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.

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.