Upper-rim-modified thioether thiacalix[4]arene used for liquid-liquid extraction of Au(Ⅲ)and Pd(Ⅱ)in hydrochloric acid medium

Gold(Au)and palladium(Pd)play an increasing role in the production and human life;Therefore,it is of great significance to study their recovery.A 5,11,17,23-tetra-ethylthio-25,26,27,28-tetra-hydroxyl thiacalix[4]arene(TCAET)was synthesized specifically for the capture of Au(Ⅲ)and Pd(Ⅱ)from HCl medium by liquid-liquid extraction.In a 0.1 mol·L^(-1)HCl medium,the transfer of Au(Ⅲ)and Pd(Ⅱ)from the aqueous phase to the organic phase was highly efficient,with a transfer ratio of 100%for Au(Ⅲ)and 98%for Pd(Ⅱ).Furthermore,the extraction equilibrium time for Au(Ⅲ)was just 5 min.Job's method data demonstrated that TCAET formed complexes with Au(Ⅲ)and Pd(Ⅱ)in a ratio of 2:3 and 1:1,respectively,during the extraction process.TCAET showed high selectivity toward Pd(Ⅱ)and Au(Ⅲ)over other competing metal ions.Moreover,both Au(Ⅲ)and Pd(Ⅱ)could be successfully stripped from the loaded organic phases with a 1.0 mol·L^(-1)thiourea in 0.5 mol·L^(-1)HCl and 0.5 mol·L^(-1)thiourea in 0.5 mol·L^(-1)HCl,respectively.Results obtained from five consecutive extraction-stripping cycles showed good reusability of TCAET toward Au(Ⅲ)and Pd(Ⅱ)recovery.The conclusion can provide a certain reference for thiacalixarene in the recovery of precious metal species.

The extraction of aromatics using N-methylpyrrolidone: Liquid-liquidequilibrium determination and mechanism exploration

LLE data of cyclooctane/3-methylpentane + benzene/toluene + N-methylpyrrolidone (NMP) at 298.15 Kand 313.15 K under a pressure of 101.3 kPa were measured in this work. The Othmer-Tobias and Handequations were adopted to validate the reliability of LLE data, where the correlation coefficients (R2) wereclose to unity, indicating the high reliability of the experimental data. The experimental data were analyzed using the distribution coefficient (D) and separation factor (S), and the effect of NMP extracting benzene and toluene from aromatics was explored. Meanwhile, the reason for the different extractionefficiencies of benzene and toluene using NMP was analyzed by quantum chemical calculations. TheNRTL and UNIQUAC thermodynamic models were used to correlate the liquid–liquid equilibrium data,and the relevant binary interaction parameters were obtained. The calculated root mean square deviation(RMSD) were all less than 0.0063, indicating that the obtained binary interaction parameters can be usedto simulate and calculate the extraction of aromatics using NMP.

An Ensemble Detection Method for Shilling Attacks Based on Features of Automatic Extraction

Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

Purpose:Automatic keyphrase extraction(AKE)is an important task for grasping the main points of the text.In this paper,we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approach:We regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT,which was released by Google in 2018.We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain,which contains 100,000 abstracts as training set,6,000 abstracts as development set and 3,094 abstracts as test set.We use unsupervised keyphrase extraction methods including term frequency(TF),TF-IDF,TextRank and supervised machine learning methods including Conditional Random Field(CRF),Bidirectional Long Short Term Memory Network(BiLSTM),and BiLSTM-CRF as baselines.Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.Findings:Compared with character-level BiLSTM-CRF,the best baseline model with F1 score of 50.16%,our character-level sequence labeling model based on BERT obtains F1 score of 59.80%,getting 9.64%absolute improvement.Research limitations:We just consider automatic keyphrase extraction task rather than keyphrase generation task,so only keyphrases that are occurred in the given text can be extracted.In addition,our proposed dataset is not suitable for dealing with nested keyphrases.Practical implications:We make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts(CAKE)publicly available for the benefits of research community,which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/value:By designing comparative experiments,our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models.And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Exploring the Potentialities of Automatic Extraction of University Webometric Information

