AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments

The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic decision-makers.To this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 situation.We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,sentiment analysis,location entity detection,and decomposition tree analysis.We deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,2022.The system was evaluated on a limited dataset between July 15,2021 to August 10,2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,respectively.As of 12 January,2022,the proposed solution has detected 43,169 locations using entity recognition.According to the best of our knowledge,this study is the first to report location intelligence with entity detection,sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.

Simulation annealing diagnosis algorithm method for optimized forecast of the dynamic response of floating offshore wind turbines

Design of floating offshore wind turbines(FOWTs)needs reliable and innovative technologies to overcome the challenges on how to better predict the dynamic responses in terms of aero-hydro-servo-elastic disciplines.This paper aims to demonstrate the optimized prediction of the dynamic response of FOWTs by Simulation annealing diagnosis algorithm(SADA).SADA is an Artificial Intelligence technology-based method,which utilizes the advantages of numerical simulation,basin experiment and machine learning algorithms.The actor network in deep deterministic policy gradient(DDPG)is adopted to take actions to adjust the Key disciplinary parameters(KDPs)in each loop according to the feedback of 6DOF motions of platform in dynamic response analysis.The results demonstrated that the mean values of the platform's motions and rotor axial thrust force could be predicted with higher accuracy.On this basis,other physical quantities that designers are more concerned about but cannot be obtained from experiments and actual measurements will be predicted by SADA with more credibility.This SADA method differs from traditional supervised learning applications in renewable energy,which do not need to be provided physical quantities with strong direct correlation.All targets can be artificially set for SADA to obtain a better self-learning performance.In general,designers can use SADA to get a more accurate and optimized prediction of the dynamic response of FOWTs,especially those physical quantities that cannot be directly obtained through the basin experiments.

Aligned Organization of Synapses and Mitochondria in Auditory Hair Cells

Recent studies have revealed great functional and structural heterogeneity in the ribbon-type synapses at the basolateral pole of the isopotential inner hair cell(IHC).This feature is believed to be critical for audition over a wide dynamic range,but whether the spatial gradient of ribbon morphology is fine-tuned in each IHC and how the mitochondrial network is organized to meet local energy demands of synaptic transmission remain unclear.By means of three-dimensional electron microscopy and artificial intelligence-based algorithms,we demonstrated the cell-wide structural quantification of ribbons and mitochondria in mature mid-cochlear IHCs of mice.We found that adjacent IHCs in staggered pairs differ substantially in cell body shape and ribbon morphology gradient as well as mitochondrial organization.Moreover,our analysis argues for a location-specific arrangement of correlated ribbon and mitochondrial function at the basolateral IHC pole.