Experimental data for pure ionic liquids at 298.15 K and other relevant informationa\n\t\t\t\t\t
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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\r\n\r\n\tThe book will aim to include the latest information used in current practice and current research areas on which the future practice will be based on. Not only on modern investigation and diagnosing tools biopsy techniques and radiological imaging but also modern concepts for managing these tumours. The three main areas in managing involve radiotherapy chemotherapy and surgical oncology and the latest advances in these fields are intended to be discussed. This book will aim to benefit not only trainees of surgery, oncology medicine, orthopaedics but also medical students, general practitioners, and anybody interested in the field of bone tumour management.
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Pioneering work on anatomy and blood supply to joints mainly hip joints and causative factors leading to avascular necrosis was done at the University of Warwick, and the University of California Los Angeles.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"67634",title:"Dr.",name:"Hiran",middleName:"Wimal",surname:"Amarasekera",slug:"hiran-amarasekera",fullName:"Hiran Amarasekera",profilePictureURL:"https://mts.intechopen.com/storage/users/67634/images/system/67634.png",biography:"Hiran Amarasekera is a Consultant Orthopaedic Surgeon Currently practicing in Sri Lanka. After obtaining the MBBS from Kasturba medical college, Manipal, Inda, he completed the MS in Surgical sciences from the University of Colombo. He obtained the fellowship of the Royal College of Surgeons of Edinburgh (FRCS Ed) and board certification in 2003. \n\nHis special interests are in the areas of young adult hip and knee problems, sports injuries, lower limb arthroplasty, and keyhole joint surgery, and revision arthroplasty. His present research is focused on non-surgical and minimally invasive alternative treatment for osteoarthritis. He worked and trained in many countries for over twenty including India, Sri Lanka, Australia, United States, and the UK.\n\nAs a keen researcher, he has completed an MPhil from the University of Warwick and completed a research fellowship at the University of California Los Angeles, (UCLA). \n\nPresently, he works as a medical educator, as an honorary senior lecturer at the University of Kelaniya and Kothalawela Defense University in Sri Lanka. He is an examiner of medical students both in Sri Lanka and the UK and a course provider for Trauma courses run by the college of surgeons and was elected a fellow of Sri Lanka College of surgeons in 2013.\n\nDr. Amarasekera is the editor of the Journal of Sri Lanka Orthopaedic association and council member. He is a reviewer for the Journal of Bone and Joint Surgery (Br) e and Bone and Joint Journal (BJJ) and a member of the editorial board of the Sri Lanka Journal of Surgery (SLJS). \n\nHe has over 50 international publications, presentations and several book chapters to his credit and has reviewed over 100 papers for journals of BJJ and SLJS.\n\nAfter joining IntechOpen in 2012 he authored three book chapters and edited several open access books with them.",institutionString:"University of Warwick Science Park",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Warwick Science Park",institutionURL:null,country:{name:"United Kingdom"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429341",firstName:"Paula",lastName:"Gavran",middleName:null,title:"Ms.",imageUrl:"//cdnintech.com/web/frontend/www/assets/author.svg",email:"paula@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2270",title:"Fourier Transform",subtitle:"Materials Analysis",isOpenForSubmission:!1,hash:"5e094b066da527193e878e160b4772af",slug:"fourier-transform-materials-analysis",bookSignature:"Salih Mohammed Salih",coverURL:"https://cdn.intechopen.com/books/images_new/2270.jpg",editedByType:"Edited by",editors:[{id:"111691",title:"Dr.Ing.",name:"Salih",surname:"Salih",slug:"salih-salih",fullName:"Salih Salih"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"117",title:"Artificial Neural Networks",subtitle:"Methodological Advances and Biomedical Applications",isOpenForSubmission:!1,hash:null,slug:"artificial-neural-networks-methodological-advances-and-biomedical-applications",bookSignature:"Kenji Suzuki",coverURL:"https://cdn.intechopen.com/books/images_new/117.jpg",editedByType:"Edited by",editors:[{id:"3095",title:"Prof.",name:"Kenji",surname:"Suzuki",slug:"kenji-suzuki",fullName:"Kenji Suzuki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3828",title:"Application of Nanotechnology in Drug Delivery",subtitle:null,isOpenForSubmission:!1,hash:"51a27e7adbfafcfedb6e9683f209cba4",slug:"application-of-nanotechnology-in-drug-delivery",bookSignature:"Ali Demir Sezer",coverURL:"https://cdn.intechopen.com/books/images_new/3828.jpg",editedByType:"Edited by",editors:[{id:"62389",title:"PhD.",name:"Ali Demir",surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"38663",title:"New Brønsted Ionic Liquids: Synthesis, Thermodinamics and Catalytic Activity in Aldol Condensation Reactions",doi:"10.5772/51163",slug:"new-br-nsted-ionic-liquids-synthesis-thermodinamics-and-catalytic-activity-in-aldol-condensation-rea",body:'\n\t\tIt is a continuous challenge to find new catalysts able to perform with good activities and selectivity condensation reactions for the synthesis of pharmaceutical and fine chemicals. In the last years room temperature ionic liquids (ILs) have received a lot of interest as environmental friendly or “green” alternatives to conventional molecular solvents. They differ from molecular solvents by their unique ionic character and their “structure and organization” which can lead to specific effects [1].
