Uniform Parameters Research Articles

Nonlocalized Conductive Paths Construction and In-depth Mechanism Analysis for the Robust Resistive Switching in Halide Perovskites.

The conductive paths (CPs) established by defects in halide perovskites (HPs) tend to be disrupted under external influences, leading to deterioration of their RRAM performances. Here we propose an effective strategy to enhance the CPs in HP RRAMs by doping Ag+ to partially substitute Pb2+ in MAPbI3, which facilitates the nonlocalized growth of Ag CPs and thereby improves the stability of CPs. The optimal doped device demonstrates excellent RRAM performances including high ON/OFF ratios (>107), long retention (>105 s), large endurance (>103 cycles), uniform parameters, and excellent yield. In-depth mechanism investigation illustrates the homogeneous distribution of doping Ag+ and the migration of a sufficient quantity of Ag+ contribute to the formation of stable, nonlocalized Ag CPs in HP. This strategy possesses the superiorities of not requiring the participation of an active electrode, simple material preparation, and broad applicability, which provides a new perspective for developing high-performance HP RRAMs.

Understanding the variation in morphology and rhizome yield of Indian ginger (Zingiber officinale Rosc.) through distinctness, uniformity and stability (DUS) descriptors: A natural marker to enhance crop productivity

Ginger (Zingiber officinale) is a widely consumed and economically significant plant known for its medicinal and culinary properties. Understanding the diversity within ginger germplasm is vital for its successful use in breeding programs and conservation efforts. Morphological characterization plays a key role in identifying distinct varieties, ensuring uniform traits, protecting breeders' rights and conserving genetic diversity, all of which support commercial production and drive innovation in breeding. The present investigation was carried out at the Hybrid Rice Evaluation Centre, Gudalur, Nilgiris, with 32 genotypes to study the variability for identifying the best performing ginger genotypes. Genotypes were examined for 2 qualitative and 8 quantitative traits using Distinctness, Uniformity and Stability (DUS) parameters. Out of the traits assessed, plant height, number of leaves on the main stem and dry recovery were monomorphic, with no variation within the population. However, 4 characters, normal growth habit, shoot diameter, leaf length and leaf width, exhibited 2 distinct variations within the genotypes, indicating dimorphism. Additionally, 3 traits such as the number of tillers per clump, rhizome thickness and rhizome shapes showed multiple variations among the studied genotypes, illustrating polymorphism within the population. Principal component analysis was conducted on 7 principal components with eigenvalues of more than one accounted for 98.28 % of the total variability. Among these traits, leaf length, rhizome thickness and number of tillers demonstrated the highest variation, while the remaining traits showed lower variability. Based on PCA and cluster analysis, the genotypes Nadia, Manipur Local, Aswathy, Nagaland Local, Bhaise, ACC 578, Himachal Local, Rio De Janeiro, IISR Vajra, Humnabad Local, Hassan Local, Thalavadi Local and Chikkamagalore Local 2 were identified as the most diverse genotypes.

Concurrent multi-scale design optimization of fiber-reinforced composite material based on an adaptive normal distribution fiber optimization scheme for minimum structural compliance and additive manufacturing

