Effect of Grain Particle Size on Quality of Multi-grain Chips Under Screw Extrusion Processing

Multi-grain chips processed by screw extrusion processing have high nutrition and production value with a low glycemic index.To analyze the effects of particle sizes on the qualities of multi-grain chips extrusion processing by using a single screw extruder,mesh numbers were selected as 80,100 and 120 to describe different grain particle sizes.It was found that the particle sizes of the raw materials had effects on the basic components,sensory properties,texture properties,antioxidant activities and in vitro digestibilities of extruded chips.The results showed that with the decrease of particle sizes,the moisture contents,starch contents of the chips decreased,and fat contents,dietary fiber contents increased.The edible qualities of the chips increased with the decrease of the grain sizes of raw materials.The antioxidant capacities and estimated glycemic indexes of the three kinds of chips showed a trend of decreasing first,and then increasing with the decrease of particle sizes.Correlation analysis showed that the total antioxidant capacities of chips were negatively correlated with the estimated glycemic indexes.The research results provided valuable guidance for the quality processing of multi-grain chips under extrusion processing.

Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.

An efficient wear-leveling-aware multi-grained allocator for persistent memory file systems

Persistent memory(PM)file systems have been developed to achieve high performance by exploiting the advanced features of PMs,including nonvolatility,byte addressability,and dynamic random access memory(DRAM)like performance.Unfortunately,these PMs suffer from limited write endurance.Existing space management strategies of PM file systems can induce a severely unbalanced wear problem,which can damage the underlying PMs quickly.In this paper,we propose a Wear-leveling-aware Multi-grained Allocator,called WMAlloc,to achieve the wear leveling of PMs while improving the performance of file systems.WMAlloc adopts multiple min-heaps to manage the unused space of PMs.Each heap represents an allocation granularity.Then,WMAlloc allocates less-worn blocks from the corresponding min-heap for allocation requests.Moreover,to avoid recursive split and inefficient heap locations in WMAlloc,we further propose a bitmap-based multi-heap tree(BMT)to enhance WMAlloc,namely,WMAlloc-BMT.We implement WMAlloc and WMAlloc-BMT in the Linux kernel based on NOVA,a typical PM file system.Experimental results show that,compared with the original NOVA and dynamic wear-aware range management(DWARM),which is the state-of-the-art wear-leveling-aware allocator of PM file systems,WMAlloc can,respectively,achieve 4.11×and 1.81×maximum write number reduction and 1.02×and 1.64×performance with four workloads on average.Furthermore,WMAlloc-BMT outperforms WMAlloc with 1.08×performance and achieves 1.17×maximum write number reduction with four workloads on average.