Search Results (166)

Background/Objectives: Topical hemostatic biomaterials are used to control diffuse parenchymal bleeding during hepatectomy. TachoSil is a widely used standard fibrin sealant patch. We evaluated the efficacy and safety of InnoSEAL Plus DL, a novel bioinspired absorbable chitosan–catechol/gelatin hemostatic patch, compared with TachoSil. Methods: This multicenter, randomized, single-blind, active-controlled, parallel-group noninferiority trial enrolled adults undergoing hepatectomy who had persistent oozing from the hepatic transection surface despite primary hemostasis. Participants were randomized in a 1:1 ratio to receive InnoSEAL Plus DL or TachoSil. The primary endpoint was hemostatic success within 3 min of application, with a prespecified noninferiority margin of −19.4 percentage points (pp). Safety was assessed up to 30 days postoperatively. Results: Ninety patients were randomized (45 per group). In the per-protocol population, 3 min hemostatic success was achieved in 100.0% of both the InnoSEAL Plus DL (43/43) and TachoSil (41/41) groups. The risk difference was 0.0 pp, and the lower bound of the one-sided 97.5% confidence interval was −8.2 pp, confirming noninferiority. The mean time to hemostasis was similar between groups (1.2 vs. 1.3 min), and no intraoperative rebleeding occurred. Adverse events were reported in 78/90 patients (86.7%) and serious adverse events in 6/90 (6.7%); the latter were typical post-hepatectomy events unrelated to the study devices. No deaths were reported. Conclusions: InnoSEAL Plus DL was noninferior to TachoSil for achieving rapid intraoperative hemostasis during hepatectomy, with no unexpected safety concerns. This bioinspired hemostatic patch is an effective alternative to fibrin sealant, without the use of human-derived proteins. Full article

Today, microfluidics has become a revolutionary field of engineering due to its wide range of applications, including lab-on-a-chip devices, microscale biochemical reactors, drug delivery systems, and disease diagnostics. Efficient fluid mixing has been a significant challenge in these systems due to the dominance of laminar flow and low-Reynolds number conditions, where mixing relies primarily on slow molecular diffusion. It is very difficult to achieve rapid mixing and homogeneous mixing within a limited length. In this study, a bioinspired passive micromixer is developed based on the cristae architecture of mitochondria, which is known for maximizing surface area and transport efficiency in biological systems. The micromixer incorporates cristae-like microstructures within a straight microchannel to produce continuous flow deflection, stretching, and folding, thereby promoting chaotic advection without relying on external energy sources. It also includes mitochondrial granules, such as micropillars, within the channel to disrupt streamline flow. Thus, a numerical investigation was conducted to design four different micromixer geometries: conventional T-channel, and T-channels with a single, double and triple matrix of cristae. The analysis was performed in COMSOL Multiphysics, in which “Laminar flow” and “Transport of diluted species” physics were used, and a stationary study was executed. Simulations were conducted at different Reynolds numbers (Re = 0.1–100) to observe the feasibility of the proposed designs. For analysis, the mixing index and concentration profiles at the outlet and along the length were also examined. The results showed that the high cristae density channel performed well, achieving a mixing index of 95.85% at Re = 0.1 and 85.84% at Re = 100, proving that the proposed mitochondria-inspired cristae Mito-mixer delivers efficient mixing over a broad Reynolds-number range while maintaining a compact, length-efficient design. Full article

Flexible heat exchangers with intricate three-dimensional (3D) geometries exhibit superior mechanical and thermal performance compared with traditional two-dimensional (2D) designs. Their ability to offer greater design freedom and unique functionalities makes them particularly attractive for wearable medical devices. This study investigates flexible heat exchanger technologies in three main directions: (i) miniaturisation, (ii) integration of physical and mathematical models, and (iii) enhanced adaptability through heterogeneous design integration. Through a combination of literature review, mathematical modelling, and experimental analysis, the thermal efficiency of several configurations is compared, including basic thermoplastic polyurethane (TPU) tubes and 3D bio-inspired TPU tubes with aluminium-finned structures. The findings establish a foundation for the development of next-generation flexible wearable medical cooling devices with improved thermal management capabilities and practical applicability in industrial design. Furthermore, the outcomes of this research will directly support the development of improved wearable cooling devices within a UK-based medical device SME, Paxman Scalp Coolers, facilitating the translation of advanced heat exchanger designs into clinically relevant and commercially viable solutions. Full article

