Water Gauge Image Denoising Model Based on Improved Adaptive Total Variation

As an important part of water level warning in water conservancy projects,often due to the influence of environmental factors such as light and stains,the acquired water gauge images have sticky,broken and bright spot conditions,which affect the identification of water gauges.To solve this problem,a water gauge image denoising model based on improved adaptive total variation is proposed.Firstly,the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function;secondly,the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points;finally,according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering,the New model can adaptively denoise in the smooth area and protect the edge area,so as to have the characteristics of both edge-preserving denoising.The experimental results show that the new model has a great improvement in image vision,higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio,and an average increase of 9%in structural similarity,which is more beneficial to practical applications.

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Global climate change is expected to accelerate biological invasions,necessitating accurate risk forecasting and management strategies.However,current invasion risk assessments often overlook adaptive genomic variation,which plays a significant role in the persistence and expansion of invasive populations.Here we used Molgula manhattensis,a highly invasive ascidian,as a model to assess its invasion risks along Chinese coasts under climate change.Through population genomics analyses,we identified two genetic clusters,the north and south clusters,based on geographic distributions.To predict invasion risks,we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability,respectively.These approaches yielded distinct predictions:the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster(i.e.,lower invasion risks),while the species distribution model indicated higher future habitat suitability for the same cluster(i.e,higher invasion risks).By integrating these models,we found that the south cluster exhibited minor genome-niche disruptions in the future,indicating higher invasion risks.Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change.Moreover,incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

Adaptive backstepping control for levitation system with load uncertainties and external disturbances

To explore the precise dynamic response of the levitation system with active controller, a maglev guide way-electromagnet-air spring-cabin coupled model is derived firstly. Based on the mathematical model, it shows that the inherent nonlinearity, inner coupling, misalignments between the sensors and actuators, load uncertainties and external disturbances are the main issues that should be solved in engineering. Under the assumptions that the loads and external disturbance are measurable, the backstepping module controller developed in this work can tackle the above problems effectively. In reality, the load is uncertain due to the additions of luggage and passengers, which will degrade the dynamic performance. A load estimation algorithm is introduced to track the actual load asymptotically and eliminate its influence by tuning the parameters of controller online. Furthermore,considering the external disturbances generated by crosswind, pulling motor and air springs, the extended state observer is employed to estimate and suppress the external disturbance. Finally, results of numerical simulations illustrating closed-loop performance are provided.

Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis

Background:Single-cell RNA sequencing(scRNA-seq)data provides a whole new view to study disease and cell differentiation development.With the explosive increment of scRNA-seq data,effective models are demanded for mining the intrinsic biological information.Methods:This paper proposes a novel non-negative matrix factorization(NMF)method for clustering and gene coexpression network analysis,termed Adaptive Total Variation Constraint Hypergraph Regularized NMF(ATV-HNMF).ATV-HNMF can adaptively select the different schemes to denoise the cluster or preserve the cluster boundary information between clusters based on the gradient information.Besides,ATV-HNMF incorporates hypergraph regularization,which can consider high-order relationships between cells to reserve the intrinsic structure of the space.Results:Experiments show that the performances on clustering outperform other compared methods,and the network construction results are consistent with previous studies,which illustrate that our model is effective and useful.Conclusion:From the clustering results,we can see that ATV-HNMF outperforms other methods,which can help us to understand the heterogeneity.We can discover many disease-related genes from the constructed network,and some are worthy of further clinical exploration.

Research on Spatially Adaptive High-Order Total Variation Model for Weak Fluorescence Image Restoration

Confocal laser scanning microscopy(CLSM) has emerged as one of the most advanced fluorescence cell imaging techniques in the field of biomedicine. However, fluorescence cell imaging is limited by spatial blur and additive white noise induced by the excitation light. In this paper, a spatially adaptive high-order total variation(SA-HOTV) model for weak fluorescence image restoration is proposed to conduct image restoration. The method consists of two steps: optimizing the deconvolution model of the fluorescence image by the generalized Lagrange equation and alternating direction method of multipliers(ADMM); using spatially adaptive parameters to balance the image fidelity and the staircase effect. Finally, an comparison of SA-HOTV model and Richardson-Lucy model with total variation(RL-TV model) indicates that the proposed method can preserve the image details ultimately,reduce the staircase effect substantially and further upgrade the quality of the restored weak fluorescence image.

A mesh optimization method using machine learning technique and variational mesh adaptation

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation.In light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense.As the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD calculation.However,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is proposed.The regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression model.After the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area decreases.Benchmark examples are first used to verify the feasibility and effectiveness of the proposed method.And then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our method.Results show that the proposed method significantly improves the accuracy of the local flow features on the adaptive meshes.Our work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

Population transcriptomics reveals a potentially positive role of expression diversity in adaptation

While it is widely accepted that genetic diversity determines the potential of adaptation,the role that gene expression variation plays in adaptation remains poorly known.Here we show that gene expression diversity could have played a positive role in the adaptation of Miscanthus lutarioriparius.RNA-seq was conducted for 80 individuals of the species,with half planted in the energy crop domestication site and the other half planted in the control site near native habitats.A leaf reference transcriptome consisting of 18,503 high-quality transcripts was obtained using a pipeline developed for de novo assembling with population RNA-seq data.The population structure and genetic diversity of M.lutarioriparius were estimated based on 30,609 genic single nucleotide polymorphisms.Population expression（Ep） and expression diversity（Ed）were defined to measure the average level and the magnitude of variation of a gene expression in the population,respectively.It was found that expression diversity increased while genetic Resediversity decreased after the species was transplanted from the native habitats to the harsh domestication site,especially for genes involved in abiotic stress resistance,histone methylation,and biomass synthesis under water limitation.The increased expression diversity could have enriched phenotypic variation directly subject to selections in the new environment.