\n\t\t\tRoom-temperature ILs have been used as clean solvents and catalysts for green chemistry, stabilizing agents for the catalysts or intermediates, electrolytes for batteries, in photochemistry and electrosynthesis etc [2-6]. Their success as environmental benign solvents or catalysts is described in numerous reactions [7-11], such as Diels-Alder reactions [12, 13], the Friedel-Crafts reaction [14-17], esterification [18-20], cracking rections [21], and so on.\n\t\t\tThe link between ionic ILs and green chemistry is related to the solvent properties of ILs. Some of the properties that make ILs attractive media for catalysis are: they have no significant vapour pressure and thus create no volatile organic pollution during manipulation; ILs have good chemical and thermal stability, most ILs having liquid ranges for more than 3000C; they are immiscible with some organic solvents and therefore can be used in two-phase systems; ILs polarity can be adjusted by a suitable choice of cation/anion; they are able to dissolve a wide range of organic, inorganic and organometallic compounds; ILs are often composed of weakly coordinating anions and therefore have the potential to be highly polar.
\n\t\t\tThe number of ILs has increased exponentially in the recent years. Many of them are based on the imidazolium cation and in a lesser proportion, alkyl pyridiniums and trialkylamines (Scheme 1). By changing the anion or the alkyl chain of the cation, a wide variety of ILs may be designed for specific applications. They can be of hydrophobic or hydrophilic nature depending on the chemical structures involved.
\n\t\t\tMain cations and anions described in literature [1].
ILs can be divided into two broad categories: aprotic ionic liquids (AILs) and protic ionic liquids (PILs).
\n\t\t\t\n\t\n\t\t\tAILs largely dominate the open literature due to their relative inertness to organometallic compounds and their potential of applications, particularly in catalysis. They are synthesized by transferring an alkyl group to the basic nitrogen site through SN2 reactions [1].
\n\t\t\tPILs are formed through proton transfer from a Brønsted acid to a Brønsted base. Recently there has been an increasing interest in PILs due to their greater potential as environmental friendly solvents and promising applications. Moreover, they present the advantage of being cost-effective and easily prepared as their formation does not involve the formation of residual by-products. A specific feature of the PILs is that they are capable of developing a certain hydrogen bonding potency, including proton acceptance and proton donation and they are highly tolerant to hydroxylic media [22-23].
\n\t\t\tThe application of new policies on terms of environment, health and safety deals towards minimizing or substituting organic volatile solvents by green alternatives, placing a renewed emphasis on research and development of lesser harmful compounds as ILs. On the other hand, recently the interest in the use of PILs to tailor the water properties for cleaning applications in processes of minimization of CO2/SO2 emissions has increased [24-26].
\n\t\t\tIn the last years numerous studies report the use of ILs as selective catalysts for different reactions, like aldol condensation reactions where several ILs have been successfully applied as homogeneous and heterogeneous catalysts [27-30]. Abelló et al. [28] described the use of choline hydroxide as basic catalyst for aldol condensation reactions between several ketones and aldehydes. Better conversions and selectivities were obtained when compared to other well-known catalysts, such as rehydrated hydrotalcites, MgO and NaOH. In addition, higher performance was obtained when choline was immobilized on MgO.
\n\t\t\tZhu et al. [27] described the use of 1,1,3,3-tetramethylguanidine lactate ([TMG] [Lac]) as recyclable catalyst for direct aldol condensation reactions at room temperature without any solvent. It was demonstrated that for each reaction only the aldol adduct was produced when the molar ratio of the IL and substrate was smaller than 1. Moreover, after the reaction the IL was easily recovered and recycled without considerably decrease of activity.
\n\t\t\tKryshtal et al. [29] described the application of tetraalkylammonium and 1,3-dialkylimidazolium perfluoro-borates and perfluoro-phosphates as recoverable phase-transfer catalysts in multiphase reactions of CH-acids, in particular in solid base-promoted cross-aldol condensations. The catalysts retained their catalytic activity over several reaction cycles.
