Search Results (971)

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

As an essential component of dynamic loads, traditional time-domain identification methods exhibit notably insufficient accuracy when dealing with distributed dynamic load identification under unknown initial conditions. This paper explores a novel and effective methodology, utilizing the Bayesian framework and orthogonal polynomials fitting, to reconstruct the distributed dynamic loads of thin plate structures over any arbitrary time period under unknown initial conditions. The forced vibration under the orthogonal basis function loads and the free decay vibration after the removal of basis function loads are used to characterize the forced vibration induced by the identified distributed dynamic load and the decay vibration caused by unknown initial conditions, respectively. By integrating structural dynamic responses within a multi-layer Bayesian framework, the time history and spatial distribution of the load over any arbitrary time period are identified. The innovation of this methodology is that the contribution of the initial conditions to the response is independently characterized by the free decay response caused by the removal of the basis function loads, which effectively resolves the issue of insufficient identification accuracy in existing traditional time-domain methods due to unknown initial conditions. Consequently, the accuracy and reliability of the distributed dynamic load identification is significantly enhanced, which provides a new solution for distributed dynamic load identification under unknown initial conditions. Additionally, simulation cases involving various load conditions and noise levels are discussed under unknown initial conditions over arbitrary time periods. The results demonstrate that the proposed method achieves favorable identification accuracy and robustness under unknown initial conditions. Full article

This paper investigates the spatial asymptotic behaviour of solutions to a coupled thermoelastic system of Green–Naghdi Type III in an exterior domain of ${\mathbb{R}}^3$. The system couples elastodynamics with a second-order heat conduction law and contains indefinite cross-coupling terms between mechanical and thermal variables. By constructing a weighted energy functional and deriving a first-order differential inequality in the radial direction, we establish a Phragmén–Lindelöf alternative: for each fixed time, the total energy either grows exponentially or decays exponentially as $r\to \infty$, with an explicit decay rate that depends on the material coefficients and a free parameter $\omega$. This dichotomy itself reveals a fundamental symmetry in the spatial behaviour—growth versus decay—which is intimately linked to the radial symmetry of the exterior geometry and the inherent structure of the coupled system. The result provides a complete characterization of spatial stability for this thermoelastic model in unbounded exterior domains. Full article

This paper is concerned with the defocusing energy-critical wave equation with a localized semilinearity and damping, set in the exterior of two disjoint, strictly convex obstacles in ${\mathbb{R}}^3$. The trapping nature of this geometry, due to a periodic ray bouncing between the obstacles, is the main obstruction to decay. Under an exterior geometric control condition, the local energy is shown to decay exponentially, uniformly over data supported in a fixed ball with energy below any prescribed level. The rate is exactly that of the damped linear flow, and hence independent of the data and of the energy level; only the constant depends on the latter. The approach relies on Lafontaine’s lossless Strichartz estimates, a Lax–Phillips and microlocal defect measure analysis of the damped linear flow, and a Duhamel–Gronwall scheme. Full article

This work develops a hybrid kernel sparse Gaussian process regression integrated with kernel density estimation (HCSGPR-UQ) to resolve three critical drawbacks of conventional lithium-ion battery State of Energy (SOE) estimators: degraded accuracy under dynamic loads, high computational overhead, and inadequate uncertainty quantification. A composite covariance kernel is built by weighting the radial basis function (RBF) and Matérn 5/2 kernels to simultaneously model global smooth SOE decay trends and local nonlinear fluctuations induced by abrupt current/temperature variations. Inducing-point sparse approximation is adopted to accelerate model inference, while kernel density estimation (KDE) generates nonparametric prediction bounds for quantitative uncertainty evaluation. Validations are carried out on a 75 Ah traction lithium-ion cell across −5 °C to 35 °C under Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) cycles. Experimental results reveal that the proposed method yields mean absolute errors (MAEs) of only 0.32% (DST) and 0.38% (BBDST), runs roughly 15× faster than full Gaussian process regression (GPR), and attains a 94.7% coverage probability for nominal 95% prediction intervals. Balancing estimation precision, real-time inference speed and statistical reliability, the proposed framework delivers a viable online SOE estimation solution for vehicle battery management systems (BMSs). Full article

