Cell Biomechanical Modeling Based on Membrane Theory with Considering Speed Effect of Microinjection

As an effective method to deliver external materials into biological cells, microinjection has been widely applied in the biomedical field. However, the cognition of cell mechanical property is still inadequate, which greatly limits the efficiency and success rate of injection. Thus, a new rate-dependent mechanical model based on membrane theory is proposed for the first time. In this model, an analytical equilibrium equation between the injection force and cell deformation is established by considering the speed effect of microinjection. Different from the traditional membrane-theory-based model, the elastic coefficient of the constitutive material in the proposed model is modified as a function of the injection velocity and acceleration, effectively simulating the influence of speeds on the mechanical responses and providing a more generalized and practical model. Using this model, other mechanical responses at different speeds can be also accurately predicted, including the distribution of membrane tension and stress and the deformed shape. To verify the validity of the model, numerical simulations and experiments are carried out. The results show that the proposed model can match the real mechanical responses well at different injection speeds.Comment: 10 pages, 12 figures, submitted to IEEE TMech

Effect of viscosity and heterogeneity on dispersion in porous media during miscible flooding processes

In this paper, a mathematical model has been developed to quantitatively examine the effect of viscosity and heterogeneity on dispersion in porous media at the pore scale during miscible flooding processes. More specifically, the Navier-Stokes equation and advection-diffusion equation are coupled with supplementary equations to describe the solvent transport behaviour. Two-dimensional heterogeneous models are numerically developed as a function of porosity and permeability, assuming that the grain sizes satisfy normal distribution. In addition, the performance of miscible hydrocarbon gas injection in heterogeneous porous media is comprehensively evaluated. It is found that a larger aspect ratio (ratio of pore throat size) in the single non-flowing pore model results in a greater asymmetry of the concentration curve. As for single non-flowing pore models and heterogeneous models, the dispersion coefficients increase with the expansion of the non-flowing domain. Both the heterogeneity of porous media and the variable viscosity of th fluid mixture contribute to the asymmetry of the concentration curve in the heterogeneous model. A negative correlation is established between the sorting coefficients of pore throat size and the power-law coefficients. As for slug injection, the injected solvent slug size along the longitudinal direction does not effectively influence the longitudinal length of the mixing zone for a given porous medium and fluids, though the Peclet number and the porosity greatly affect the length and concentration distribution of the mixing zone.Cited as: Bai, Z., Song, K., Fu, H., Shi, Y., Liu, Y., Chen, Z. Effect of viscosity and heterogeneity on dispersion in porous media during miscible flooding processes. Advances in Geo-Energy Research, 2022, 6(6): 460-471. https://doi.org/10.46690/ager.2022.06.0

SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation

Traditional sequential recommendation methods assume that users' sequence data is clean enough to learn accurate sequence representations to reflect user preferences. In practice, users' sequences inevitably contain noise (e.g., accidental interactions), leading to incorrect reflections of user preferences. Consequently, some pioneer studies have explored modeling sequentiality and correlations in sequences to implicitly or explicitly reduce noise's influence. However, relying on only available intra-sequence information (i.e., sequentiality and correlations in a sequence) is insufficient and may result in over-denoising and under-denoising problems (OUPs), especially for short sequences. To improve reliability, we propose to augment sequences by inserting items before denoising. However, due to the data sparsity issue and computational costs, it is challenging to select proper items from the entire item universe to insert into proper positions in a target sequence. Motivated by the above observation, we propose a novel framework--Self-augmented Sequence Denoising for sequential Recommendation (SSDRec) with a three-stage learning paradigm to solve the above challenges. In the first stage, we empower SSDRec by a global relation encoder to learn multi-faceted inter-sequence relations in a data-driven manner. These relations serve as prior knowledge to guide subsequent stages. In the second stage, we devise a self-augmentation module to augment sequences to alleviate OUPs. Finally, we employ a hierarchical denoising module in the third stage to reduce the risk of false augmentations and pinpoint all noise in raw sequences. Extensive experiments on five real-world datasets demonstrate the superiority of \model over state-of-the-art denoising methods and its flexible applications to mainstream sequential recommendation models. The source code is available at https://github.com/zc-97/SSDRec.Comment: ICDE 202

