A More Efficient Approach for Remote Sensing Image Classification

Over the past decade,the significant growth of the convolutional neural network(CNN)based on deep learning(DL)approaches has greatly improved the machine learning(ML)algorithm’s performance on the semantic scene classification(SSC)of remote sensing images(RSI).However,the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms,e.g.,automation and simplicity,partially lost.Traditional ML strategies(e.g.,the handcrafted features or indicators)and accuracy-aimed strategies with a high trade-off(e.g.,the multi-stage CNNs and ensemble of multi-CNNs)are widely used without any training efficiency optimization involved,which may result in suboptimal performance.To address this problem,we propose a fast and simple training CNN framework(named FST-EfficientNet)for RSI-SSC based on an EfficientNetversion2 small(EfficientNetV2-S)CNN model.The whole algorithm flow is completely one-stage and end-to-end without any handcrafted features or discriminators introduced.In the implementation of training efficiency optimization,only several routine data augmentation tricks coupled with a fixed ratio of resolution or a gradually increasing resolution strategy are employed,so that the algorithm’s trade-off is very cheap.The performance evaluation shows that our FST-EfficientNet achieves new state-of-the-art(SOTA)records in the overall accuracy(OA)with about 0.8%to 2.7%ahead of all earlier methods on the Aerial Image Dataset(AID)and Northwestern Poly-technical University Remote Sensing Image Scene Classification 45 Dataset(NWPU-RESISC45D).Meanwhile,the results also demonstrate the importance and indispensability of training efficiency optimization strategies for RSI-SSC by DL.In fact,it is not necessary to gain better classification accuracy by completely relying on an excessive trade-off without efficiency.Ultimately,these findings are expected to contribute to the development of more efficient CNN-based approaches in RSI-SSC.

Improvement in potassium ion batteries electrodes: Recent developments and efficient approaches

Demand for efficient and continuous application for high-grid energy storage systems involves the study towards novel battery technologies. Hence, considering the vast naturally available resources of potassium all over the world and its encouraging intercalation chemistries, it has recently enticed attention in electrochemical energy storage industry in the form of potassium ion batteries (PIBs). The major factor in this K+ based battery, is to develop efficient approaches to manufacture electrode substance to intercalate its big size potassium ions with considerable voltage, kinetics, charge/discharge capacity, capacity retention, cost, etc. This study contributes in the recent developments of anode and cathode materials for PIBs, including several electrode materials in regards to synthesis, structure, electrochemical performance, and K-storage mechanisms. Finally, the review contributes to provide helpful sources for the increasing number of scientists working in this industry regarding its critical issues and challenges and also to indicate the future direction of electrode materials in PIBs.

The Lexical Approach and Its Implications for EFL Teaching in China

This paper briefly introduces the principles of the Lexical Approach, based on the classroom observations and surveys of six college English teachers who have taken lexical ideas into their classrooms, demonstrates the changes in methodology and efficiency of classroom teaching which involves implementing the Lexical Approach, and emphasizes that implementing the Lexical Approach in classroom teaching is more successful in training students＇ comprehensive skills than previous practice.

Genetic Dissection of Grain Size Traits Through Genome-Wide Association Study Based on Genic Markers in Rice

Grain size plays a significant role in rice,starting from affecting yield to consumer preference,which is the driving force for deep investigation and improvement of grain size characters.Quantitative inheritance makes these traits complex to breed on account of several alleles contributing to the complete trait expression.We employed genome-wide association study in an association panel of 88 rice genotypes using 142 new candidate gene based SSR(cgSSR)markers,derived from yield-related candidate genes,with the efficient mixed-model association coupled mixed linear model for dissecting complete genetic control of grain size traits.A total of 10 significant associations were identified for four grain size-related characters(grain weight,grain length,grain width,and length-width ratio).Among the identified associations,seven marker trait associations explain more than 10%of the phenotypic variation,indicating major putative QTLs for respective traits.The allelic variations at genes OsBC1L4,SHO1 and OsD2 showed association between 1000-grain weight and grain width,1000-grain weight and grain length,and grain width and length-width ratio,respectively.The cgSSR markers,associated with corresponding traits,can be utilized for direct allelic selection,while other significantly associated cgSSRs may be utilized for allelic accumulation in the breeding programs or grain size improvement.The new cgSSR markers associated with grain size related characters have a significant impact on practical plant breeding to increase the number of causative alleles for these traits through marker aided rice breeding programs.