\n\t\t\tIn the study of Lombardo et al. [30] two onium ion-tagged prolines, imidazolium bis (trifluoromethylsulfonyl)imide-substituted proline and butyldimethylammonium bis (trifluoromethylsulfonyl) imide-substituted proline, were synthesized and their catalytic activity in the direct asymmetric aldol condensation was studied. The catalytic protocol developed by this group makes use of a 6-fold lower amount of catalyst with respect to the preceding reports [31, 32] and affords greater chemical yields and higher enantioselectivity.
\n\t\t\tThe main objective of this chapter is to develop and study the applications of a new family of ILs based on substituted amine cations of the form RNH3+ combined with organic anions of the form R’COO- (being of different nature R and R’). The variations in the anion alkyl chain, in conjunction with the cations, lead to a large matrix of materials.
\n\t\t\tThis kind of compounds show interesting properties for industrial use of ILs: low cost of preparation, simple synthesis and purification methods. Moreover, the very low toxicity and the degradability of this kind of ILs have been verified. Thus, sustainable processes can be originated from their use.
\n\t\t\tRecently, many studies dealing with the application of ILs in organic synthesis and catalysis have been published, pointing out the vast interest in this type of compounds [33-36]. With these facts in mind, we studied their catalytic potential for two condensation reactions of carbonyl compounds. The products obtained from these reactions are applied in pharmacological, flavor and fragrance industry.
\n\t\tThe ILs synthesized in this work are: 2-hydroxy ethylammonium formate (2-HEAF), 2-hydroxy ethylammonium acetate (2-HEAA), 2-hydroxy ethylammonium propionate (2-HEAP), 2-hydroxi ethylammonium butanoate (2-HEAB), 2-hydroxi ethylammonium isobutanoate (2-HEAiB) and 2-hydroxi ethylammonium pentanoate (2-HEAPE).
\n\t\t\t\tThe amine (Merck Synthesis, better than 99%) was placed in a three necked flask all-made-in-glass equipped with a reflux condenser, a PT-100 temperature sensor for controlling temperature and a dropping funnel. The flask was mounted in a thermal bath. A slight heating is necessary for increasing miscibility between reactants and then allow reaction. The organic acid (Merck Synthesis, better than 99%) was added drop wise to the flask under stirring with a magnetic bar. Stirring was continued for 24 h at laboratory temperature, in order to obtain a final viscous liquid. Lower viscosity was observed in the final product by decreasing molecular weight of reactants. No solid crystals or precipitation was noticed when the liquid sample was purified or stored at freeze temperature for a few months after synthesis. The reaction is a simple acid–base neutralization creating the formiate, acetate, propionate, butanoate, isobutanoate or pentanoate salt of ethanolamine that in a general form should be expressed as follows:
\n\t\t\t\tFor example, when formic acid is used this equation shows the chemical reaction for the reactants ethanolamine + formic acid, with 2-HEAF as neutralization product.
\n\t\t\t\tBecause these chemical reactions are highly exothermic, an adequate control of temperature is essential throughout the chemical reaction; otherwise heat evolution may produce the dehydration of the salt to the corresponding amide, as in the case for nylon salts (salts of diamines with dicarboxy acids).
\n\t\t\t\tAs observed in our laboratory during IL synthesis, dehydration begins around 423.15 K for the lightest ILs. The color varied in each case from transparent to dark yellow when the reaction process and purification (strong agitation and slight heating for the vaporization of residual non-reacted acid for at least for 24 h) were completed.
\n\t\t\t\tThere was no detectable decomposition for the ILs studied here when left for over 12 months at laboratory temperature. Less than 1% amide was detected after this period of time. On the basis of these results it appears obvious that the probability of amide formation is low for this kind of structures.
\n\t\t\t\tIn order to obtain the supported ILs, 1 g of IL was dissolved in 7 ml of ethanol and after stirring at room temperature for 30 min, 1 g of alanine (Fluka, better than 99%) was added. The mixture was stirred for 2 h and then heated at 348 K under vacuum to remove ethanol. The supported ILs thus obtained were labelled hereafter as a-ILs.
\n\t\t\tFT-IR spectrum was taken by a Jasco FT/IR 680 plus model IR spectrometer, using a NaCl disk.
\n\t\t\tDuring the course of the experiments, the purity of ILs was monitored by different physical properties measurements. The pure ILs were stored in sun light protected form, constant humidity and low temperature. Usual manipulation and purification in our experimental work was applied [22].