Structural lightweight is a core technical requirement for the structural design of aerospace and new energy power equipment structures. For multi-scale variable stiffness design optimization of discrete fiber-reinforced composite laminates, one of the challenges is how to avoid the explosion of design variable combinations caused by the increase in the number of candidate discrete fiber laying angles. The Normal Distribution Fiber Optimization (NDFO) interpolation scheme has the numerical advantage that the number of design variables does not increase with an increase in the number of candidate discrete fiber laying angles. However, the traditional NDFO interpolation scheme uses uniform penalty parameters across all elements, which means that normalizing the penalty parameters for all the elements ignores the convergence differences of discrete fiber laying angles in different elements within the macro-scale structure topology. This leads to time-consuming and unstable optimization iteration of the macro-scale structural topology and micro-scale discrete fiber laying angle selection. Especially, it easily causes the micro-scale discrete fiber laying angle selection to fall into the local optimum prematurely. Therefore, considering the difficulties and challenges of the traditional NDFO interpolation scheme in the multi-scale variable stiffness design optimization of fiber-reinforced composites. This paper proposes an Adaptive Normal Distribution Fiber Optimization (ANDFO) interpolation scheme, and the feedback mechanism of the convergence rate of the element design variable and the objective function is introduced to achieve the adaptive reduction of the penalty parameters. Based on the proposed ANDFO interpolation scheme, a multi-scale design optimization model of fiber-reinforced composite laminates is established, considering the macro-scale structure topology and micro-scale discrete fiber laying angel selection. The explicit sensitivity of the objective function of minimizing structural compliance to the macro-scale topological design variables and the micro-scale fiber laying angle design variables is derived. Considering the manufacturability of additive manufacturing based on the optimized design results, a multi-scale nonlinear continuous filtering strategy for discrete fiber laying angle is adopted to improve the continuity of the local fiber laying path. Numerical examples systematically present the coupling effects of macro-scale structural topology and micro-scale fiber laying path, multi-scale nonlinear discrete fiber continuous filtering laying path structure, and continuous fiber additive manufacturing multi-scale optimized structure. The proposed ANDFO scheme provides a new theoretical and methodological approach for the lightweight and integrated multi-scale design and manufacturing of fiber-reinforced composite laminates through additive manufacturing technology.

Gaussian level-set percolation on complex networks.

We present a solution of the problem of level-set percolation for multivariate Gaussians defined in terms of weighted graph Laplacians on complex networks. It is achieved using a cavity or message passing approach, which allows one to obtain the essential ingredient required for the solution, viz. a self-consistent determination of locally varying percolation probabilities. The cavity solution can be evaluated both for single large instances of locally treelike graphs, and in the thermodynamic limit of random graphs of finite mean degree in the configuration model class. The critical level h_{c} of the percolation transition is obtained through the condition that the largest eigenvalue of a weighted version B of a nonbacktracking matrix satisfies λ_{max}(B)|_{h_{c}}=1. We present level-dependent distributions of local percolation probabilities for Erdős-Rényi networks and and for networks with degree distributions described by power laws. We find that there is a strong correlation between marginal single-node variances of a massless multivariate Gaussian and local percolation probabilities at a given level h, which is nearly perfect at negative values h, but weakens as h↗0 for the system with power-law degree distribution, and generally also for negative values of h, if the multivariate Gaussian acquires a nonzero mass. The theoretical analysis simplifies in the case of random regular graphs with uniform edge weights of the weighted graph Laplacian of the system and uniform mass parameter of the Gaussian field. An asymptotic analysis reveals that for edge weights K=K(c)≡1 the critical percolation threshold h_{c} decreases to 0, as the degree c of the random regular graph diverges. For K=(c)=1/c, however, the critical percolation threshold h_{c} is shown to diverge as c→∞.

Two-Dimensional, Multi-Reaction, and Multi-Species Porous Electrode Modeling of Metal Oxide Electroreduction with Applications in Nuclear Fuel Reprocessing and Iron Oxide Reduction

Many electrochemical processes involve the reduction of a porous solid metal oxide into pure metal such as the Fray-Farthing-Chen (FFC) Cambridge process 1 for titanium and ULCOWIN 2 process for iron. With optimal electrolysis cell designs, these processes would produce metal efficiently without energy losses to parasitic side reactions. Predictive modeling of the reaction rate (current) distribution underlying these processes can allow for increased energy efficiency and higher metal electrowinning yields. This would be beneficial to the fields of nuclear fuel reprocessing and sustainable metals production, which utilize electro-reduction of uranium oxides and iron oxides, respectively. This presentation will cover the development of a two-dimensional, multi-reaction model that simulates the current distribution during oxide reduction and models the potential profiles of both the metal oxide phase and the liquid electrolyte phase within the oxide pores. The model applies the concepts of porous electrode theory developed by Newman and Tobias 3 but modifies it to incorporate multiple reactions.4, 5 The model is then applied to uranium oxide reduction in a molten salt electrolyte, and aqueous processing of iron oxide ores for iron electrowinning. This modeling study helps establish the main controlling parameters for the current distribution uniformity, i.e., the solid electronic conductivity of the oxide phase, the liquid electrolyte conductivity of the molten (or aqueous) electrolyte, the tortuosity and porosity of the porous oxide matrix, and the exchange current densities of the metal oxide and the liquid electrolyte reactions. Experimentally measured current distribution profiles on a porous electrode comprising iron oxide particles will be shown as validation of model findings.References G. Z. Chen, D. J. Fray, and T. W. Farthing, Nature, 407 (6802), 361-364 (2000).M. Abdul Quader, S. Ahmed, S. Z. Dawal, and Y. Nukman, Renewable and Sustainable Energy Reviews, 55 537-549 (2016).J. S. Newman and C. W. Tobias, Journal of The Electrochemical Society, 109 (12), 1183 (1962).D. R. Baker and M. W. Verbrugge, Journal of The Electrochemical Society, 165 (16), A3952-A3964 (2018).D. R. Baker, M. W. Verbrugge, and W. Gu, Journal of The Electrochemical Society, 166 (4), A521-A531 (2019).

