-sea or nuclear containment. Their superior load-bearing capacity enables diverse applications, including satellite casings and high-pressure vessels. Meticulous optimization of geometric parameters and material selection ensures robustness in demanding scenarios. Given their significance, this study examines the natural frequency and static response of bio-inspired helicoidally laminated carbon fiber–reinforced polymer matrix composite spherical panels surrounded by Winkler elastic foundation support. Utilizing a 3D elasticity approach and the finite element method (FEM), the governing equations of motion are derived via Hamilton’s Principle. The study compares five helicoidal stacking configurations—recursive, exponential, linear, semicircular, and Fibonacci—with traditional laminate designs, including cross-ply, quasi-isotropic, and unidirectional arrangements. Parametric analyses explore the influence of lamination patterns, number of plies, panel thickness, support rigidity, polar angles, and edge constraints on natural frequencies, static deflections, and stress distributions. The analysis reveals that the quasi-isotropic (QI) laminate configuration yields optimal vibrational performance, attaining the highest fundamental frequency. In contrast, the cross-ply (CP) laminate demonstrates marginally best static performance, exhibiting minimal deflection. The unidirectional (UD) laminate consistently shows the poorest performance across both static and dynamic metrics. These investigations reveal stress transfer mechanisms across layers and elucidate vibration and bending behaviors in laminated spherical shells. Crucially, the results underscore the ability of helicoidal arrangements in augmenting mechanical and structural performance in engineering applications. Full article

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article

The bioinspired superhydrophobic surfaces have demonstrated many fascinating performances in fields such as self-cleaning, anti-corrosion, anti-icing, energy-harvesting devices, and antibacterial coatings. However, developing a low-cost, feasible, and scalable production approach to fabricate robust superhydrophobic surfaces has remained one of the main challenges in the past decades. In this paper, we propose an uncommon method for the fabrication of a durable superhydrophobic coating on the surface of the glass slide (GS). By utilizing the screen printing method and high-temperature curing, the epoxy resin grid (ERG) coating was uniformly and densely loaded on the surface of GS (ERG@GS). Subsequently, the hydrophobic silica (H-SiO₂) was deposited on the surface of ERG@GS by the impregnation method, thereby obtaining a superhydrophobic surface (H-SiO₂@ERG@GS). It is demonstrated that the micro-grooves in ERG can provide a large specific surface area for the deposition of low surface energy materials, while the micro-columns can offer excellent protection for the superhydrophobic coating when it is subjected to mechanical wear. It is important to note that micro-columns, micro-grooves, and nano H-SiO₂ jointly form the micro–nano structure, providing a uniform and robust rough structure for the superhydrophobic surface. Therefore, the combination of a micro–nano rough structure, low surface energy material, and air cushion effect endow the material with excellent durability and superhydrophobic property. The results show that H-SiO₂@ERG@GS possesses excellent self-cleaning property, mechanical durability, and chemical stability, indicating that this preparation method of the robust superhydrophobic coating has significant practical application value. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

The flexible micro-structured surface found in biological skins exhibits remarkable drag reduction properties, inspiring applications in the aerospace industry, underwater exploration, and pipeline transportation. To address the challenge of efficiently replicating such structures, this study presents a composite flexible polymer film with a bio-inspired micro-dimple array, fabricated via an integrated process of precision milling, polishing, and micro-injection molding using thermoplastic polyurethane (TPU). We systematically investigated the influence of key injection parameters on the shape accuracy and surface quality of the film. The experimental results show that polishing technology can significantly reduce mold core surface roughness, thereby enhancing film replication accuracy. Among the parameters, melt temperature and holding time exerted the most significant effects on shape precision PV and bottom roughness R_a, while injection speed showed the least influence. Under optimized conditions of a melt temperature of 180 °C, injection speed of 60 mm/s, holding pressure of 7 MPa, and holding time of 13 s, the film achieved a micro-structure shape accuracy of 13.502 μm and bottom roughness of 0.033 μm. Numerical simulation predicted a maximum drag reduction rate of 10.26%, attributable to vortex cushion effects within the dimples. This performance was experimentally validated in a flow velocity range of 0.6–2 m/s, with the discrepancy between simulated and measured drag reduction kept within 5%, demonstrating the efficacy of the proposed manufacturing route for flexible bio-inspired drag reduction film. Full article