\n\t\t\t\tThe densities and ultrasonic velocities of pure components were measured with an Anton Paar DSA-5000 vibrational tube densimeter and sound analyzer, with a resolution of 10−5 g cm−3 and 1 m s−1. Apparatus calibration was performed periodically in accordance with provider’s instructions using a double reference (millipore quality water and ambient air at each temperature). Accuracy in the temperature of measurement was better than ±10−2 K by means of a temperature control device that apply the Peltier principle to maintain isothermal conditions during the measurements.
\n\t\t\t\tThe ion conductivity was measured by a Jenway Model 4150 Conductivity/TDS Meter with resolution of 0.01µS to 1 mS and accuracy of ±0.5% at the range temperature. The accuracy of temperature into the measurement cell was ±0.5 ◦C.
\n\t\t\tThe studied reactions were the condensation between citral and acetone and between benzaldehyde and acetone. The reactions were performed in liquid phase using a 100 mL batch reactor equipped with a condenser system. To a stirred solution of substrate and ketone (molar ratio ketone/substrate = 4.4) was added 1 g of IL, and the flask was maintained at 333 K using an oil bath. Samples were taken at regular time periods and analyzed by gas chromatography using a flame ionization detector and an AG Ultra 2 column (15 m x 0.32 mm x 0.25
In order to separate the ILs from the reaction mixture, at the end of the reaction 6 mL of H2O were added. The mixture was stirred for 2 h and then left 15 h to repose. Two phases were separated: the organic phase which contains the reaction products and the aqueous phase which contains the IL. In order to separate the IL, the aqueous phase was heated up to 393 K under vacuum.
\n\t\t\tAs Figure 1 shows, the broad band in the 3500-2400 cm-1 range exhibits characteristic ammonium structure for all the neutralization products. The OH stretching vibration is embedded in this band. The broad band centered at 1600 cm-1 is a combined band of the carbonyl stretching and N-H plane bending vibrations. \n\t\t\tFT-IR results clearly demonstrate the IL characteristics of compounds synthesized in this work.
\n\t\t\tDue to space considerations, we will present the thermodynamic properties only for two of the studied ILs: 2-HEAF and 2-HEAPE.
\n\t\t\tThe molar mass and experimental results at standard condition for 2-HEAF and 2-HEAPE are shown in Table 1.
\n\t\t\tFT-IR spectrum for 2-HEAPE.
\n\t\t\t\tIL\n\t\t\t | \n\t\t\t\t\t\tMolecular Weight (g∙mol-1) | \n\t\t\t\t\t\tExp. Density (g∙cm-3) \n\t\t\t\t\t\t | \n\t\t\t\t\t\tExp. Ultrasonic Velocity (ms-1) | \n\t\t\t\t\t\tExp. Conductivity (μS∙cm-1) | \n\t\t\t\t\t
2-HEAF | \n\t\t\t\t\t\t107.11 | \n\t\t\t\t\t\t1.176489 | \n\t\t\t\t\t\t1709.00 | \n\t\t\t\t\t\t4197.6 | \n\t\t\t\t\t
2-HEAPE | \n\t\t\t\t\t\t163.21 | \n\t\t\t\t\t\t1.045479 | \n\t\t\t\t\t\t1591.59 | \n\t\t\t\t\t\t239.6 | \n\t\t\t\t\t
Experimental data for pure ionic liquids at 298.15 K and other relevant informationa\n\t\t\t\t\t
aOther experimental data for comparison are not available from the literature.
The densities, ultrasonic velocities and isobaric expansibility of 2-HEAF and 2-HEAPE are given in Table 2, and the ionic conductivities are given in Table 3. From the results obtained it can be observed that an increase in temperature diminishes the interaction among ions, lower values of density and ultrasonic velocity being gathered for rising temperatures in each case.