To address the inherent limitations of the traditional Aquila Optimizer (AO)—specifically slow convergence, susceptibility to local optima, and limited high-dimensional adaptability—this study proposes a multi-strategy Improved Aquila Optimizer algorithm. Key enhancements include the integration of a logarithmically decaying tangent flight factor to optimize high-dimensional solution distributions, and a dual-layer t-distribution adaptive perturbation model to dynamically regulate search density. Additionally, to solve path-planning problems under strict constraints, we incorporate a prior feasible region initialization, a continuous-to-discrete mapping correction, and a local fine-search mechanism for trajectory smoothing. The proposed Improved Aquila Optimizer algorithm is systematically evaluated against the original AO and six popular algorithms (PSO, SSA, GWO, DBO, DE, and GA) across 23 benchmark functions, the CEC2017 suite, and multi-scale grid maps. The results demonstrate that the Improved Aquila Optimizer algorithm achieves an order-of-magnitude improvement in convergence reliability. By prioritizing absolute search stability and robustness in high-dimensional tasks, the proposed algorithm attains an optimal balance between convergence quality and practical engineering efficiency, proving exceptionally effective in complex path-planning scenarios. Full article

South Africa’s updated Nationally Determined Contribution sets a 2030 emissions ceiling of 420 MtCO₂. Using historical data from 2000 to 2023 and projections to 2050, this study identifies optimal decarbonization pathways by integrating logistic decay model, linear programming, genetic algorithm optimization (Decay–LP–GA), and Pareto analysis. A prior study notes that 2023 emissions lie substantially below the NDC ceiling, leaving 239.1 MtCO₂ of unused carbon space. However, the present study finds that optimized pathways transcend rather than utilize this space, achieving 67–85% emissions below 2023 levels. Notably, aggressive near-term coal phase-out increases 2050 emissions by disrupting efficiency investment, indicating that timing governs long-term outcomes. The optimal strategy therefore prioritizes slow near-term coal reduction (0.69% annually) to allow front-loaded efficiency gains (4.46% annually) through 2035, followed by accelerated phase-out to achieve 96% reduction by 2050. This sequencing reduces cumulative emissions by 8.0% (300 MtCO₂) relative to the LP minimum. The Pareto frontier spans 51.5–92.6 MtCO₂ in 2030 and 52.8–64.2 MtCO₂ in 2050, with all Pareto-optimal solutions requiring coal shares below 40% by 2030, conflicting with Integrated Resource Plan constraints. Consequently, maintaining a ≥40% coal floor raises minimum feasible emissions to 101.5 MtCO₂, generating a 22.8–49.4 MtCO₂ feasibility gap. The findings show that optimal strategy is not to use its remaining carbon space, but to render the 420 MtCO₂ target redundant through front-loaded efficiency gains and strategically timed coal phase-out, providing clear direction for sustainable climate finance. Full article

We investigate the asymptotic behavior of a Timoshenko beam system in which the free end carries a tip mass subject to a restoring spring force and a tempered Caputo fractional damping force with parameter $\eta >0$ (the case $\eta =0$ is left as an open problem). Using a diffusive state space reformulation of the fractional term, the original problem is embedded into an augmented first-order evolution system on a carefully constructed Hilbert space. Well-posedness is established via the Lumer–Phillips theorem. A spectral analysis of the governing operator, combined with the Arendt–Batty–Lyubich–Vũ theorem, shows that the associated $C_0$-semigroup is strongly asymptotically stable even when the classical equal-wave-speeds condition for the Timoshenko system is violated, provided $\eta >0$. Moreover, resorting to the Borichev–Tomilov resolvent method, we reduce the polynomial energy decay to a single resolvent exponent $\mathcal{\ell }>0$, so that the energy of every solution issued from a datum in the domain of the generator decays at least as fast as $t^{-1/\mathcal{\ell }}$ as $t\to +\infty$. We establish the estimates that control ℓ; we identify the mechanism that governs it—the inertia of the tip mass, which screens the damper at high frequency—and we measure ℓ numerically. In particular, the exponent is not dictated by the second-order character of the Timoshenko operator, contrary to what a comparison with the fourth–order beam might suggest. Full article