Enhancing Uniformity, Read Voltage Margin, and Retention in Three-Dimensional and Self-Rectifying Vertical Pt/Ta2O5/Al2O3/TiN Memristors.

This study introduces a Ta2O5-based self-rectifying memristor (SRM) with an Al2O3 interfacial layer adopted to improve switching uniformity, read voltage margin, and long-term retention. The Pt/Ta2O5/Al2O3/TiN (PTAT) device exhibits a 105 rectification ratio, 104 on/off ratio, 2 × 106 endurance, and retention of 104 s at 150 °C. A 3-layer 4 × 4 vertical resistive random access memory structure exhibits uniform switching parameters. The coefficient of variation (CV) for device-to-device measurements is 0.23 for the low resistance state (LRS) and 0.22 for the high resistance state (HRS), while for cycle-to-cycle measurements, the CV is 0.38 for the LRS and 0.11 for the HRS. Finally, the present study demonstrates the superior performance of the PTAT devices in the context of hardware-aware training for a fully connected neural network implementation. These advancements position the PTAT device as a promising candidate for high-density three-dimensional storage class memory and low-power neural networks, offering the consistent performance and reliability necessary for future high-density storage applications.

Enhancing Daily Crash Count Prediction using Deep Learning with Window Size Selection and Seasonality Predictor Integration

The escalating need for proactive safety measures, coupled with advancements in data collection and analytical techniques, has significantly refined the accuracy of crash count predictions, shifting from annual scales to finer daily or hourly estimates. This research places emphasis on recurrent neural networks (RNNs), specifically the long short-term memory (LSTM) model, acknowledged for effectively managing sequential data in time series predictions. Paramount considerations encompass the treatment of input data, including the decision to incorporate temporal features alongside endogenous historical target values, and the establishment of an optimal window size for data input. Despite the critical nature of these facets, exhaustive studies concurrently investigating both under controlled conditions are scarce. This research addresses this gap, assessing diverse scenarios featuring distinct temporal treatments and window sizes, and employing an LSTM model with uniform fine-tuned parameters to ensure a fair comparison. Findings indicate a significant variation in performance among models employing different window sizes and month predictor integration, under identical RNN structures and LSTM configurations. The best model outperformed the least effective by roughly 30% concerning root mean square error (RMSE) and ranking correlation between predicted and actual target crash counts in the test datasets. Unlike seasonality predictor treatment, diverse window size selections did not lead to statistically significant differences in model performance. Generally, a window size of 3 performed worst under most conditions, followed by a size of 14. Sizes of 7 and 28 performed best in nearly an equal numbers of cases.

Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth

Abstract. Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5–AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µg m−3, with regional values ranging from 59.8 µg m−3 in North America to more than 190 µg m−3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η, exhibiting a population-weighted mean difference in aerosol composition of 12.3 µg m−3, which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µg m−3, which reflects spatial variation in the column–surface relationship.