\n\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t|||||||||||||||
T (K) | \n\t\t\t\t\t\tρ (gcm-3) | \n\t\t\t\t\t\tu (ms-1) | \n\t\t\t\t\t\tκS\n\t\t\t\t\t\t\t (TPa-1) | \n\t\t\t\t\t\t103 · α (K-1) | \n\t\t\t\t\t\tT (K) | \n\t\t\t\t\t\tρ (gcm-3) | \n\t\t\t\t\t\tu (ms-1) | \n\t\t\t\t\t\tκS\n\t\t\t\t\t\t\t (TPa-1) | \n\t\t\t\t\t\t103 · α (K-1) | \n\t\t\t\t\t||||||
338.15 | \n\t\t\t\t\t\t1.148091 | \n\t\t\t\t\t\t1613.59 | \n\t\t\t\t\t\t334.53 | \n\t\t\t\t\t\t0.6188 | \n\t\t\t\t\t\t327.16 | \n\t\t\t\t\t\t1.155890 | \n\t\t\t\t\t\t1639.38 | \n\t\t\t\t\t\t321.90 | \n\t\t\t\t\t\t0.6148 | \n\t\t\t\t\t||||||
337.90 | \n\t\t\t\t\t\t1.148254 | \n\t\t\t\t\t\t1614.14 | \n\t\t\t\t\t\t334.26 | \n\t\t\t\t\t\t0.6187 | \n\t\t\t\t\t\t326.91 | \n\t\t\t\t\t\t1.156069 | \n\t\t\t\t\t\t1639.97 | \n\t\t\t\t\t\t321.62 | \n\t\t\t\t\t\t0.6147 | \n\t\t\t\t\t||||||
337.66 | \n\t\t\t\t\t\t1.148433 | \n\t\t\t\t\t\t1614.71 | \n\t\t\t\t\t\t333.97 | \n\t\t\t\t\t\t0.6186 | \n\t\t\t\t\t\t326.66 | \n\t\t\t\t\t\t1.156247 | \n\t\t\t\t\t\t1640.57 | \n\t\t\t\t\t\t321.34 | \n\t\t\t\t\t\t0.6146 | \n\t\t\t\t\t||||||
337.40 | \n\t\t\t\t\t\t1.148608 | \n\t\t\t\t\t\t1615.30 | \n\t\t\t\t\t\t333.67 | \n\t\t\t\t\t\t0.6185 | \n\t\t\t\t\t\t326.41 | \n\t\t\t\t\t\t1.156426 | \n\t\t\t\t\t\t1641.16 | \n\t\t\t\t\t\t321.06 | \n\t\t\t\t\t\t0.6145 | \n\t\t\t\t\t||||||
337.15 | \n\t\t\t\t\t\t1.148785 | \n\t\t\t\t\t\t1615.87 | \n\t\t\t\t\t\t333.39 | \n\t\t\t\t\t\t0.6184 | \n\t\t\t\t\t\t326.16 | \n\t\t\t\t\t\t1.156603 | \n\t\t\t\t\t\t1641.75 | \n\t\t\t\t\t\t320.78 | \n\t\t\t\t\t\t0.6144 | \n\t\t\t\t\t||||||
336.91 | \n\t\t\t\t\t\t1.148963 | \n\t\t\t\t\t\t1616.46 | \n\t\t\t\t\t\t333.09 | \n\t\t\t\t\t\t0.6183 | \n\t\t\t\t\t\t325.91 | \n\t\t\t\t\t\t1.156780 | \n\t\t\t\t\t\t1642.34 | \n\t\t\t\t\t\t320.50 | \n\t\t\t\t\t\t0.6143 | \n\t\t\t\t\t||||||
336.66 | \n\t\t\t\t\t\t1.149139 | \n\t\t\t\t\t\t1617.04 | \n\t\t\t\t\t\t332.80 | \n\t\t\t\t\t\t0.6182 | \n\t\t\t\t\t\t325.65 | \n\t\t\t\t\t\t1.156957 | \n\t\t\t\t\t\t1642.94 | \n\t\t\t\t\t\t320.21 | \n\t\t\t\t\t\t0.6142 | \n\t\t\t\t\t||||||
336.41 | \n\t\t\t\t\t\t1.149316 | \n\t\t\t\t\t\t1617.63 | \n\t\t\t\t\t\t332.51 | \n\t\t\t\t\t\t0.6182 | \n\t\t\t\t\t\t325.40 | \n\t\t\t\t\t\t1.157136 | \n\t\t\t\t\t\t1643.53 | \n\t\t\t\t\t\t319.93 | \n\t\t\t\t\t\t0.6141 | \n\t\t\t\t\t||||||