Efficient mixing is a persistent bottleneck in agricultural and agrochemical processing, where rapid and uniform mixing must be achieved under laminar flow with low energy input and gentle shear. Inspired by peristaltic transport in biological systems, this study investigates a bio-inspired flexible-wall squeezing mixer and establishes a two-dimensional computational framework to quantify how periodic wall deformation governs scalar homogenization in a flexible conduit. An Arbitrary Lagrangian–Eulerian dynamic mesh approach is implemented to resolve moving boundaries and to prescribe actuation, enabling the systematic evaluation of the separate and coupled effects of peak wall-normal velocity amplitude A and actuation frequency f on mixing performance. Mixing effectiveness is quantified using a variance-based mixing index MI and a sustained-threshold mixing time ts, and response surface methodology is employed to map the A–f design space and interpret the roles of time-dependent shear, interfacial stretching and folding, and vortex intensification. Relative to a non-actuated baseline, a peak wall-normal velocity amplitude of 3 × 10⁻³ m s⁻¹ at 2 Hz reduces t_s by 21.3%. At fixed f = 3 Hz, increasing A from 1 × 10⁻³ to 4 × 10⁻³ m s⁻¹ shortens t_s by 10.2%, while at fixed A = 3 × 10⁻³ m s⁻¹, raising f from 1 to 5 Hz further decreases t_s by 6.6% with diminishing gains at the lowest frequencies. The response surface identifies an operating optimum at A = 4 × 10⁻³ m s⁻¹ and f = 5 Hz, achieving a peak MI of 0.9557 and a minimum t_s of 7.81 s. A periodically squeezed physical mixing loop was further examined using fluorescence imaging to assess outlet homogeneity trends. The stabilized outlet coefficient of variation (CV) decreased from about 0.65 without squeezing to 0.60 at 1 Hz and 10 mm s⁻¹, 0.58 at 2 Hz and 10 mm s⁻¹, and 0.54 at 2 Hz and 30 mm s⁻¹, indicating that stronger and faster actuation improves outlet uniformity. The numerical and experimental results are therefore interpreted jointly as mechanistic and trend-level evidence, while a rigorous quantitative prediction for the cylindrical compliant device will require future three-dimensional, compliance-resolved simulations and broader experimental benchmarking. Full article

Compliant mechanisms have contributed to many advances in soft robotics, and there is strong motivation to translate these ideas to assistive devices where adaptive motion at the human interface is required. This work presents novel reconfigurable compliant joints (RCJs) as a parameterized joint element for functional biomimicry in lower-extremity joints for prosthetic knees and ankle–foot orthoses, with concepts that extend to other limb joints. The RCJ uses a rigid hub and outer ring joined by an array of flexible links with centerlines defined by cubic Bézier curves. Link shapes are organized into four Bézier classes (A–D), with base types using 10, 12, or 14 uniformly distributed link slots and variants generated by modifying active-link count and distribution, forming a structured morphology space of 12 configurations for machine design. Dual-extrusion 3D-printed prototypes are characterized by a custom testing apparatus using a 2.2 kN load cell at 25 mm/s over a 0–90° rotation range across six recorded load cycles to measure torque–angle curves and stiffness under large deformations. Angle-dependent stiffness is evaluated over three fixed intervals (0–30°, 30–60°, and 60–90°) to quantify multi-stage behavior. A 2-dimensional corotational frame model and a Simscape Multibody model, including a rolling-contact knee configuration, use the same parameterization to relate geometry, nonlinear mechanics, and system-level motion. Experiments and simulations show multi-stage torque–angle profiles and predictable stiffness modulation across all configurations, with both magnitude and transition angle tunable through Bézier class and active-link distribution, positioning the RCJ as a CAD/CAE-compatible joint architecture for assistive devices or wearable robotic systems and a basis for advancing functional biomimicry in compliant mechanism design. Full article