Purpose:The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’websites.The information automatically extracted can be potentially updated with a frequency higher than once per year,and be safe from manipulations or misinterpretations.Moreover,this approach allows us flexibility in collecting indicators about the efficiency of universities’websites and their effectiveness in disseminating key contents.These new indicators can complement traditional indicators of scientific research(e.g.number of articles and number of citations)and teaching(e.g.number of students and graduates)by introducing further dimensions to allow new insights for“profiling”the analyzed universities.Design/methodology/approach:Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web.This study implements an advanced application of the webometric approach,exploiting all the three categories of web mining:web content mining;web structure mining;web usage mining.The information to compute our indicators has been extracted from the universities’websites by using web scraping and text mining techniques.The scraped information has been stored in a NoSQL DB according to a semistructured form to allow for retrieving information efficiently by text mining techniques.This provides increased flexibility in the design of new indicators,opening the door to new types of analyses.Some data have also been collected by means of batch interrogations of search engines(Bing,www.bing.com)or from a leading provider of Web analytics(SimilarWeb,http://www.similarweb.com).The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register(https://eter.joanneum.at/#/home),a database collecting information on Higher Education Institutions(HEIs)at European level.All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.Findings:The main findings of this study concern the evaluation of the potential in digitalization of universities,in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’websites.These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.Research limitations:The results reported in this study refers to Italian universities only,but the approach could be extended to other university systems abroad.Practical implications:The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites.The approach could be applied to other university systems.Originality/value:This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping,optical character recognition and nontrivial text mining operations(Bruni&Bianchi,2020).

Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

The vast availability of information sources has created a need for research on automatic summarization. Current methods perform either by extraction or abstraction. The extraction methods are interesting, because they are robust and independent of the language used. An extractive summary is obtained by selecting sentences of the original source based on information content. This selection can be automated using a classification function induced by a machine learning algorithm. This function classifies sentences into two groups: important or non-important. The important sentences then form the summary. But, the efficiency of this function directly depends on the used training set to induce it. This paper proposes an original way of optimizing this training set by inserting lexemes obtained from ontological knowledge bases. The training set optimized is reinforced by ontological knowledge. An experiment with four machine learning algorithms was made to validate this proposition. The improvement achieved is clearly significant for each of these algorithms.

A Semi-automatic Method Based on Statistic for Mandarin Semantic Structures Extraction in Specific Domains

This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract

Template Guided Live Wire and Its Application on Automatic Extraction of Tongue in Digital Image

In this paper, we propose a novel automatic object extraction algorithm, named the Template Guided Live Wire, based on the popularly used live-wire techniques. We discuss in details the novel method’s applications on tongue extraction in digital images. With the guides of a given template curve which approximates the tongue’s shape, our method can finish the extraction of tongue without any human intervention. In the paper, we also discussed in details how the template guides the live wire, and why our method functions more effectively than other boundary based segmentation methods especially the snake algorithm. Experimental results on some tongue images are as well provided to show our method’s better accuracy and robustness than the snake algorithm.

A review of automatic detection of epilepsy based on EEG signals

Epilepsy is a common neurological disorder that occurs at all ages.Epilepsy not only brings physical pain to patients,but also brings a huge burden to the lives of patients and their families.At present,epilepsy detection is still achieved through the observation of electroencephalography(EEG)by medical staff.However,this process takes a long time and consumes energy,which will create a huge workload to medical staff.Therefore,it is particularly important to realize the automatic detection of epilepsy.This paper introduces,in detail,the overall framework of EEG-based automatic epilepsy identification and the typical methods involved in each step.Aiming at the core modules,that is,signal acquisition analog front end(AFE),feature extraction and classifier selection,method summary and theoretical explanation are carried out.Finally,the future research directions in the field of automatic detection of epilepsy are prospected.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