336.16 | \n\t\t\t\t\t\t1.149494 | \n\t\t\t\t\t\t1618.22 | \n\t\t\t\t\t\t332.21 | \n\t\t\t\t\t\t0.6181 | \n\t\t\t\t\t\t325.16 | \n\t\t\t\t\t\t1.157314 | \n\t\t\t\t\t\t1644.12 | \n\t\t\t\t\t\t319.66 | \n\t\t\t\t\t\t0.6140 | \n\t\t\t\t\t||||||
335.90 | \n\t\t\t\t\t\t1.149669 | \n\t\t\t\t\t\t1618.81 | \n\t\t\t\t\t\t331.92 | \n\t\t\t\t\t\t0.6180 | \n\t\t\t\t\t\t324.90 | \n\t\t\t\t\t\t1.157490 | \n\t\t\t\t\t\t1644.72 | \n\t\t\t\t\t\t319.37 | \n\t\t\t\t\t\t0.6139 | \n\t\t\t\t\t||||||
335.65 | \n\t\t\t\t\t\t1.149848 | \n\t\t\t\t\t\t1619.38 | \n\t\t\t\t\t\t331.64 | \n\t\t\t\t\t\t0.6179 | \n\t\t\t\t\t\t324.65 | \n\t\t\t\t\t\t1.157669 | \n\t\t\t\t\t\t1645.32 | \n\t\t\t\t\t\t319.09 | \n\t\t\t\t\t\t0.6138 | \n\t\t\t\t\t||||||
335.40 | \n\t\t\t\t\t\t1.150027 | \n\t\t\t\t\t\t1619.96 | \n\t\t\t\t\t\t331.35 | \n\t\t\t\t\t\t0.6178 | \n\t\t\t\t\t\t324.40 | \n\t\t\t\t\t\t1.157846 | \n\t\t\t\t\t\t1645.91 | \n\t\t\t\t\t\t318.81 | \n\t\t\t\t\t\t0.6137 | \n\t\t\t\t\t||||||
335.16 | \n\t\t\t\t\t\t1.150205 | \n\t\t\t\t\t\t1620.55 | \n\t\t\t\t\t\t331.05 | \n\t\t\t\t\t\t0.6177 | \n\t\t\t\t\t\t324.15 | \n\t\t\t\t\t\t1.158023 | \n\t\t\t\t\t\t1646.50 | \n\t\t\t\t\t\t318.54 | \n\t\t\t\t\t\t0.6136 | \n\t\t\t\t\t||||||
334.90 | \n\t\t\t\t\t\t1.150384 | \n\t\t\t\t\t\t1621.13 | \n\t\t\t\t\t\t330.77 | \n\t\t\t\t\t\t0.6176 | \n\t\t\t\t\t\t323.90 | \n\t\t\t\t\t\t1.158201 | \n\t\t\t\t\t\t1647.09 | \n\t\t\t\t\t\t318.26 | \n\t\t\t\t\t\t0.6135 | \n\t\t\t\t\t||||||
334.66 | \n\t\t\t\t\t\t1.150560 | \n\t\t\t\t\t\t1621.71 | \n\t\t\t\t\t\t330.48 | \n\t\t\t\t\t\t0.6175 | \n\t\t\t\t\t\t323.65 | \n\t\t\t\t\t\t1.158378 | \n\t\t\t\t\t\t1647.68 | \n\t\t\t\t\t\t317.98 | \n\t\t\t\t\t\t0.6134 | \n\t\t\t\t\t||||||
334.40 | \n\t\t\t\t\t\t1.150740 | \n\t\t\t\t\t\t1622.30 | \n\t\t\t\t\t\t330.19 | \n\t\t\t\t\t\t0.6174 | \n\t\t\t\t\t\t323.40 | \n\t\t\t\t\t\t1.158556 | \n\t\t\t\t\t\t1648.28 | \n\t\t\t\t\t\t317.70 | \n\t\t\t\t\t\t0.6133 | \n\t\t\t\t\t||||||
334.16 | \n\t\t\t\t\t\t1.150916 | \n\t\t\t\t\t\t1622.89 | \n\t\t\t\t\t\t329.90 | \n\t\t\t\t\t\t0.6173 | \n\t\t\t\t\t\t323.15 | \n\t\t\t\t\t\t1.158734 | \n\t\t\t\t\t\t1648.90 | \n\t\t\t\t\t\t317.42 | \n\t\t\t\t\t\t0.6132 | \n\t\t\t\t\t||||||
333.90 | \n\t\t\t\t\t\t1.151094 | \n\t\t\t\t\t\t1623.48 | \n\t\t\t\t\t\t329.61 | \n\t\t\t\t\t\t0.6173 | \n\t\t\t\t\t\t322.90 | \n\t\t\t\t\t\t1.158910 | \n\t\t\t\t\t\t1649.47 | \n\t\t\t\t\t\t317.15 | \n\t\t\t\t\t\t0.6131 | \n\t\t\t\t\t||||||