Optimization of Connection Parameters of Self-Adaptive Modular Floating Wind Farms

Connection parameters are the key factors influencing the responses of modular floating structures; due to the complexity of structural response properties, the assessment and optimization of connection parameters are still vital issues in designing modular floating structures. In the present work, for a novel structural configuration of self-adaptive modular floating wind farms based on existing works from the case of a mobile offshore base, a quantified approach for the assessment of connection parameters is established based on frequency domain numerical analysis taking into account both the economic effects and generalized performance. Based on the quantified assessment, an optimization process is carried out, to obtain a connection parameter combination. From the optimized connection parameters, it can be found that the most appropriate tri-axial stiffness according to present studies is in the magnitude from 1 × 106 to 7 × 107 N/m, and the damping ratio is close to 1.0 for most connection structures. By contrast with feasible uniform connection parameters, the optimization methodology is confirmed to be able to reduce both connection parameters and responses. Then, a time domain approach for the calculation of motions of interconnected modular floating structures taking into account geometry nonlinearity is proposed and obtained in good accordance with the frequency domain results, and the effectiveness of both the frequency domain and time domain is validated.

Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.

Compared to artificial neural networks (ANNs), spiking neural networks (SNNs) present a more biologically plausible model of neural system dynamics. They rely on sparse binary spikes to communicate information and operate in an asynchronous, event-driven manner. Despite the high heterogeneity of the neural system at the neuronal level, most current SNNs employ the widely used leaky integrate-and-fire (LIF) neuron model, which assumes uniform membrane-related parameters throughout the entire network. This approach hampers the expressiveness of spiking neurons and restricts the diversity of neural dynamics. In this paper, we propose replacing the resistor in the LIF model with a discrete memristor to obtain the heterogeneous memristive LIF (MLIF) model. The memristance of the discrete memristor is determined by the voltage and flux at its terminals, leading to dynamic changes in the membrane time parameter of the MLIF model. SNNs composed of MLIF neurons can not only learn synaptic weights but also adaptively change membrane time parameters according to the membrane potential of the neuron, enhancing the learning ability and expression of SNNs. Furthermore, since the proper threshold of spiking neurons can improve the information capacity of SNNs, a learnable straight-through estimator (LSTE) is proposed. The LSTE, based on the straight-through estimator (STE) surrogate function, features a learnable threshold that facilitates the backward propagation of gradients through neurons firing spikes. Extensive experiments on several popular static and neuromorphic benchmark datasets demonstrate the effectiveness of the proposed MLIF and LSTE, especially on the DVS-CIFAR10 dataset, where we achieved the top-1 accuracy of 84.40 .

Towards sharper excess risk bounds for differentially private pairwise learning

Pairwise learning is a vital part of machine learning. It depends on pairs of training instances, and is naturally fit for modeling relationships between samples. However, as a data driven paradigm, it faces huge privacy issues. Differential privacy (DP) is a useful tool to protect the privacy of machine learning, but corresponding excess population risk bounds are loose in existing DP pairwise learning analysis. In this paper, we propose a gradient perturbation algorithm for pairwise learning to get better risk bounds under Polyak–Łojasiewicz condition, including both convex and non-convex cases. Specifically, for the theoretical risk bound in expectation, previous best results are of rates O(pn2ϵ2+1n) and O(pnϵ+1n) under strongly convex condition and convex conditions, respectively. In this paper, we use the on-average stability and achieve an O(min{pn1.5ϵ+p1.5n2.5ϵ3,pn2ϵ2+1n}) bound, significantly improving previous bounds. For the high probability risk bound, previous best results are analyzed by the uniform stability, and O(βnU+pnϵ) excess population risk bounds are achieved under strongly convex or convex conditions, where βnU is the traditional pairwise uniform stability parameter, it is large since it considers the worst case of the loss sensitivity. In this paper, we propose the pairwise locally elastic stability and improve the high probability bound to O(βEn+pnϵ), in which the pairwise locally elastic stability parameter βE≪βnU because it considers the average sensitivity of the pairwise loss function.