Classical contact mechanics typically relies on simplifying assumptions such as linear elasticity and frictionless interfaces. A notable example is the Westergaard model, a rigorous theoretical solution for the contact between a rigid sinusoidal surface and an elastic half-space with a flat surface. This configuration captures the features of surface roughness at a single characteristic scale. Such modeling is particularly relevant since most natural and engineered surfaces exhibit roughness, significantly influencing their contact behavior. In this work, we present a nonlinear analytical contact model, which overcomes the main limitations of the Westergaard solution. Specifically, we formulate the contact problem within a finite elasticity framework and include interfacial friction. The analytical model is derived from the results of dedicated finite element simulations and subsequently validated against experimental data from the literature, demonstrating improved predictive accuracy in estimating the contact area as a function of the applied mean pressure. This work lays the foundation for the development of weakly nonlinear multiscale models, where solutions for single-scale roughness can be superimposed to approximate the behavior of more complex, fractal surface geometries. Such an approach holds promise for applications in areas such as tactile human–device interactions, soft robotics, and the design of bioinspired surfaces. Full article

Nanoscale electrical contacts, especially those between materials of dissimilar electronic properties, often represent one of the main causes of drops in energy transfer efficiency. They are also among the sources of above-threshold noise, and their performance often decreases over the lifetime of the nanodevices. Scale-down limitations from mesoscopic to nanoscale devices, and likewise, of nanoscale to quantum-scale devices are also impeded by contacts’ quality. Making more reliable, energy-efficient electrical contacts is among the goals of the nanoelectronics research within the framework of energy-efficient electronic systems. This report focuses on the design, nanofabrication, and testing of novel shapes of electrical contacts. Lithography and nanofabrication were utilized to mimic the approximate shape of insect setae for mesoscale contacts design. The contacts are tested for elementary charge transport via I–V curves and for the broadband, 1/f noise. Tests show that contacts design leads to a measurable decrease in the energy necessary to operate a contact as a switch by at least 12–20%, depending on temperature, while broadband noise shows measurably lower power spectra, for bio-inspired contacts. The proposed method is open to modifications and improvements as required by various on-chip applications. Full article

This review critically analyzes the evolution of passive heat transfer enhancement in internal flows, charting a paradigm shift from momentum-based flow perturbation to the precise engineering of vortex structures. The central thesis is that the highest-performance, next-generation thermal systems will be realized through ‘flow field programming’—a unified design paradigm that intelligently architects vortex-topology and surface architecture across scales using smart materials, additive manufacturing, and artificial intelligence. This progression is traced from classical devices such as twisted tapes, which generate global swirl, to bio-inspired aerofoil inserts that efficiently produce discrete longitudinal vortices. The synergy achieved in compound systems—through the integration of geometries or the combination of inserts with advanced fluids—is identified as a key mechanism for surpassing traditional performance limits. Furthermore, applications in microscale and phase-change heat transfer, where surface engineering dominates, are explored. The novelty of this work lies in its synthesis of the underlying vortex-generation physics across diverse techniques and scales, introducing ‘flow field programming’ as a forward-looking framework for adaptive thermal management. This evolution—from static geometries to intelligent, responsive designs—is positioned to dramatically improve energy sustainability by enabling more compact, efficient, and adaptive thermal management across power generation, advanced electronics, and renewable energy systems. Full article

