Search results for: deep eutectic solvent

Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Durata Haciu, Berti Manisa, Ozgur Birer

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ZnO nanoparticles have been synthesized by ultrasonic irradiation from simple linear alcohols and water/ethanolic mixtures, at 50 oC. By changing the composition of the solvent, the shape could be altered. While no product was obtained from methanolic solutions, in ethanol, sheet like lamellar structures prevail.n-propanol and n-butanol resulted in needle like structures. The morphology of ZnO could be thus tailored in a simple way, by varying the solvent, under ultrasonic irradiation, in a relatively less time consuming method. Variation of the morphology and size of Zn also provides a means for modulating the band-gap. Although the chemical effects of ultrasound do not come from direct interaction with molecular species, the high energy derived from acoustic cavitation creates a unique interaction of energy and matter with great potential for synthesis.

Keywords: ultrasound, ZnO, linear alcohols, morphology

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Authors: S. Bouameur, S. Chirani

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The aim of this study was to evaluate the potential use of EthylCellulose in the preparation of microspheres as a Drug Delivery System for sustained release of phenobarbital. The microspheres were prepared by solvent evaporation technique using ethylcellulose as polymer matrix with a ratio 1:2, dichloromethane as solvent and Polyvinyl alcohol 1% as processing medium to solidify the microspheres. Size, shape, drug loading capacity and entrapement efficiency were studied.

Keywords: phenobarbital, microspheres, ethylcellulose, polyvinylacohol

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Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

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Authors: M. Abdallah

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Urban development requires deep excavations near buildings and other structures. Deep excavation has become more a necessity for better utilization of space as the population of the world has dramatically increased. In Lebanon, some urban areas are very crowded and lack spaces for new buildings and underground projects, which makes the usage of underground space indispensable. In this paper, a numerical modeling is performed using the finite element method to study the deep excavation-diaphragm wall soil-structure interaction in the case of nonlinear soil behavior. The study is focused on a comparison of the results obtained using different support systems. Furthermore, a parametric study is performed according to the remoteness of the structure.

Keywords: deep excavation, ground anchors, interaction soil-structure, struts

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Authors: Imad Eddine Charif, Sidi Mohamed Mekelleche, Didier Villemin

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The solvent effects on the keto-enol tautomeric equilibriums of acetylacetone and dimedone are theoretically investigated at the correlated Becke-3-parameter-Lee-Yang-Parr (B3LYP) and second-order Møller-Plesset (MP2) computational levels. The present study shows that the most stable keto tautomer of acetylacetone corresponds to the trans-diketo, E,Z form; while the most stable enol tautomer corresponds to the closed cis-enol,Z,Z form. The keto tautomer of dimedone prefers the trans diketo, E, E form; while the most stable enol tautomer corresponds to trans-enol form. The calculated free Gibbs enthalpies indicate that, in polar solvents, the keto-enol equilibrium of acetylacetone is shifted toward the keto tautomer; whereas the keto-enol equilibrium of dimedone is shifted towards the enol tautomer. The experimental trends of the change of equilibrium constants with respect to the change of solvent polarity are well reproduced by both B3LYP and MP2 calculations.

Keywords: acetylacetone, dimedone, solvent effects, keto-enol equilibrium, theoretical calculations

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Natália C. C. Lobato, Ângela M. Ferreira, Marcelo B. Mansur

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In solvent extraction operations, large sedimentation areas in the mixer-settler are required when the disengagement of the aqueous and the organic phases is slow and/or difficult. The use of a magnetic organic liquid (also known as ferrofluid), consisting of magnetite nanoparticles coated by oleic acid dispersed in the organic diluent, has proven successful to speed up phase disengagement. The method, however, has never been used industrially; therefore, the aim of this study is to raise its main limitations. Tests were carried out using a ferrofluid containing 30 g/l of magnetite dissolved in commercial aliphatic kerosene Exxsol D80. The efficiency of cobalt extraction ([Co] = 1 g/l) with 10% v/v Cyanex 272 (bis-2,4,4-trimethylpentyl phosphinic acid) at changing pH of the aqueous phase (2 to 7) was found unaffected in the conditions studied. However, the chemical resistance of the ferrofluid in contact with deionized water at changing acidity (from 10-7 to 2 mol/l) revealed that the nanoparticles are not resistant when contacted to aqueous solutions with a pH ≤ 2. Such result represents a serious limitation to the applicability of the method mainly to hydrometallurgical systems because solvent extraction operations are normally done in acid conditions, therefore more effective strategies to coat the particles are required.