We study a delayed semilinear evolution equation with a Caputo time derivative and the spectral fractional Dirichlet Laplacian on a bounded connected domain. The model separates two forms of memory: the Caputo operator retains the distributed Volterra history, whereas the nonlinear production samples the single past state $u(t-\tau )$. Working in the strongly continuous phase space $C_0(\Omega )$, we prove local well-posedness, positivity, a sup-norm continuation criterion, and a compatible weak formulation. In the delayed-source case with $\mu =0$, the solution exists globally and remains bounded on every finite time interval, while the first Dirichlet mode admits an explicit recursive sequence of positive lower bounds across successive delay windows. In the dissipative case $\mu >0$, $p>q>1$, histories satisfying the explicit smallness conditions remain in an invariant order interval and the $L^2$-energy decays at a Mittag–Leffler rate. The scalar computations are presented only as heuristic first-mode surrogate experiments. In addition, an independent spatially resolved sine spectral-Galerkin/L1 computation of the PDE, with temporal and spectral refinement studies, is included as a numerical illustration. Full article

We propose an entropy-stable numerical scheme for an augmented one-dimensional blood flow model by constructing an explicit diffusion operator independent of the reconstruction method used for the scaled entropy variables. The diffusion term plays an important role in entropy-stable schemes, i.e., schemes satisfying a discrete entropy inequality. In general, the diffusion operator involves a diffusion matrix that depends on the scaled right eigenvectors and on the reconstruction of the scaled variables. We derive a simple, explicit expression for this operator that avoids computing the full set of scaled eigenvectors. The performance of the scheme is assessed through numerical experiments, focusing on Riemann problems, which confirm its ability to capture shock waves accurately and provide numerical evidence of entropy decay for non-smooth solutions. Full article

Reliable fruit quality assessment is crucial for ensuring food safety and value in modern agriculture. However, many current approaches still rely heavily on visual cues, making it difficult to assess internal quality indicators such as sweetness or internal decay. To address this limitation, we propose HSAF-Net, a heterogeneous multimodal fusion framework integrating spiking neural networks (SNNs) and artificial neural networks (ANNs) for comprehensive, non-destructive fruit quality assessment. Specifically, the SNN encodes near-infrared (NIR) spectral signals to extract internal sugar-related features, whereas the ANN-based TH-YOLOv8 model detects external surface defects from high-resolution RGB images. A microsecond-level synchronous acquisition scheme is implemented to ensure precise alignment between the NIR and RGB modalities. To effectively combine heterogeneous features, we design a Heterogeneous Modality Attention (HMA) mechanism that dynamically fuses multi-source information based on task-specific relevance. Compared with image-only detection, the proposed framework explicitly separates internal biochemical sensing from external defect localization and then integrates their complementary decisions in a unified grading pipeline. Experimental results on 616 pear samples demonstrate that the HSAF-Net achieves 95.2% classification accuracy, 95.1% mAP95, and an internal defect miss rate as low as 7.5%, outperforming conventional single-modality and early-fusion baselines by a notable margin. The system maintains a real-time inference speed of 55 ms per sample on the Ascend Atlas 200DK A2 edge platform, validating its deployment potential. The current evaluation is based on crisp pear samples collected under controlled acquisition conditions; therefore, broader cross-variety and cross-season validation remains necessary before large-scale commercial deployment. This study presents an end-to-end multimodal SNN-ANN fusion architecture tailored for fruit grading and provides a scalable, high-precision solution for post-harvest quality assessment with broad applicability to other agricultural products. Full article

