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Droplet-based energy generator (DEG) has emerged as a promising platform for sustainable micro-energy harvesting, yet improving its energy conversion efficiency remains a primary research focus. In this work, we report an artificial leaf droplet-based energy generator (ALDEG) that mimics the interspaced soft-rigid venation pattern of plant leaves to enhance energy conversion by promoting droplet spreading. Testing reveals that the optimal interplay between membrane energy absorption and elastic rebound, modulated by venation pattern, leads to a 216% enhancement in voltage output and 233% enhancement in current output compared to conventional planar DEGs. An analytical model is developed to describe the relationship between the artificial venation pattern and droplet spreading dynamics, elucidating a "soft-over-hard" principle in which membrane elastic rebound enhances droplet spreading and thus charge generation. Furthermore, a multitier ALDEG architecture inspired by rainfall cascading through forest canopies demonstrates scalable energy harvesting and exhibits robust performance when powering a diverse range of electronic devices. This work establishes a new bio-inspired design framework for DEGs, demonstrating how biomimicry can be harnessed to modulate the substrate structure of DEGs for enhanced performance. The ALDEG platform holds promise for powering self-powered systems for autonomous environmental monitoring, smart agriculture, and other decentralized applications.

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, disaster early warning, and smart grids. However, sensor nodes face strict energy limitations. Unbalanced energy consumption and hotspots severely shorten the network lifetime. To address these problems, this paper proposes an optimized Spotted Hyena Optimization-Energy-Efficient Non-Uniform Clustering algorithm (SHOE) for cluster head selection and data transmission. The algorithm has three main innovations: combining a bio-inspired metaheuristic with an improved EEUC (Energy-Efficient Unequal Clustering) multi-hop relay and a Gaussian distribution model for non-uniform node deployment; designing a multi-dimensional fitness function considering energy, distance, and node location; and introducing empty cluster and isolated node repair mechanisms to balance exploration and exploitation. Specifically, the multi-dimensional fitness function guides the heuristic search process towards high-quality cluster head candidates, while the empty cluster and isolated node repair mechanisms dynamically rectify abnormal network structures, ensuring the robustness of the final architecture optimized by the bio-inspired framework. Simulations in MATLAB show that SHOE outperforms LEACH (Low-Energy Adaptive Clustering Hierarchy), PSOE (Particle Swarm Optimization with Evolutionary Strategy), PL-EBC (Probabilistic Localized Energy-Balanced Clustering), and CGWOA (Chaotic Grey Wolf Optimization Algorithm) in reducing node death, saving energy, and extending network lifetime. It improves adaptability to non-uniform distribution and optimizes energy balance, thus enhancing the efficiency and stability of WSNs.

Event cameras are bio-inspired vision sensors offering low latency, low power consumption, and high dynamic range, capturing motion with microsecond-level precision via a per-event triggering mechanism. Despite these advantages, the inherent sparsity and lack of color in event data hinder direct analysis, necessitating advanced deep learning approaches. To achieve low-latency and high-precision motion segmentation for indoor robotic applications, this paper introduces a dual-branch decoupled CNN framework. Specifically, Principal Component Analysis (PCA) is utilized to project 3D event point clouds into 2D motion trend maps, capturing local motion priors while suppressing ambiguity in structured environments. Concurrently, an Event Leaky Integration (ELI) model, inspired by biological membrane potentials, is designed to enhance the structural representation of sparse events. Within this framework, separate branches respectively perform motion validation and shape extraction and are fused via a Spatial Gated Fusion (SGF) module to suppress static background interference. It is demonstrated experimentally that with an input window of only 10 ms, the proposed method achieves a 77% average mIoU across five indoor test scenarios from the EV-IMO dataset with an inference latency of 10 ms per frame. Compared to state-of-the-art methods like MSRNN and GCN, which required 30-300 ms event slices, our framework achieves a favorable trade-off between computational efficiency and segmentation accuracy, maintaining competitive performance under ultra-short time windows for indoor event-based motion processing.

Hydroxyapatite-calcium sulfate (HACaS) bone cements have been clinically established. Combining HACaS with an antiresorptive (zoledronic acid, ZA) and osteoanabolic agent (bone morphogenic protein 2; BMP-2) may enhance the performance of HACaS bone cements in challenging indications, but it must be ensured that this does not impair their setting and mechanical properties. This study established a Vicat/Gillmore-inspired indentation protocol to quantify force-based endpoints and the setting of HACaS with biological adjuvants. HACaS was mixed with or without ZA and/or BMP-2 at 0 min and after a 2 min pre-setting phase with reduced NaCl content (lower liquid-to-powder ratio). For each time point (3-90 min), three cylindrical pellets (Ø 4 mm, height 6 mm) underwent single indentation. Setting was defined as the maximum force at needle penetration, and endpoint hardness was defined as peak force at failure. For 24 h endpoints, specimens were incubated in blood at 37 °C. One-way ANOVA with Tukey's H post hoc test was performed per time point (n = 3; 24 h endpoints n = 5). All 2 min protocols showed accelerated setting, consistent with the initial lower liquid-to-powder ratio. ZA significantly delayed setting and remained lowest at 90 min and after 24 h in blood. Mixing sequence and vehicle composition critically influenced early mechanical properties and should be considered in the further preclinical evaluation of HACaS with osteoanabolic or antiresorptive agents.

