Tribological behavior of polydopamine/polytetrafluoroethylene coating on laser textured stainless steel with Hilbert curves

Shallow Hilbert curve patterns with easily programmable texture density were selected for laser texturing of stainless steel substrates.Two different texture path segment lengths(12 and 24 μm)and four different laser power percentages(5%,10%,15%,and 20%)were investigated.The textured and smooth substrates were coated with thin polydopamine/polytetrafluoroethylene(PDA/PTFE)coatings for tribological property assessment.The effects of texture density(texture area coverage)and laser power on the durability and friction of the coated surfaces were studied.Laser texturing the substrates improved the coating durability up to 25 times,reduced the friction coefficient,and prevented coating global delamination.The textures fabricated with a laser power of 15%and a texture path segment length of 12 μm yielded the best coating durability.The textures provided the interlocking for the PTFE coating and thus prevented its global delamination.Furthermore,the PTFE inside the texture grooves replenished the solid lubricant worn away in the wear track and prolonged the coating wear life.

An Improved Hilbert Curve for Parallel Spatial Data Partitioning

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.

Generating and AnalyzingN-di mensional Hilbert Cell

In this paper, two algorithms are presented for generating two code scan lists of an N dimensional Hilbert cell, and a formal proof of the backward encoding algorithm is given. On the basis of the self similarity properties of a Hilbert curve, this paper gives a novel algorithm for generating a static evolvement rule table through analyzing a Hilbert cell. By looking up the static evolvement rule table, the N dimensional Hilbert mappings are efficiently implemented.

Spatial skyline query method based on Hilbert R-tree in multi-dimensional space

In view of the shortage of the spatial skyline query methods(SSQ methods) in dealing with the problem of skyline query in multidimensional space, a spatial skyline query method based on Hilbert R-tree in multidimensional space is proposed. This method takes the advantages of Hilbert R-tree which combines R-tree and Hilbert curve with high efficiency and dimensionality reduction. According to the number of query points, the proposed method in static query point environment is divided into single query point of SSQ method(SQ-HSKY algorithm) and multi-query points of SSQ method(MQP-HSKY algorithm). The SQ-HSKY method uses the spatial relationship between objects to propose pruning strategy and the skyline set in the filtering and refining process are computed. The MQP-HSKY method uses the topological relationship between data points and query points to prune non skyline points and generate the dominant decision circle to obtain the global skyline set. Theoretical study and experiments confirm the effectiveness and superiority of these methods on the skyline query.

A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'resources.Sustainable Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations.Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this work.We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph store.First,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is described.Then,a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework.The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare organizations.Our approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval times.It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

EPIMR:Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic diseases.Due to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology approaches.Many EPI predictors have been developed,but their prediction accuracy still needs to be enhanced.Here,we design a new model named EPIMR to identify enhancer-promoter interactions.First,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial information.Second,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction levels.Finally,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction information.Experimental results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling datasets.Furthermore,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction ability.In conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription mechanisms.Our code and results are available at https://github.com/guofei-tju/EPIMR.