3-D geometric design of microwaveable food products for optimal heating uniformity based on machine learning-supervised multiphysics models

Multiphysics models can assist in geometric design of frozen microwaveable foods, but the conventional 'parametric-sweeping' strategy is computationally intensive. This study develops an online machine learning (ML)-supervised multiphysics modeling strategy to simultaneously optimize multiple geometrical parameters (e.g., top surface width, top surface length, and the ratio of top-to-bottom dimensions) for optimal microwave heating uniformity. First, one or more paired geometric dimensional parameters and heating uniformity results obtained from multiphysics modeling are used as initial training data for the ML optimization process that integrates Gaussian Process Regression (GPR) and Bayesian optimization. Then, the multiphysics modeling procedure is supervised by an ML process to generate new paired geometry-heating uniformity results to expand the training dataset. The loop of multiphysics modeling and ML optimization is conducted to effectively identify geometry designs with good heating uniformity. Results indicate that the ML-supervised optimization strategy can efficiently identify good geometric designs with low heating uniformity by using much fewer multiphysics models than the parametric sweep approach. The ML-supervised approach also exhibits great robustness, delivering good performance even with small (as little as one model) and randomly selected initial training data. This ML-supervised approach is flexible and can be modified to meet specific needs for broad industrial implementation in the development of microwaveable food.

DEVELOPMENT OF AN ALGORITHM FOR CALCULATING COMPONENT COMPOSITION TO IMPROVE THE DEVELOPMENT OF OFFSHORE OIL FIELDS IN THE NORTH CASPIAN BASIN

The paper presents an algorithm to reconstruct the component composition of the mixture based on the results of production modeling in the black oil format and a fixed set of tables with the results of phase equilibrium calculations in a given pressure range. The approach is based on analyzing the ratio of flow rates for each phase and the ratio between average reservoir pressure and saturation pressure. The algorithm takes into account the possibility of oil degassing when saturation pressure decreases, as well as gas breakthrough of gas caps to the bottoms of producing wells.Implementation of the method is based on a unified thermodynamic model of multilayer reservoir, built on the principles of a single set of HC mixture components and uniform parameters of the equation of state on a set of fluid samples for all modeling stages: reservoir model, model of the gathering system and site facilities. In addition to steady-state flow calculations performed together with reservoir modeling, the unified model is planned to be used for dynamic flow calculations in the corresponding simulator (well start/stop), which will allow solving production problems in the process of operation of wells with complex completions.The conversion algorithm is automated using modern high-level tools: Python and data automation components from MS Excel, which eliminates the need for manual data input at all stages and makes it possible to obtain the estimated component composition of produced fluid for any set of wells and for any time step within the forecasting horizon.The obtained compositions are used to verify the reproduction of the main controlled parameters (oil density and gas oil ratio) and can be used for subsequent calculations of fluid transportation and treatment systems.The implemented algorithm has been tested for stability at different ratios of gas and oil flow rates by wells and meets the practical requirements of engineering calculations — the error of oil density is not higher than 2 % and gas factor is not higher than 10 %.

Numerical investigation of the reflection of unsteady decelerating incident shock waves

The incident shock attenuation phenomenon in shock tube has received widespread interest because of its inevitable influence on experimental gas properties. However, few studies have investigated the cascading effects of the resulting nonuniformity on shock reflection in shock tunnels before the nozzle. This paper describes a numerical study on the unsteady reflection of a decelerating incident shock driven by a decelerating piston, as a simplification of the complex nonideal factors. The initial decay and uniform parameter distributions are generated based on the non-inertial frame. The results indicate that the existence of the nonuniform area forces the reflected shock wave region to undergo a transition process of attenuation and then stability. The final values of the gas parameters in zone 5 will, therefore, deviate from those given by the traditional relation for an ideal shock tube, which are only dependent on the terminal shock Mach number. This affects the determination of the total temperature T5, which is difficult to measure directly. We discuss the reconstruction of the nonuniform region with the attenuation trajectory of the incident shock wave and find that there is no one-to-one correspondence between this trajectory and the resulting nonuniformity, which introduces additional uncertainties to the predictions. Thus, an analogy method is developed through the multilevel block building algorithm, allowing the total temperature T5 to be determined using the total pressure p5 considering the effect of nonuniformity. Further assessments verify the applicability of this method in cases with multidimensional and viscous interactions.