333.65 | \n\t\t\t\t\t\t1.151271 | \n\t\t\t\t\t\t1624.06 | \n\t\t\t\t\t\t329.32 | \n\t\t\t\t\t\t0.6172 | \n\t\t\t\t\t\t322.66 | \n\t\t\t\t\t\t1.159088 | \n\t\t\t\t\t\t1650.06 | \n\t\t\t\t\t\t316.87 | \n\t\t\t\t\t\t0.6130 | \n\t\t\t\t\t||||||
333.41 | \n\t\t\t\t\t\t1.151449 | \n\t\t\t\t\t\t1624.64 | \n\t\t\t\t\t\t329.03 | \n\t\t\t\t\t\t0.6171 | \n\t\t\t\t\t\t322.41 | \n\t\t\t\t\t\t1.159265 | \n\t\t\t\t\t\t1650.66 | \n\t\t\t\t\t\t316.59 | \n\t\t\t\t\t\t0.6129 | \n\t\t\t\t\t||||||
333.16 | \n\t\t\t\t\t\t1.151625 | \n\t\t\t\t\t\t1625.23 | \n\t\t\t\t\t\t328.75 | \n\t\t\t\t\t\t0.6170 | \n\t\t\t\t\t\t322.16 | \n\t\t\t\t\t\t1.159442 | \n\t\t\t\t\t\t1651.25 | \n\t\t\t\t\t\t316.32 | \n\t\t\t\t\t\t0.6128 | \n\t\t\t\t\t||||||
332.90 | \n\t\t\t\t\t\t1.151804 | \n\t\t\t\t\t\t1625.82 | \n\t\t\t\t\t\t328.46 | \n\t\t\t\t\t\t0.6169 | \n\t\t\t\t\t\t321.91 | \n\t\t\t\t\t\t1.159620 | \n\t\t\t\t\t\t1651.85 | \n\t\t\t\t\t\t316.04 | \n\t\t\t\t\t\t0.6127 | \n\t\t\t\t\t||||||
332.65 | \n\t\t\t\t\t\t1.151981 | \n\t\t\t\t\t\t1626.41 | \n\t\t\t\t\t\t328.17 | \n\t\t\t\t\t\t0.6168 | \n\t\t\t\t\t\t321.65 | \n\t\t\t\t\t\t1.159797 | \n\t\t\t\t\t\t1652.43 | \n\t\t\t\t\t\t315.77 | \n\t\t\t\t\t\t0.6126 | \n\t\t\t\t\t||||||
332.41 | \n\t\t\t\t\t\t1.152159 | \n\t\t\t\t\t\t1626.99 | \n\t\t\t\t\t\t327.88 | \n\t\t\t\t\t\t0.6167 | \n\t\t\t\t\t\t321.40 | \n\t\t\t\t\t\t1.159976 | \n\t\t\t\t\t\t1653.03 | \n\t\t\t\t\t\t315.49 | \n\t\t\t\t\t\t0.6125 | \n\t\t\t\t\t||||||
332.15 | \n\t\t\t\t\t\t1.152338 | \n\t\t\t\t\t\t1627.58 | \n\t\t\t\t\t\t327.59 | \n\t\t\t\t\t\t0.6166 | \n\t\t\t\t\t\t321.15 | \n\t\t\t\t\t\t1.160154 | \n\t\t\t\t\t\t1653.63 | \n\t\t\t\t\t\t315.22 | \n\t\t\t\t\t\t0.6124 | \n\t\t\t\t\t||||||
331.90 | \n\t\t\t\t\t\t1.152514 | \n\t\t\t\t\t\t1628.16 | \n\t\t\t\t\t\t327.31 | \n\t\t\t\t\t\t0.6165 | \n\t\t\t\t\t\t320.91 | \n\t\t\t\t\t\t1.160330 | \n\t\t\t\t\t\t1654.22 | \n\t\t\t\t\t\t314.94 | \n\t\t\t\t\t\t0.6124 | \n\t\t\t\t\t||||||
331.65 | \n\t\t\t\t\t\t1.152694 | \n\t\t\t\t\t\t1628.75 | \n\t\t\t\t\t\t327.02 | \n\t\t\t\t\t\t0.6164 | \n\t\t\t\t\t\t320.66 | \n\t\t\t\t\t\t1.160509 | \n\t\t\t\t\t\t1654.81 | \n\t\t\t\t\t\t314.67 | \n\t\t\t\t\t\t0.6123 | \n\t\t\t\t\t||||||
331.40 | \n\t\t\t\t\t\t1.152871 | \n\t\t\t\t\t\t1629.34 | \n\t\t\t\t\t\t326.74 | \n\t\t\t\t\t\t0.6163 | \n\t\t\t\t\t\t320.40 | \n\t\t\t\t\t\t1.160688 | \n\t\t\t\t\t\t1655.41 | \n\t\t\t\t\t\t314.39 | \n\t\t\t\t\t\t0.6122 | \n\t\t\t\t\t||||||
