Segregation Behaviour and Microstructure of Alloy 718VADER Ingot

The segregation behaviour and miclostructure of an alloy 718 VADER ingot in comparison with a VAR ingot were studied. The results show that one serious problem of the alloy 718 VADER ingot is dealing with radial chemistry gradients (especially macro-segregation in Nb). The main factor of Nb gradient forming is the flow of Nb rich fluid through passages among the cellular structures driven by centrifugal force from the rotating mold, Detailed structure and micro-segregation studies on ingots impress us that the VADER process does not show the advantage on the improvement of micro-segregation especially in Nb. The cellular structure produced by the VADER process is no more effective in reducjng Nb micro-segregation during homogenization treatment than the dendritic structure by the VAR process. Experimental results lead us to believe that the VADER process is unsuitable to manufacture alloy 718.

Thalassemia Screening by Sentiment Analysis on Social Media Platform Twitter

Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production,resulting in a drop in the size of red blood cells.In severe forms,it can lead to death.This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival.Therefore,controlling thalassemia is extremely important and is made by promoting screening to the general population,particularly among thalassemia carriers.Today Twitter is one of the most influential social media platforms for sharing opinions and discussing different topics like people’s health conditions and major public health affairs.Exploring individuals’sentiments in these tweets helps the research centers to formulate strategies to promote thalassemia screening to the public.An effective Lexiconbased approach has been introduced in this study by highlighting a classifier called valence aware dictionary for sentiment reasoning(VADER).In this study applied twitter intelligence tool(TWINT),Natural Language Toolkit(NLTK),and VADER constitute the three main tools.VADER represents a gold-standard sentiment lexicon,which is basically tailored to attitudes that are communicated by using social media.The contribution of this study is to introduce an effective Lexicon-based approach by highlighting a classifier calledVADERto analyze the sentiment of the general population,particularly among thalassemia carriers on the social media platform Twitter.In this study,the results showed that the proposed approach achieved 0.829,0.816,and 0.818 regarding precision,recall,together with F-score,respectively.The tweets were crawled using the search keywords,“thalassemia screening,”thalassemia test,“and thalassemia diagnosis”.Finally,results showed that India and Pakistan ranked the highest in mentions in tweets by the public’s conversations on thalassemia screening with 181 and 164 tweets,respectively.

真空电弧双电极重熔GH169合金的铸态组织特征

真空电弧双电极重熔(VADER)是生产细晶锭的一项新的铸造技术,本文对VADER熔炼的GH_(169)合金的铸态组织进行了分析和研究,并与自耗重熔工艺(VAR)做了对比,同时还探讨了Mg对VADER熔炼的GH_(169)合金铸态组织的影响。

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application services.Particularly,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere.To provide an improved service,it is important to analyze online reviews that contain user satisfaction.Several previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey data.These methods may not be appropriate for analyzing the users of mobile applications.To overcome this limitation,several researchers perform user experience analysis through online reviews and star ratings.However,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text.This variation disturbs the performance of machine learning.To alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is introduced.The current study aims to build a more accurate sentiment classifier based on machine learning with VADER.In this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

真空双电极自耗重熔过程中工艺因素对细晶铸锭组织的影响

本文研究了真空双电极自耗高合金钢过程中一些主要工艺因素对细晶锭组织特征的影响。结果表明,电极电流密度、二根水平对置自耗电极在熔化时电极端面上形成的金属液滴下落于锭模中的位置变化、锭模旋转速度、电极和锭模尺寸配比及合金成份不同,是影响铸锭组织的重要因素。目前,已经初步掌握了各个因素优化条件下炼制某些高合金钢细晶铸锭,等轴细晶粒平均直径为0.17毫米,相当于ASTM2～3级。细晶锭直径达到φ180～250毫米,锭重120～200公斤。细晶锭的热锻塑性及材料的综合性能都优于常用的真空自耗方法生产的材料性能。