Ciliary-driven flow refers to the movement of fluid by the rhythmic and coordinated beating cilia and finds applications in the respiratory tract, fallopian tube, embryonic node, brain ventricles, paranasal sinuses, and understanding flows in the auditory tube. Previous research on cilia-driven flow has demonstrated forced convective flow with no-slip boundary conditions, which is crucial in mucus clearance and is not firmly stuck to the periciliary layer. This paper develops the mixed convective flow of Ellis fluid near the periciliary layer with a lubricated surface. The partial slip boundary condition provides reduced friction near the periciliary layer for the Ellis fluid flow. The momentum and energy equations are simplified by the lubrication approach, and the resulting problem is solved analytically. This research achieves the exact solutions for the temperature and velocity profiles for the consistency index 3. The findings show that the mucus flow along the lubricated surface is enhanced by the slip parameter and viscosity (shear-thinning fluid) parameter beta, but the flow across the trachea decays due to the slip and viscosity parameters. The mucus temperature rises due to the radiation and Prandtl number, which also help to reduce the frictional forces near the periciliary layer and facilitate faster mucociliary clearance. Full article

In this work, a comprehensive analysis of the relationship between photoluminescent, thermo-oxidative, and photocatalytic (upon simulated sunlight exposure) properties of ZnO powders is performed. The correlation between the X-ray diffraction and microscopic data is studied. ZnO powders of various sizes and morphologies were used: pseudo-spherical nanoparticles (30–50 nm), submicron faceted crystallites (100–500 nm), and plate- and rod-like microstructures (up to 20 μm). The mean specific surface area values were 32 m²/g, 3.8 m²/g, and 2.6 m²/g for pseudo-spherical nanoparticles, submicron faceted crystallites, and plate- and rod-like microstructures, respectively. According to the XRD data, microstresses and carbon-based impurities were present in ZnO nanoparticles, which is characteristic of nanomaterials synthesized at low temperatures. According to the photoluminescence spectroscopy data, the emission in ZnO was reduced due to high defectiveness, and characteristic emission bands indicated the presence of organic impurities. Upon long signal registration times, an intensive luminescence band with an effective maximum at 579 nm occurred, which indicated the presence of long-term components exhibiting decay times τ ~300 μs. According to the XRD data, the crystal structure parameters of ZnO submicro- and microparticles were close, with no impurities present. In their photoluminescence spectra, pronounced UV and defect-related bands were present with intensity ratios of 11.6 and 6.88, respectively. The decrease in the UV and defect-related luminescence band intensity ratios indicates deviation from the stoichiometry toward an increased Zn over oxygen content. At long signal registration times, in submicron ZnO particles, a luminescence band with maxima at 425 and 490 nm is present, which decays rapidly. An emission band in the 530 nm region is also present, which decays for ≤80 μs, and a weak long-wavelength emission decaying for ~100 μs. At long delay and strobe times (up to milliseconds), only an emission in the 460 nm region is observed, which we connect to the triplet–singlet transition of a defect center (F*, F⁺*). At lower intensities, an emission connected to the surface contamination by organic impurities is observed. In photoluminescence spectra of ZnO microparticles, no long-wavelength emission components are observed. However, upon immersing into methylene blue solution, a modification of the surface and UV region of the spectra is observed with signs of charge carrier recombination rate acceleration. It is shown that the catalytic action of ZnO powders in polyethylene thermo-oxidation processes is determined by a combination of factors. In addition to dispersity and concentration, which are the key parameters, the morphology of ZnO particles, the presence of impurities, the surface state, and the distribution of active sites have a significant influence on catalysis. It has been experimentally demonstrated that these secondary factors can markedly affect the rate of radical formation in polyethylene films and alter their resistance to oxidation. ZnO nanoparticles exhibited low catalytic activity in both photocatalysis (rate constant 0.146 min⁻¹) and thermocatalysis due to the high defect density of the crystallites and the presence of carbon-containing impurities. Submicron ZnO particles, owing to a high carrier generation rate and suppressed recombination (via trapping), demonstrated the highest photoactivity (rate constant 0.729 min⁻¹). Submicron ZnO particles exhibit a catalytic effect on the thermo-oxidation of polyethylene (PE films); however, at concentrations above 8 wt.% a transition to an inhibiting effect is observed. ZnO microparticles catalyzed the oxidation of PE films over a broader concentration range (1–12 wt.%), with oxidation inhibition observed only at 18 wt.%. At the same time, they demonstrated moderate photocatalytic activity (rate constant 0.256 min⁻¹). These characteristics of the samples correlate with data obtained by microscopy, photoluminescence spectroscopy, and X-ray diffraction analysis. Full article