Keywords: magnetic solvent extraction, oleic acid, magnetite nanoparticles, cyanex 272

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Authors: Kenichi Hirota, Jifeng Wang, Sadao Araki, Koji Endo, Hideki Yamamoto

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Zirconium (Zr) has been used as a fuel cladding tube for nuclear reactors, because of the excellent corrosion resistance and the low adsorptive material for neutron. Generally speaking, the natural resource of Zr is often containing Hf that has similar properties. The content of Hf in the Zr resources is about 2~4 wt%. In the industrial use, the content of Hf in Zr resources should be lower than the 100 ppm. However, the separation of Zr and Hf is not so easy, because of similar chemical and physical properties such as melting point, boiling point and things. Solvent extraction method has been applied for the separation of Zr and Hf from Zr natural resources. This method can separate Hf with high efficiency (Hf < 100ppm), however, it needs much amount of organic solvents for solvent extraction and the cost of its disposal treatment is high. Therefore, we attached attention for the fractional crystallization. This separation method depends on the solubility difference of Zr and Hf in the solvent. In this work, hexafluorozirconate (hafnate) (K2Zr(Hf)F6) was used as model compound. Solubility of K2ZrF6 in water showed lower than that of K2HfF6. By repeating of this treatment, it is possible to purify Zr, practically. In this case, 16-18 times of recrystallization stages were needed for its high purification. The improvement of the crystallization process was carried out in this work. Water, hydrofluoric acid (HF) and hydrofluoric acid (HF) +hydrochloric acid (HCl) mixture were chosen as solvent for dissolution of Zr and Hf. In the experiment, 10g of K2ZrF6 was added to each solvent of 100mL. Each solution was heated for 1 hour at 353K. After 1h of this operation, they were cooled down till 293K, and were held for 5 hours at 273K. Concentration of Zr or Hf was measured using ICP analysis. It was found that Hf was separated from Zr-Hf mixed compound with high efficiency, when HF-HCl solution was used for solvent of crystallization. From the comparison of the particle size of each crystal by SEM, it was confirmed that the particle diameter of the crystal showed smaller size with decreasing of Hf content. This paper concerned with purification of Zr from Zr-Hf mixture using crystallization method.

Keywords: crystallization, zirconium, hafnium, separation

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Authors: Korban Oosthuizen, Robert C. Luckay

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Due to the growing popularity of electric vehicles and the importance of cobalt as part of the cathode material for lithium-ion batteries, demand for this metal is on the rise. Recycling of the cathode materials by means of solvent extraction is an attractive means of recovering cobalt and easing the pressure on limited natural resources. In this study, a series of straight chain and macrocyclic diamine ligands were developed for the selective recovery of cobalt from the solution containing nickel and manganese by means of solvent extraction. This combination of metals is the major cathode material used in electric vehicle batteries. The ligands can be protonated and function as ion-pairing ligands targeting the anionic [CoCl₄]²⁻, a species which is not observed for Ni or Mn. Selectivity for Co was found to be good at very high chloride concentrations and low pH. Longer chains or larger macrocycles were found to enhance selectivity, and linear chains on the amide side groups also resulted in greater selectivity over the branched groups. The cation of the chloride salt used for adjusting chloride concentrations seems to play a major role in extraction through salting-out effects. The ligands developed in this study show good selectivity for Co over Ni and Mn but require very high chloride concentrations to function. This research does, however, open the door for further investigations into using diamines as solvent extraction ligands for the recovery of cobalt from spent lithium-ion batteries.

Keywords: hydrometallurgy, solvent extraction, cobalt, lithium-ion batteries

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Authors: Ian Marc G. Cabugsa, Kim Ryan A. Won

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Fast, reliable and simultaneous HPLC analysis of theobromine and caffeine in cacao and cocoa products was optimized in this study. The samples tested were raw, fermented, and roasted cacao beans as well as commercially available cocoa products. The HPLC analysis was carried out using step gradient solvent system with acetonitrile and water buffered with H3PO4 as the mobile phase. The HPLC system was optimized using 273 nm wavelength at 35 °C for the column temperature with a flow rate of 1.0 mL/min. Using this method, the theobromine percent recovery mean, Limit of Detection (LOD) and Limit of Quantification (LOQ) is 118.68(±3.38)%, 0.727 and 1.05 respectively. The percent recovery mean, LOD and LOQ for caffeine is 105.53(±3.25)%, 2.42 and 3.50 respectively. The inter-day and intra-day precision for theobromine is 4.31% and 4.48% respectively, while 7.02% and 7.03% was for caffeine respectively. Compared to the standard method in AOAC using methanol in isocratic solvent system, the results of the study produced lesser chromatogram noise with emphasis on theobromine and caffeine. The method is readily usable for cacao and cocoa substances analyses using HPLC with step gradient capability.