Stellar spectral template library construction based on generative adversarial networks

Stellar spectral template libraries play an important role in the automated analysis of stellar spectra. Synthetic template libraries cover a very large parameter space but suffer from poor matching with observed spectra. In this study, we propose a synthetic-to-observed spectral translation (SOST) method based on generative adversarial networks. The SOST method is able to calibrate synthetic spectra by converting them to the corresponding observed spectra. We applied this method to Kurucz synthetic spectra and observed spectra data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). After that, we constructed a stellar spectral library with uniform and broad parameter distributions using the SOST-corrected Kurucz synthetic spectra. Our stellar spectral template library contains 2431 spectra spanning a parameter space of 3500–8000 K for effective temperature (Teff), 0.0–5.0 dex for surface gravity (log g), and −2.0–0.5 dex for metallicity ([Fe/H]). The spectra in the library have a resolution of R ∼ 1800 and cover the wavelength range 3900–8700 Å. In order to verify the accuracy of this template library, we used the template library and the template-matching algorithm to derive the parameters of the PASTEL database. Compared to measurements using the original synthetic template library, the accuracies of the three parameters, Teff, log g, and [Fe/H], are improved, from 140 K, 0.31 dex, and 0.21 dex to 121 K, 0.26 dex, and 0.13 dex, respectively. In addition, we re-parameterised more than six million stellar spectra released by LAMOST DR8.

3D numerical study of NH3/H2 MILD combustion in a reversed flow MILD combustion furnace

Combustion of fuels to generate energy is essential for numerous residential and industrial human endeavors. In contrast to fossil fuels, the demand for carbon-free fuels such as ammonia and hydrogen to meet climate commitments is rapidly growing. This study conducts a numerical investigation of NH3/H2 MILD combustion. A three-dimensional CFD simulation model of a 90-degree sector of a reversed flow MILD combustion furnace is presented. The SAGE detailed chemical kinetics solver featuring the CEU-NH3 mechanism is employed with dynamic mechanism reduction for computational efficiency. The effects of varying the hydrogen mole fraction in the inlet fuel mixture from 0 to 50 % on the thermal characteristics, reaction zone, and emissions are investigated. The findings show that temperature homogeneity, assessed through temperature uniformity parameters and standard deviation, reveals a diminishing trend in temperature homogeneity with increased hydrogen concentration. The boundaries of the reaction zone are determined according to the NNH and N mole fractions, and both criteria are approximately identical. All the investigated cases match the MILD combustion definition; however, the cases with H2 concentrations ranging from 10 to 40 % fall under the MILD-like combustion regime. Emissions analysis shows that for all investigated cases, the ammonia slip emissions and the exhaust N2O emissions are below 1 and 0.5 ppm, respectively. The NOx emissions are found to increase abruptly with increasing hydrogen concentration from 0 to 35 % and subsequently undergo minor fluctuations.

In-situ rapid nondestructive characterization of multiple irregular three-dimensional grain boundary structures and electrical properties by nanorobot with SEM in ZnO

To address the problem of spatial quantification of irregular grain boundaries, a unique approach for the nondestructive characterization of multiple irregular three-dimensional grain boundaries is proposed using nanorobots with scanning electron microscopy (SEM) to accomplish in-situ rapid synchronized quantification of structures and electrical properties. By constructing a three-dimensional trigonometric geometric mapping relationship, multiple irregular three-dimensional grain boundary structures and electrical properties can be rapidly quantified. Experimental results demonstrate that it is feasible to accomplish in-situ rapid nondestructive characterizations. The two-dimensional profile shapes of the targeted grain boundaries at each layer exhibit irregular and non-uniform features in the Y-Z plane. The hypotenuse length between the virtual space tilting lines presents a three-dimensional characteristic in the X-Z plane. The spatial tilt angle of the virtual space tilting line can be calculated using the right-triangle relation. The unequal numbers of grains along the same tilted hypotenuse direction with equal lengths directly illustrate the existence of irregular and nonlinear grain boundaries in bulk ZnO ceramics. The relatively uniform spatial sizes of the grain boundary illustrate the coupled spatial characteristics existing in the irregular micro-structures. The relatively uniform equivalent circuit fitting parameters confirm the existence of relatively uniform doping components in bulk ZnO ceramics. Furthermore, this method provides a unique approach for the rapid nondestructive quantitative analysis of multiple three-dimensional irregular grain boundaries in bulk polycrystalline materials.