Electrochemical oxidation is an effective method for degrading environmentally harmful textile dyes that are difficult to remove by conventional treatments. The novel graphite–Co₃O₄ anode, prepared by electrochemically modifying recycled graphite tubes for AAS samples and characterized by SEM, EDX, FTIR, XRD, and BET, was used for electrochemical degradation of RB 4 dye in model solutions and textile dyeing effluent. Modification did not disrupt the graphite crystal structure but altered its surface properties, improving its dye degradation performance. It increased the decolorization rate constant of a model solution from 0.0148 min⁻¹ to 0.0753 min⁻¹ and maximum COD decay from 59% to 91%, reducing decolorization energy consumption from 3.26 kWh m⁻³ to 0.89 kWh m⁻³. Degradation at the graphite–Co₃O₄ anode proceeded via ·OH radicals and most likely the Co³⁺/Co²⁺ redox couple. It followed the pseudo-first-order kinetics. Pastel- and dark-shade dyeing textile effluents (containing 53 mg dm⁻³ and 159 mg dm⁻³ RB 4, respectively) were decolorized in about 70 and 110 min; COD decay reached about 87% and 80% after 180 min of electrolysis, respectively. The corresponding energy consumption was 1.75 kWh m⁻³, 2.39 kWh m⁻³, 2.97 kWh m⁻³, and 2.91 kWh m⁻³, respectively. The anode was efficient and stable in the given working conditions. Full article

This study investigated the influence of a novel flow–through electro-Fenton (EF) reactor configuration on hydrodynamics and dye removal efficiency using Pt and boron-doped diamond (BDD) anodes coupled with a carbon–PTFE gas diffusion cathode. In this work, an innovative pre–pilot-scale reactor operating in recirculation mode was used to treat 100 mg L⁻¹ Calcon dye solutions in 0.05 mol L⁻¹ Na₂SO₄ at pH 3.0 under electrochemical oxidation (EO) with electrogenerated H₂O₂ (EO-H₂O₂), EF, and Photoelectro-Fenton (PEF) conditions. The effects of applied current density (30–90 mA cm⁻²) and Fe²⁺ concentration (0.25–0.75 mmol L⁻¹) were evaluated through color removal, TOC decay, and identification of oxidation intermediates. The hydrodynamic characterization results revealed flow conditions in a transitional region between laminar and turbulent flow (Re = 3.6 × 10³; Sh = 246). Comparing EF and EO-H₂O₂ processes, when Fe²⁺ was added to the solution, it significantly accelerated discoloration and, consequently, dye degradation in the former, while the absence of Fe²⁺ resulted in slower discoloration kinetics, reaching only 85.8% color removal after 180 min in the latter. The best performance was obtained with 0.50 mmol L⁻¹ Fe²⁺ in EF, achieving >98% discoloration. Among the investigated processes, PEF exhibited the highest mineralization efficiency. TOC removals using BDD as the anode efficiently reached high mineralization levels of 83.88%, 86.99%, and 93.38% for EO-H₂O₂, EF, and PEF, respectively, while Pt as the anode achieved 81.80%, 85.14%, and 91.63%. Overall, the BDD/PEF system showed the best degradation and mineralization performance. The proposed reactor was designed at the pre-pilot scale and incorporates vertical recirculation flow with hydrodynamic optimization, enabling efficient mass transfer and improved oxidant generation. The study provides practical insights into the reactor engineering aspects required for the future scale-up of EF technologies. Full article