Keywords: cacao, caffeine, HPLC, step gradient solvent system, theobromine

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Authors: Jiaqi Huang, Yuheng Wang

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Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks.

Keywords: automated theorem proving, premises selection, deep learning, interpreting deep learning

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Authors: Mannal Tariq

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Construction industry is making progress at a high pace. The trend of the world is getting more biased towards the high rise buildings. Deep beams are one of the most common elements in modern construction having small span to depth ratio. Deep beams are mostly used as transfer girders. This experimental study consists of 16 reinforced concrete (RC) deep beams. These beams were divided into two groups; A and B. Groups A and B consist of eight beams each, having 381 mm (15 in) and 457 mm (18 in) depth respectively. Each group was further subdivided into four sub groups each consisting of two identical beams. Each subgroup was comprised of solid/control beam (without opening), opening above neutral axis (NA), at NA and below NA. Except for control beams, all beams with openings were strengthened with carbon fibre reinforced polymer (CFRP) vertical strips. These eight groups differ from each other based on depth and location of openings. For testing sake, all beams have been loaded with two symmetrical point loads. All beams have been designed based on strut and tie model concept. The outcome of experimental investigation elaborates the difference in the shear behaviour of deep beams based on depth and location of circular openings variation. 457 mm (18 in) deep beam with openings above NA show the highest strength and 381 mm (15 in) deep beam with openings below NA show the least strength. CFRP sheets played a vital role in increasing the shear capacity of beams.

Keywords: CFRP, deep beams, openings in deep beams, strut and tie modal, shear behaviour

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Authors: Seyed Abolhasan Naeini, Saeideh Mohammadi

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Embankment settlement on soft clays has always been problematic due to the high compaction and low shear strength of the soil. Deep soil mixing and geosynthetics are two soil improvement methods in such fields. Here, a numerical study is conducted on the embankment performance on the soft ground improved by deep soil mixing columns and geosynthetics based on the data of a real project. For this purpose, the finite element method is used in the Plaxis 2D software. The Soft Soil Creep model considers the creep phenomenon in the soft clay layer while the Mohr-Columb model simulates other soil layers. Results are verified using the data of an experimental embankment built on deep mixing columns. The effect of depth and diameter of deep mixing columns and the stiffness of geogrid on the vertical and horizontal movements of embankment on clay subsoil will be investigated in the following.

Keywords: PLAXIS 2D, embankment settlement, horizontal movement, deep soil mixing column, geogrid

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Authors: Matthew Korey, Jeffrey Youngblood, John Howarter

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Background and Significance: Tannic acid (TA) is a bio-based high molecular weight organic, aromatic molecule that has been found to increase thermal stability and flame retardancy of many polymer matrices when used as an additive. Although it is biologically sourced, TA is a pollutant in industrial wastewater streams, and there is a desire to find applications in which to downcycle this molecule after extraction from these streams. Additionally, epoxy thermosets have revolutionized many industries, but are too flammable to be used in many applications without additives which augment their flame retardancy (FR). Many flame retardants used in epoxy thermosets are synthesized from petroleum-based monomers leading to significant environmental impacts on the industrial scale. Many of these compounds also have significant impacts on human health. Various bio-based modifiers have been developed to improve the FR of the epoxy resin; however, increasing FR of the system without tradeoffs with other properties has proven challenging, especially for TA. Methodologies: In this work, TA was incorporated into the thermoset by use of solvent-exchange using methyl ethyl ketone, a co-solvent for TA, and epoxy resin. Samples were then characterized optically (UV-vis spectroscopy and optical microscopy), thermally (thermogravimetric analysis and differential scanning calorimetry), and for their flame retardancy (mass loss calorimetry). Major Findings: Compared to control samples, all samples were found to have increased thermal stability. Further, the addition of tannic acid to the polymer matrix by the use of solvent greatly increased the compatibility of the additive in epoxy thermosets. By using solvent-exchange, the highest loading level of TA found in literature was achieved in this work (40 wt%). Conclusions: The use of solvent-exchange shows promises for circumventing the limitations of TA in epoxy.