Finite element analysis (FEA) is a powerful tool that forms the cornerstone of modeling cardiac biomechanics. However, FEA is computationally expensive for creating digital twins, which typically involves performing tens or hundreds of FEA simulations to estimate tissue parameters, limiting its clinical application. We have developed an attention-enhanced graph neural network (GNN)-based FEA emulator, HeartSimSage, to rapidly predict passive biventricular myocardial displacements from patient-specific geometries, chamber pressures, and material properties. Designed to overcome the limitations of current emulators, HeartSimSage can effectively handle diverse three-dimensional (3D) biventricular geometries, mesh topologies, fiber directions, structurally based constitutive models, and physiological boundary conditions. It also supports flexible mesh structures, allowing variable node count, ordering, and element connectivity. To optimize information propagation, we developed a neighboring connection strategy inspired by Graph Sample and Aggregate (GraphSAGE) that prioritizes local node interactions while maintaining mid-to-long-range dependencies. Additionally, we integrated Laplace-Dirichlet solutions to enhance spatial encoding and employed subset-based training to improve computational efficiency. Incorporating the attention mechanism allows HeartSimSage to adaptively weigh neighbor contributions and filter out irrelevant information flow, enhancing prediction accuracy. As a result, errors in the predicted biventricular myocardial displacements by HeartSimSage were limited to a median of 0.280 mm with an interquartile range of [0.167, 0.484] mm compared to traditional FEA, while achieving a computational speedup of approximately 13000X on a GPU and 190X on a CPU. We validated our model on a published left ventricle dataset and analyzed the model's sensitivity to hyperparameters, neighboring connection strategies, and the attention mechanism.

Smart textiles require advanced sensing capabilities, yet existing sensor-integrated fabrics suffer from poor breathability, brittleness, and thermal vulnerability, restricting large-scale deployment. Herein, inspired by the epidermal bubble-like cell structure of ice plants, we developed an ultra-lightweight Janus fabric, with a polyelectrolyte membrane as the key component-its inherent high stability, excellent ion conductivity, and good compatibility endow the fabric with superior structural flexibility and functional synergy. This design integrates passive daytime radiative cooling (PDRC) and sensing functions, retaining breathability and directional moisture transport. Notably, the polyelectrolyte membrane-enhanced fabric achieves 9.86°C sub-ambient cooling (101 W m- 2 net cooling power) under 1 sun intensity, 100% accurate motion monitoring, and stable triboelectric output (10 V stable output under 10 N constant force), along with exceptional durability (1000 folding cycles), recyclability, and antibacterial activity. Owing to the prominent advantages of structural innovation, excellent performance, and strong practicality, this study can not only be effectively extended to other inorganic particle systems (e.g., SiO2, boron nitride) but also holds broad application prospects in wearable electronic devices, flexible robots, and intelligent sensing systems.

Diabetic wound healing is a highly coordinated, multi-stage process that relies critically on the pro-regenerative microenvironment. Recently, stem cell-based therapies have emerged as a promising paradigm in regenerative medicine, largely attributable to their inherent self-renewal capacity, multilineage differentiation potential, and pro-repair secretome. Despite these advances, the therapeutic potential of neural stem cells (NSCs) in cutaneous wound repair remains largely unexplored. We systematically evaluated the pro-angiogenic, antioxidant, and mitochondrial modulatory effects of NSCs on endothelial cells. A bio-inspired hydrogel was designed for NSC encapsulation with favorable preliminary biocompatibility. The in vivo pro-healing efficacy of NSCs-armed hydrogel was then assessed in diabetic mice. NSCs enhanced angiogenesis, antioxidant capacity, and mitochondrial function in endothelial cells. The engineered hydrogel exhibited favorable preliminary biocompatibility for the encapsulation of NSCs. This platform accelerated diabetic wound healing by regulating inflammation, promoting angiogenic, and exerting neural-supportive effects. This NSC-loaded hydrogel platform offers a promising and clinically relevant strategy for diabetic wound repair.