Bio-inspired engineering draws on principles refined by natural evolution to tackle persistent challenges in fluid mechanics, structural dynamics, and thermal transport. This article presents a critical, mechanism-driven narrative review that integrates recent advances across three complementary domains that are often treated independently, namely: flow-control strategies such as leading-edge tubercles, alula-like devices, riblets, superhydrophobic skins, and hybrid low-Reynolds-number fliers; fluid-structure interactions inspired by aquatic and aerial organisms that leverage compliant foils, flexible filaments, ciliary arrays, and piezoelectric fluttering plates for propulsion, wake regulation, mixing, and energy harvesting; and phase-change heat-transfer surfaces modeled after stomata, porous biological networks, and textured cuticles that enhance nucleation control, liquid replenishment, and droplet or bubble removal. Rather than providing an exhaustive catalog of biological analogues, this review emphasizes the underlying physical mechanisms that link these domains and enable multifunctional performance. These developments reveal shared physical principles, including multiscale geometry, capillary- and vortex-mediated transport, and compliance-enabled flow tuning, which motivate the integrated treatment of aerodynamic, hydrodynamic, and thermal systems in applications spanning aerospace, energy conversion, and microscale thermal management. The review assesses persistent challenges associated with scaling biological architectures, ensuring long-term durability, and modeling tightly coupled fluid-thermal-structural interactions. By synthesizing insights across flow control, fluid-structure interaction, and phase-change heat transfer, this review provides a unifying conceptual framework that distinguishes it from prior domain-specific reviews. Emerging opportunities in hybrid multi-mechanism designs, data-driven optimization, multiscale modeling, and advanced fabrication are identified as promising pathways to accelerate the translation of biological strategies into robust, multifunctional thermal–fluid systems. Full article

The rapid expansion of health big data, encompassing genomic profiles and wearable device telemetry, has significantly escalated personal privacy risks. This systematic literature review (SLR) synthesizes 86 peer-reviewed studies (2014–2025) through the dual lens of the NIST Cybersecurity and Privacy Frameworks to evaluate emerging risks, mitigation technologies, and regulatory landscapes. Our analysis identifies unauthorized access as the predominant threat, while blockchain-based solutions comprise 22.1% of proposed interventions. However, a comparative evaluation reveals critical performance trade-offs: differential privacy mechanisms incur a 15–35% utility loss, whereas blockchain implementations impose a 40–50% computational overhead. Furthermore, an assessment of major regulatory frameworks (GDPR, HIPAA, PIPL, and emerging regional laws in Sub-Saharan Africa) elucidates significant cross-jurisdictional conflicts. To address these challenges, we propose the Bio-inspired Adaptive Healthcare Privacy (BAHP) framework, validated through retrospective case study analysis, offering a dynamic approach to securing sensitive health ecosystems. Full article

Most pediatric exoskeletons for cerebral palsy (CP) focus on lower-limb assistance and neglect trunk control, limiting rehabilitation outcomes. This study presents a self-aligning trunk–limb exoskeleton that integrates trunk stabilization with active lower-limb support. The design includes a hip–waist rapid adjustment mechanism, a bioinspired gear-rolling knee joint, modular thigh–shank structures, a trunk support module, and a body-weight support device. To enable transparent and coordinated assistance under pathological gait conditions, a continuous gait progress-based multi-joint control framework is developed. Joint motion is described as continuous gait progress over the full gait cycle (0–100%), and joint-specific progress estimates are fused into a unified system-level reference using observability-weighted circular statistics. Inter-joint coordination is achieved through phase-consistency-based temporal modulation implemented, enabling smooth synchronization while preserving joint-level autonomy and motion continuity. Technical evaluation—comprising kinematic misalignment analysis, simulation validation, and gait trials—demonstrated a 66.8% reduction in hip misalignment and an 87.4% reduction in knee misalignment. Gait parameters under exoskeleton-assisted walking closely matched baseline walking, confirming natural kinematic preservation without interference. These results indicate that the proposed trunk–limb exoskeleton improves human–robot synergy, enhances postural stability, and provides a promising solution for pediatric gait rehabilitation in CP. Full article

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. 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

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential. Full article