Keywords: sustainable, flame retardant, epoxy, tannic acid

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Authors: Ruqaya H. Aljabery

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Reinforced Concrete (RC) deep beams are one of the main critical structural elements in terms of safety since significant loads are carried in a short span. The shear capacity of these sections cannot be predicted accurately by the current design codes like ACI and EC2; thus, they must be investigated. In this research, non-linear behavior of RC deep beams strengthened in shear with Fiber Reinforced Polymer (FRP) strips, and the efficiency of FRP in terms of enhancing the shear capacity in RC deep beams are examined using Finite Element Analysis (FEA), which is conducted using the software ABAQUS. The effect of several parameters on the shear capacity of the RC deep beam are studied in this paper as well including the effect of the cross-sectional area of the FRP strip and the shear reinforcement area to the spacing ratio (As/S), and it was found that FRP enhances the shear capacity significantly and can be a substitution of steel stirrups resulting in a more economical design.

Keywords: Abaqus, concrete, deep beam, finite element analysis, FRP, shear strengthening, strut-and-tie

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Zubi Sadiq, Ghazala Yaqub, Almas Hamid

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We wish to report herein a very stimulating variation in Fischer indole reaction by adopting solvent and catalyst free conditions. A concise synthesis of dinitro tetrahydrocarbazole derivative 3 was introduced without the use of any accelerating agent at ambient temperature with fairly good yield. Product was fully corroborated by UV, FTIR, 1H NMR, 13C NMR, MS, and CHN analysis.

Keywords: fischer indole reaction, neutral medium, solvent free synthesis, tetrahydrocarbazole

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Authors: Mohammad Shameel, Peter K. K. Loh, James H. Ng

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Cybercrime is a new category of crime which poses a different challenge for crime investigators and incident responders. Attackers can mask their identities using a suite of tools and with the help of the deep web, which makes them difficult to track down. Scouring the deep web manually takes time and is inefficient. There is a growing need for a tool to scour the deep web to obtain useful evidence or intel automatically. In this paper, we will explain the background and motivation behind the research, present a survey of existing research on related tools, describe the design of our own crime/threat intelligence hunting tool prototype, demonstrate its capability with some test cases and lastly, conclude with proposals for future enhancements.

Keywords: cybercrime, deep web, threat intelligence, web crawler

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Authors: Hana' Al-Ghanim, Mu'tasim Abdel-Jaber, Maha Alqam

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This experimental investigation deals with shear strengthening of reinforced concrete (RC) deep beams using the externally bonded carbon fiber-reinforced polymer (CFRP) composites. The current study, therefore, evaluates the effectiveness of four various configurations for shear strengthening of deep beams with two different types of CFRP materials including sheets and laminates. For this purpose, a total of 10 specimens of deep beams were cast and tested. The shear performance of the strengthened beams is assessed with respect to the cracks’ formation, modes of failure, ultimate strength and the overall stiffness. The obtained results demonstrate the effectiveness of using the CFRP technique on enhancing the shear capacity of deep beams; however, the efficiency varies depending on the material used and the strengthening scheme adopted. Among the four investigated schemes, the highest increase in the ultimate strength is recorded by using the continuous wrap of two layers of CFRP sheets, exceeding a value of 86%, whereas an enhancement of about 36% is achieved by the inclined CFRP laminates.

Keywords: deep beams, laminates, shear strengthening, sheets

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Authors: Ugur Buyuk, Sevda Engin