This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy efficiency and alignment with sustainable computing objectives. The primary goal was to design and implement CL techniques in SNNs to assess the model's ability to maintain accuracy in explored environments while reducing CO2 emissions through the optimized use of a subset of the data. This study emerges in response to the increasing energy demand of deep learning models, which poses a challenge to sustainability. SNNs, inspired by the efficiency of biological neural systems, offer significant advantages in terms of computational and energy consumption, making them a promising alternative. CL techniques, such as Elastic Weight Consolidation and replay memory, are integrated to mitigate catastrophic forgetting in sequential learning tasks. The methodology includes adapting the PilotNet architecture for SNNs, preprocessing datasets generated in the Udacity driving simulator, and evaluating models in incremental learning scenarios. The experiments compare the performance of SNNs with CL against baseline models without CL, using mean squared error (MSE), computational efficiency, and equivalent CO2 emissions as evaluation metrics. The results demonstrate that replay memory enables the retention of prior knowledge with a limited increase in energy consumption. This work concludes that SNNs with CL are a viable alternative for sustainable AI applications. Future research directions include a focus primarily on hardware-specific implementations and real-world testing.

While AI-generated content (AIGC) strives to replicate human imagination, current models like score-based diffusion remain slow and energy-intensive. This inefficiency stems from conventional digital computers, where physically separated storage and processing units cause data-transfer bottlenecks, and discrete operations disrupt naturally continuous generation dynamics. Here we show a brain-inspired, analog in-memory computing system that overcomes these limitations. By employing resistive memory, our system integrates storage and computation to act as a time-continuous neural differential equation solver. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros. While maintaining generative quality equivalent to the software baseline, our system accelerated both unconditional and conditional generation tasks, by factors of 69.0 and 116.5, respectively, compared to advanced digital hardware. Furthermore, it reduced energy consumption by 31.5% and 52.0%, respectively. Our approach expands the horizon for hardware solutions in edge computing for generative AI applications.

The secondary cooling water system is difficult to control because of loop coupling, thermal inertia, and strict actuator constraints. In addition, when conventional proximal policy optimization (PPO) uses Gaussian action sampling with clipping, the mismatch between sampled and executed actions may degrade learning and control smoothness near actuator limits. To address these issues, this paper develops a Beta-policy and PID-inspired augmented-state proximal policy optimization framework, termed BPAS-PPO, for the secondary cooling water system. The framework augments the state with proportional, integral, and derivative error features, adopts a Beta-distribution policy for bounded continuous-action generation, and uses a piecewise dense reward for the dual-loop tracking task. Simulation studies on an identified linear two-input two-output (TITO) model within the selected operating region show that the complete PID-augmented state yields the most effective training representation among the tested alternatives. Compared with PID, Fuzzy-PID, and Gauss-PPO, BPAS-PPO shows lower overshoot, shorter settling time, better setpoint tracking and disturbance rejection, and smoother control actions near actuator limits. The proposed framework is effective for the studied system within the selected operating region, while its performance beyond this region requires further validation.

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error.

The deterministic integration of multiple materials is the cornerstone of the semiconductor industry, traditionally accomplished through complex microfabrication techniques, such as lithography, transfer, and wafer bonding. Inspired by biological systems that precisely form intricate intracellular structures, self-assembly offers an efficient, bottom-up pathway for monolithic integration. The challenge, however, lies in controlling the transport of multiple components within the inherently chaotic and confined fluidic environments of microfabrication, which typically induces mixed phases and structural disorder. Herein, we utilize capillary bridges with Marangoni vortex flow to guide the segregation of colloidal quantum dots (CQDs) by size, enabling the efficient self-assembly of multicomponent microstructures. The fluid flow in our system establishes a regulated concentration gradient. This gradient drives the diffusiophoresis of larger CQDs away from the evaporation front, inducing a "small-at-front" segregation. The versatility and robustness of our platform are demonstrated by the various phase-segregated microstructures with customizable morphologies and diverse compositions. To showcase its practical application, we leverage this technique to integrate dual-wavelength lasers within a single photonic circuit, achieving the on-chip propagation of coherent light for optical communications. Our work introduces a novel approach to multicomponent microfabrication.

The lymphatic system maintains physiological homeostasis through constant immunosurveillance, immune cell trafficking, and regulation of interstitial fluid flow. Lymphatic dysfunction is associated with a wide range of pathologies, including cancer metastasis and lymphedema. Aberrant lymphatic structure also contributes to chronic wounds and transplanted organ rejection. These functional and structural deficits have inspired strategies for lymphatic vasculature modulation and tissue engineering to regulate immune functions during disease and injury rehabilitation. Lymphangiogenesis-the process of lymphatic vessel growth- is central to the success of these strategies and has broad potential if harnessed for therapeutic intervention and tissue integration. Here we review the opportunities and obstacles for biomolecular pathway modulation, nanotechnology, and tissue engineering to promote or inhibit lymphangiogenesis.