Simulation of Solvent Extraction for Naphthenic Lubricating Base Oils

Solvent extraction is the process of separating aromatics from vacuum distillates for the production oflubricating base oils. In this study, the authors use dimethyl sulfoxide (DMSO) instead of furfural as solvent, in light of itshigher selectivity, to obtain extracts with a high aromatic content for naphthenic lubricating base oils. We systematicallyinvestigated effects of the solvent-to-oil (S/O) ratio and extraction temperature on the yield of the extract, efficiency ofaromatic removal, and composition of the extracts and raffinates. The results showed that the aromatic content of extractsfor naphthenic oils could reach a high value of about 80%. The solvent maintained a high selectivity for aromatics fornaphthenic oils even under a high S/O ratio and a high extraction temperature. Moreover, the efficiency of aromatic removalfor naphthenic lubricating base oils could be enhanced by increasing either the S/O ratio or the extraction temperature,although these measures had limited effects in practice. Following this, we used the non-random two-liquid (NRTL) modelbased on the pseudo-component approach to simulate the liquid-liquid equilibrium of the system of DMSO + naphtheniclubricating base oils, and determined the parameters of binary interaction through regression based on the data on phaseequilibrium. The modeling results showed that the predicted yield, content of the solvent, and composition of the raffinatesand extracts were in good agreement with those obtained in the experiments. This validates the reliability of the model usedto represent the DMSO + naphthenic lubricating base oil system. Both the experimental data and the method of simulationreported here can help optimize the extraction of naphthenic lubricating base oils, and provide a better understanding of thecorresponding process.

Colorimetric determination of nanomolar concentrations of silicate in natural waters after liquid-liquid extraction using methyl isobutyl ketone

A sensitive solvent extraction method for the determination of nonamolar concentrations of silicate in natural waters is developed. According to the traditional aqueous silicate method, silicomolybdenum blue formed by the reaction between silicate and ammoni- um molydate and reduced by metol-sulfite reagent is extracted by methyl isobutyl ketone. The absorbance can be enhanced substantially up to 10-folds. The detection limit of silicate is 8 nmol/dm^3 , which is one tenth smaller than the traditional method, with the precision of 4.0% at a silicate level of 50 nmol/dm^3 and 3.2% at a silicate level of 6 μmol/dm^3. Comparing the calibration curves in the distilled water and seawater, it can be seen that the salt effect also exists in the extraction method. However, the salt effect is a linear function of the salinity and can be corrected by simple calibration. The proposed method is successfully applied to the determination of silicate in natural waters. Natural concentrations of arsenate, arsenite and phosphate cause negligible interference.

Automatic Sentimental Analysis by Firefly with Levy and Multilayer Perceptron

The field of sentiment analysis(SA)has grown in tandem with the aid of social networking platforms to exchange opinions and ideas.Many people share their views and ideas around the world through social media like Facebook and Twitter.The goal of opinion mining,commonly referred to as sentiment analysis,is to categorise and forecast a target’s opinion.Depending on if they provide a positive or negative perspective on a given topic,text documents or sentences can be classified.When compared to sentiment analysis,text categorization may appear to be a simple process,but number of challenges have prompted numerous studies in this area.A feature selection-based classification algorithm in conjunction with the firefly with levy and multilayer perceptron(MLP)techniques has been proposed as a way to automate sentiment analysis(SA).In this study,online product reviews can be enhanced by integrating classification and feature election.The firefly(FF)algorithm was used to extract features from online product reviews,and a multi-layer perceptron was used to classify sentiment(MLP).The experiment employs two datasets,and the results are assessed using a variety of criteria.On account of these tests,it is possible to conclude that the FFL-MLP algorithm has the better classification performance for Canon(98%accuracy)and iPod(99%accuracy).

Mass transfer coefficient in the eductor liquid-liquid extraction column

In this research gasoil desalting was investigated from mass transfer point of view in an eductor liquid–liquid extraction column(eductor-LLE device).Mass transfer characteristics of the eductor-LLE device were evaluated and an empirical correlation was obtained by dimensional analysis of the dispersed phase Sherwood number.The Results showed that the overall mass transfer coefficient of the dispersed phase and extraction efficiency have been increased by increasing Sauter mean diameter(SMD)and decreasing the nozzle diameter from 2 to 1 mm,respectively.The effects of Reynolds number(R_(e)),projection ratio(ratio of the distance between venturi throat and nozzle tip to venturi throat diameter,Rpr),venturi throat area to nozzle area ratio(R_(th-n))and two phases flow rates ratio(R_(Q))on the mass transfer coefficient(K)were determined.According to the results,K increase with increasing Re and RQ and also with decreasing Rpr and R_(th-n).Semi-empirical models of drop formation,rising and coalescence were compared with our proposed empirical model.It was revealed that the present model provided a relatively good fitting for the mass transfer model of drop coalescence.Moreover,experimental data were in better agreement with calculated data with AARE value of 0.085.