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Among the various alloy systems being considered as lead-free solder candidates, Sn-Ag-Cu alloys have been recognized as the most promising because of their excellent reliability and compatibility with current components. Thus, Sn-Ag-Cu alloys have recently attracted considerable attention and have been proposed by the Japanese, the EU and the US consortiums to replace conventional Sn-Pb eutectic solder. However, many problems or unknown characteristics of the Sn-Ag-Cu alloy system such as the best composition, the large undercooling in solidification, and the formation of large intermetallics still exist. It is expected that the addition of some solidification nuclei for Sn-Ag-Cu alloys will refine the solidification microstructure and will suppress undercooling.In the present work, the effects of the fourth elements, i.e., Zn, Ni, Bi, In and Co, on microstructural and mechanical properties of Sn-3.5Ag-0.9Cu lead-free solder were investigated. Sn-3.5Ag-0.9Cu-0.5X (X= Zn, Ni, Bi, In, Co (wt.)) alloys were prepared in a graphite crucible under vacuum atmosphere. The samples were directionally solidified upward at a constant temperature gradient and growth rates by using a Bridgman type directional solidification furnace. The microstructure, microhardness and ultimate tensile strength of alloys were measured. The effects of fourth elements on the microstructure and mechanical properties of Sn–Ag-Cu eutectic alloys were investigated. The results obtained in the present work were compared with the previous experimental results.

Keywords: lead-free solders, microhardness, microstructure, tensile strength

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Authors: Shashikant R. Kuchekar, Somnath D. Bhumkar, Haribhau R. Aher, Bhaskar H. Zaware, Ponnadurai Ramasami

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A precise, sensitive, rapid and selective method for the solvent extraction, spectrophotometric determination of palladium(II) using para-methylphenyl thiourea (PMPT) as an extractant is developed. Palladium(II) forms yellow colored complex with PMPT which shows an absorption maximum at 300 nm. The colored complex obeys Beer’s law up to 7.0 µg ml^-1 of palladium. The molar absorptivity and Sandell’s sensitivity were found to be 8.486 x 10³ l mol^-1cm^-1 and 0.0125 μg cm^-2 respectively. The optimum conditions for the extraction and determination of palladium have been established by monitoring the various experimental parameters. The precision of the method has been evaluated and the relative standard deviation has been found to be less than 0.53%. The proposed method is free from interference from large number of foreign ions. The method has been successfully applied for the determination of palladium from alloy, synthetic mixtures corresponding to alloy samples.

Keywords: solvent extraction, PMPT, Palladium (II), spectrophotometry

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Authors: Nalinee B. Suryawanshi, Vinay M. Bhandari, Laxmi Gayatri Sorokhaibam, Vivek V. Ranade

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Deep desulphurization of transportation fuels is a major environmental concern all over the world and recently prescribed norms for the sulphur content require below 10 ppm sulphur concentrations in fuels such as diesel and gasoline. The existing technologies largely based on catalytic processes such as hydrodesulphurization, oxidation require newer catalysts and demand high cost of deep desulphurization whereas adsorption based processes have limitations due to lower capacity of sulphur removal. The present work is an attempt to provide alternatives for the existing methodologies using a newer non-catalytic process based on hydrodynamic cavitation. The developed process requires appropriate combining of organic and aqueous phases under ambient conditions and passing through a cavitating device such as orifice, venturi or vortex diode. The implosion of vapour cavities formed in the cavitating device generates (in-situ) oxidizing species which react with the sulphur moiety resulting in the removal of sulphur from the organic phase. In this work, orifice was used as a cavitating device and deep desulphurization was demonstrated for removal of thiophene as a model sulphur compound from synthetic fuel of n-octane, toluene and n-octanol. The effect of concentration of sulphur (up to 300 ppm), nature of organic phase and effect of pressure drop (0.5 to 10 bar) was discussed. A very high removal of sulphur content of more than 90% was demonstrated. The process is easy to operate, essentially works at ambient conditions and the ratio of aqueous to organic phase can be easily adjusted to maximise sulphur removal. Experimental studies were also carried out using commercial diesel as a solvent and the results substantiate similar high sulphur removal. A comparison of the two cavitating devices- one with a linear flow and one using vortex flow for effecting pressure drop and cavitation indicates similar trends in terms of sulphur removal behaviour. The developed process is expected to provide an attractive environmental engineering alternative for deep desulphurization of transportation fuels.