Evaluation of geographically weighted logistic model and mixed effect model in forest fire prediction in northeast China

IntroductionForest fires seriously threaten the safety of forest resources and human beings. Establishing an accurate forest fire forecasting model is crucial for forest fire management.MethodsWe used different meteorological and vegetation factors as predictors to construct forest fire prediction models for different fire prevention periods in Heilongjiang Province in northeast China. The logistic regression (LR) model, mixed-effect logistic (mixed LR) model, and geographically weighted logistic regression (GWLR) model were developed and evaluated respectively.ResultsThe results showed that (1) the validation accuracies of the LR model were 77.25 and 81.76% in spring and autumn fire prevention periods, respectively. Compared with the LR model, both the mixed LR and GWLR models had significantly improved the fit and validated results, and the GWLR model performed best with an increase of 6.27 and 10.98%, respectively. (2) The three models were ranked as LR model < mixed LR model < GWLR model in predicting forest fire occurrence of Heilongjiang Province. The medium-and high-risk areas of forest fire predicted by the GWLR model were distributed in western and eastern parts of Heilongjiang Province in spring, and western part in autumn, which was consistent with the observed data. (3) Driving factors had strong temporal and spatial heterogeneities; different factors had different effects on forest fire occurrence in different time periods. The relationship between driving factors and forest fire occurrence varied from positive to negative correlations, whether it’s spring or autumn fire prevention period.DiscussionThe GWLR model has advantages in explaining the spatial variation of different factors and can provide more reliable forest fire predictions

Post-impoundment biomass and composition of phytoplankton in the Yangtze River

Damming, and thus alteration of stream flow, promotes higher phytoplankton populations and encourages algal blooms (density &gt; 10(6) cells L-1) in the Three Gorges Reservoir (TGR). Phytoplankton composition and biomass were studied in the Yangtze River from March 2004 to May 2005. 107 taxa were identified. Diatoms were the dominant group, followed by Chlorophyta and Cyanobacteria. In the Yangtze River, algal abundance varied from 3.13 x 10(3) to 3.83 x 10(6) cells L-1, and algal biomass was in the range of 0.06 to 659 mg C m(-3). Levels of nitrogen, phosphorus and silica did not show consistent longitudinal changes along the river and were not correlated with phytoplankton parameters. Phytoplankton abundance was negatively correlated with main channel discharge (Spearman r = -1.000, P &lt; 0.01). Phytoplankton abundance and biomass in the Yangtze River are mainly determined by the hydrological conditions rather than by nutrient concentrations.Damming, and thus alteration of stream flow, promotes higher phytoplankton populations and encourages algal blooms (density > 10(6) cells L-1) in the Three Gorges Reservoir (TGR). Phytoplankton composition and biomass were studied in the Yangtze River from March 2004 to May 2005. 107 taxa were identified. Diatoms were the dominant group, followed by Chlorophyta and Cyanobacteria. In the Yangtze River, algal abundance varied from 3.13 x 10(3) to 3.83 x 10(6) cells L-1, and algal biomass was in the range of 0.06 to 659 mg C m(-3). Levels of nitrogen, phosphorus and silica did not show consistent longitudinal changes along the river and were not correlated with phytoplankton parameters. Phytoplankton abundance was negatively correlated with main channel discharge (Spearman r = -1.000, P < 0.01). Phytoplankton abundance and biomass in the Yangtze River are mainly determined by the hydrological conditions rather than by nutrient concentrations

Effect of adding copper oxide nanoparticles on the mass/heat transfer in falling film absorption

Absorber is an essential component that affects the efficiency of absorption refrigeration unit. Falling film absorption is one of the most widespread forms of the heat/mass transfer in absorption system. In this paper, based on the software COMSOL Multiphysics, the finite element method is used to establish the model of falling film absorption. The falling film absorption properties of nanofluids was studied by adding CuO nanoparticles. The results reveal that as the film flow rate increases, the average mass transfer flux rises first and then decreases. The average mass transfer flux increases with the rise of the concentration of solution at the inlet, the decrease of the temperature of solution at the inlet and the reduction of cooling water inlet temperature. After adding copper oxide nanoparticles to lithium bromide solution, the vapor absorption performance of lithium bromide solution can be significantly improved