Comparison of liquid-liquid extraction-thin layer chromatography with solid-phase extraction-high-performance thin layer chromatography in detection of urinary morphine

Liquid-liquid extraction-thin layer chromatography （LLE-TLC） has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction （SPE） is gradually replacing the tra- ditional LLE method. High performance thin layer chromatography （HPTLC） has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over that of a routine LLE-TLC method, in extraction and detection of urinary morphine. Fifty-eight urine samples, primarily identified as mor- phine-positive samples by a strip test, ＇were re-screened by LLE-TLC and SPE-HPTLC. The results of LLE-TLC and SPE-HPTLC were then compared with each other. The results showed that the SPE-HPTLC detected 74% of total samples as morphine-positive samples whereas the LLE-TLC detected 48% of the same samples. We further discussed the effect of codeine abuse on TLC analysis of urinary morphine. Regarding the importance of morphine detection in urine, the present combined SPE-HPTLC method is suggested as a replacement method for detection of urinary morphine by many reference laboratories.

Salting-Out Assisted Liquid-Liquid Extraction Combined with HPLC for Quantitative Extraction of Trace Multiclass Pesticide Residues from Environmental Waters

In this study, salting-out assisted liquid-liquid extraction combined with high performance liquid chromatography diode array detector (SALLE-HPLC-DAD) method was developed and validated for simultaneous analysis of carbaryl, atrazine, propazine, chlorothalonil, dimethametryn and terbutryn in environmental water samples. Parameters affecting the extraction efficiency such as type and volume of extraction solvent, sample volume, salt type and amount, centrifugation speed and time, and sample pH were optimized. Under the optimum extraction conditions the method was linear over the range of 10 - 100 μg/L (carbaryl), 8 - 100 μg/L (atarzine), 7 - 100 μg/L (propazine) and 9 - 100 μg/L (chlorothalonil, terbutryn and dimethametryn) with correlation coefficients (R2) between 0.99 and 0.999. Limits of detection and quantification ranged from 2.0 to 2.8 μg/L and 6.7 to 9.5 μg/L, respectively. The extraction recoveries obtained for ground, lake and river waters were in a range of 75.5% to 106.6%, with the intra-day and inter-day relative standard deviation lower than 3.4% for all the target analytes. All of the target analytes were not detected in these samples. Therefore, the proposed SALLE-HPLC-DAD method is simple, rapid, cheap and environmentally friendly for the determination of the aforementioned herbicides, insecticide and fungicide residues in environmental water samples.

Robust Radiometric Normalization of the near Equatorial Satellite Images Using Feature Extraction and Remote Sensing Analysis

Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition conditions rather than changes in surface. Scale invariant feature transform (SIFT) has the ability to automatically extract control points (CPs) and is commonly used for remote sensing images. However, its results are mostly inaccurate and sometimes contain incorrect matching caused by generating a small number of false CP pairs. These CP pairs have high false alarm matching. This paper presents a modified method to improve the performance of SIFT CPs matching by applying sum of absolute difference (SAD) in a different manner for the new optical satellite generation called near-equatorial orbit satellite and multi-sensor images. The proposed method, which has a significantly high rate of correct matches, improves CP matching. The data in this study were obtained from the RazakSAT satellite a new near equatorial satellite system. The proposed method involves six steps: 1) data reduction, 2) applying the SIFT to automatically extract CPs, 3) refining CPs matching by using SAD algorithm with empirical threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression transformation functions. Different thresholds have experimentally tested and used in conducting this study (50 and 70), by followed the proposed method, and it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and 681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched pairs, respectively.

Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false alarm.This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor images.The proposed method leads to improving CPs matching with a significantly higher rate of correct matches.The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area.The proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method.The result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.