Lab on a bead with oscillatory centrifugal microfluidics for fast and complete mixing enables fast and accurate biomedical assays

Rapid mixing and precise timing are key for accurate biomedical assay measurement, particularly when the result is determined as the rate of a reaction: for example rapid immunoassay in which the amount of captured target is kinetically determined; determination of the concentration of an enzyme or enzyme substrate; or as the final stage in any procedure that involves a capture reagent when an enzyme reaction is used as the indicator. Rapid mixing and precise timing are however difficult to achieve in point-of-care devices designed for small sample volumes and fast time to result. By using centrifugal microfluidics and transposing the reaction surface from a chamber to a single mm-scale bead we demonstrate an elegant and easily manufacturable solution. Reagents (which may be, for example, an enzyme, enzyme substrate, antibody or antigen) are immobilised on the surface of a single small bead (typically 1–2 mm in diameter) contained in a cylindrical reaction chamber subjected to periodically changing rotational accelerations which promote both mixing and uniform mass-transfer to the bead surface. The gradient of Euler force across the chamber resulting from rotational acceleration of the disc, dΩdisc/dt, drives circulation of fluid in the chamber. Oscillation of Euler force by oscillation of rotational acceleration with period, T, less than that of the hydrodynamic relaxation time of the fluid, folds the fluid streamlines. Movement of the bead in response to the fluid and the changing rotational acceleration provides a dynamically changing chamber shape, further folding and expanding the fluid. Bead rotation and translation driven by fluid flow and disc motion give uniformity of reaction over the surface. Critical parameters for mixing and reaction uniformity are the ratio of chamber radius to bead radius, rchamber/rbead, and the product Trchamber(dΩdisc/dt), of oscillation period and Euler force gradient across the fluid. We illustrate application of the concept using the reaction of horse radish peroxidase (HRP) immobilised on the bead surface with its substrate tetramethylbenzidine (TMB) in solution. Acceleration from rest to break a hydrophobic valve provided precise timing for TMB contact with the bead. Solution uniformity from reaction on the surface of the bead in volumes 20–50 uL was obtained in times of 2.5 s or less. Accurate measurement of the amount of surface-bound HRP by model fitting to the measured kinetics of colour development at 10 s intervals is demonstrated.

Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2

Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.

Nonlinear vibrations of porous-hyperelastic cylindrical shell under harmonic force using harmonic balance and pseudo-arc length continuation methods

The resonant responses are investigated for the porous-hyperelastic Mooney–Rivlin cylindrical shell subjected to a radial harmonic excitation. Considering the higher-order shear deformation theory (HSDT), fourth-order strain-displacement relations are derived, which include the radial geometric imperfections varying along the thickness direction. Using the porous-hyperelastic Mooney–Rivlin constitution relation and Lagrange equation, the differential governing equations of motion are obtained for the porous-hyperelastic cylindrical shells. The resonant conditions are presented and the accuracy of the mathematical models is verified. The harmonic balance and pseudo-arc length continuation methods are used to obtain the amplitude-frequency and forced-amplitude curves. The stability of the solutions is analyzed by Floquet theory. The effects of the external excitation amplitudes, structure parameters and porosity infill parameters on the linear frequencies, amplitude-frequency responses and force-amplitude responses are discussed for the imperfect porous-hyperelastic cylindrical shells. The results show that the linear frequencies of the porous-hyperelastic cylindrical shells increase obviously as the uniform and sigmoid function porosity parameters reach the certain values. The increase of the structure parameters enhances the response amplitudes of the first-order mode and minified response amplitudes of the second-order mode. The decreasing porosity ratios weaken the softening nonlinear behaviors of the porous-hyperelastic cylindrical shells. With the changes of the external excitation amplitudes and structure parameters, the motion of the porous-hyperelastic cylindrical shell indicates that the synchronous vibrations occur with the period and chaotic vibrations alternately.