Keywords: cavitation, petroleum, separation, sulphur removal

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Authors: Xuewei Li, Jingfeng Yuan, Jianliang Zhou

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The 21st century has been called the century of human exploitation of underground space. Due to the characteristics of large quantity, tight schedule, low safety reserve and high uncertainty of deep foundation pit engineering, accidents frequently occur in deep foundation pit engineering, causing huge economic losses and casualties. With the successful application of information technology in the construction industry, building information modeling has become a research hotspot in the field of architectural engineering. Therefore, the application of building information modeling (BIM) and other information communication technologies (ICTs) in construction safety management is of great significance to improve the level of safety management. This research summed up the mechanism of the deep foundation pit engineering accident through the fault tree analysis to find the control factors of deep foundation pit engineering safety management, the deficiency existing in the traditional deep foundation pit construction site safety management. According to the accident cause mechanism and the specific process of deep foundation pit construction, the hazard information of deep foundation pit engineering construction site was identified, and the hazard list was obtained, including early warning information. After that, the system framework was constructed by analyzing the early warning information demand and early warning function demand of the safety management system of deep foundation pit. Finally, the safety management system of deep foundation pit construction site based on BIM through combing the database and Web-BIM technology was developed, so as to realize the three functions of real-time positioning of construction site personnel, automatic warning of entering a dangerous area, real-time monitoring of deep foundation pit structure deformation and automatic warning. This study can initially improve the current situation of safety management in the construction site of deep foundation pit. Additionally, the active control before the occurrence of deep foundation pit accidents and the whole process dynamic control in the construction process can be realized so as to prevent and control the occurrence of safety accidents in the construction of deep foundation pit engineering.

Keywords: Web-BIM, safety management, deep foundation pit, construction

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: Saziye Ilgaz, Atilla Polat

Abstract:

There is a need for development of new processing techniques to ensure safety and quality of final product while minimizing the adverse impact of extraction solvents on environment and residue levels of these solvents in final product, decaffeinated black tea. In this study pilot scale supercritical carbon dioxide (SCCO₂) extraction was used to produce decaffeinated black tea in place of solvent extraction. Pressure (250, 375, 500 bar), extraction time (60, 180, 300 min), temperature (55, 62.5, 70 °C), CO₂ flow rate (1, 2 ,3 LPM) and co-solvent quantity (0, 2.5, 5 %mol) were selected as extraction parameters. The five factors BoxBehnken experimental design with three center points was performed to generate 46 different processing conditions for caffeine removal from black tea samples. As a result of these 46 experiments caffeine content of black tea samples were reduced from 2.16 % to 0 – 1.81 %. The experiments showed that extraction time, pressure, CO₂ flow rate and co-solvent quantity had great impact on decaffeination yield. Response surface methodology (RSM) was used to optimize the parameters of the supercritical carbon dioxide extraction. Optimum extraction parameters obtained of decaffeinated black tea were as follows: extraction temperature of 62,5 °C, extraction pressure of 375 bar, CO₂ flow rate of 3 LPM, extraction time of 176.5 min and co-solvent quantity of 5 %mol.

Keywords: supercritical carbon dioxide, decaffeination, black tea, extraction

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

Abstract:

Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

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Authors: Adetokunbo Ademola Falade, Oluwatoyin Olakunle Akinsete, Hussein Omeiza Aliu

Abstract:

Solvent-aided dilution processes are often employed to recover bitumen by reducing its viscosity. In this study, methanol, toluene, and xylene were investigated as potential hydrocarbon solvents for solvent-aided hydrocarbon recovery of Agbabu bitumen. Solubility, Viscosity, and Saturate, Aromatic, Resin and Asphaltene (SARA) Analysis tests were carried out to determine the solubility of the bitumen in the solvents, the viscosity, and the SARA fraction of the natural bitumen and bitumen-solvent mixtures. Agbabu bitumen was found to have a high content of saturates and aromatics. Viscosity decreases as pressure increases, while solubility reduces as temperature increases. The experimental diffusivity of the sample decreases with temperature and increases with pressure, indicating that the presence of additional solvent molecules in the oil phase facilitates diffusion. Agbabu bitumen was found to be most soluble in toluene, and its viscosity was reduced most in it. Xylene exhibited a similar effect as toluene on the sample, though lesser but better than methanol. Methanol reduced the saturated content and significantly raised the asphaltene content, keeping the mixture viscosity high, a condition that, in turn, favors its colloidal stability. The colloidal instability index (CII) values, which account for the asphaltene stability of the mixture, show that the bitumen-methanol system with a CII of 0.874 will have mild asphaltene deposit issues while others are unstable. This approach of combining multiple tests with the CII can accurately predict the behavior of Agbabu bitumen in solvents and enhance the decision on the choice of bitumen recovery technology.

Keywords: asphaltene, bitumen, diffusivity, hydrocarbon solvent, SARA

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