331.16 | \n\t\t\t\t\t\t1.153048 | \n\t\t\t\t\t\t1629.93 | \n\t\t\t\t\t\t326.45 | \n\t\t\t\t\t\t0.6162 | \n\t\t\t\t\t\t320.15 | \n\t\t\t\t\t\t1.160863 | \n\t\t\t\t\t\t1656.01 | \n\t\t\t\t\t\t314.12 | \n\t\t\t\t\t\t0.6121 | \n\t\t\t\t\t||||||
330.90 | \n\t\t\t\t\t\t1.153225 | \n\t\t\t\t\t\t1630.52 | \n\t\t\t\t\t\t326.16 | \n\t\t\t\t\t\t0.6162 | \n\t\t\t\t\t\t319.90 | \n\t\t\t\t\t\t1.161042 | \n\t\t\t\t\t\t1656.60 | \n\t\t\t\t\t\t313.85 | \n\t\t\t\t\t\t0.6120 | \n\t\t\t\t\t||||||
330.65 | \n\t\t\t\t\t\t1.153405 | \n\t\t\t\t\t\t1631.11 | \n\t\t\t\t\t\t325.88 | \n\t\t\t\t\t\t0.6161 | \n\t\t\t\t\t\t319.65 | \n\t\t\t\t\t\t1.161218 | \n\t\t\t\t\t\t1657.19 | \n\t\t\t\t\t\t313.58 | \n\t\t\t\t\t\t0.6119 | \n\t\t\t\t\t||||||
330.41 | \n\t\t\t\t\t\t1.153582 | \n\t\t\t\t\t\t1631.69 | \n\t\t\t\t\t\t325.59 | \n\t\t\t\t\t\t0.6160 | \n\t\t\t\t\t\t319.40 | \n\t\t\t\t\t\t1.161398 | \n\t\t\t\t\t\t1657.79 | \n\t\t\t\t\t\t313.30 | \n\t\t\t\t\t\t0.6118 | \n\t\t\t\t\t||||||
330.15 | \n\t\t\t\t\t\t1.153761 | \n\t\t\t\t\t\t1632.29 | \n\t\t\t\t\t\t325.30 | \n\t\t\t\t\t\t0.6159 | \n\t\t\t\t\t\t319.15 | \n\t\t\t\t\t\t1.161574 | \n\t\t\t\t\t\t1658.39 | \n\t\t\t\t\t\t313.03 | \n\t\t\t\t\t\t0.6117 | \n\t\t\t\t\t||||||
329.90 | \n\t\t\t\t\t\t1.153939 | \n\t\t\t\t\t\t1632.88 | \n\t\t\t\t\t\t325.02 | \n\t\t\t\t\t\t0.6158 | \n\t\t\t\t\t\t318.91 | \n\t\t\t\t\t\t1.161750 | \n\t\t\t\t\t\t1658.98 | \n\t\t\t\t\t\t312.76 | \n\t\t\t\t\t\t0.6116 | \n\t\t\t\t\t||||||
329.65 | \n\t\t\t\t\t\t1.154114 | \n\t\t\t\t\t\t1633.47 | \n\t\t\t\t\t\t324.73 | \n\t\t\t\t\t\t0.6157 | \n\t\t\t\t\t\t318.65 | \n\t\t\t\t\t\t1.161930 | \n\t\t\t\t\t\t1659.58 | \n\t\t\t\t\t\t312.48 | \n\t\t\t\t\t\t0.6115 | \n\t\t\t\t\t||||||
329.41 | \n\t\t\t\t\t\t1.154294 | \n\t\t\t\t\t\t1634.06 | \n\t\t\t\t\t\t324.45 | \n\t\t\t\t\t\t0.6156 | \n\t\t\t\t\t\t318.40 | \n\t\t\t\t\t\t1.162110 | \n\t\t\t\t\t\t1660.18 | \n\t\t\t\t\t\t312.21 | \n\t\t\t\t\t\t0.6114 | \n\t\t\t\t\t||||||
329.15 | \n\t\t\t\t\t\t1.154469 | \n\t\t\t\t\t\t1634.65 | \n\t\t\t\t\t\t324.17 | \n\t\t\t\t\t\t0.6155 | \n\t\t\t\t\t\t318.16 | \n\t\t\t\t\t\t1.162286 | \n\t\t\t\t\t\t1660.78 | \n\t\t\t\t\t\t311.93 | \n\t\t\t\t\t\t0.6113 | \n\t\t\t\t\t||||||
328.91 | \n\t\t\t\t\t\t1.154648 | \n\t\t\t\t\t\t1635.24 | \n\t\t\t\t\t\t323.88 | \n\t\t\t\t\t\t0.6154 | \n\t\t\t\t\t\t317.90 | \n\t\t\t\t\t\t1.162462 | \n\t\t\t\t\t\t1661.37 | \n\t\t\t\t\t\t311.67 | \n\t\t\t\t\t\t0.6112 | \n\t\t\t\t\t||||||
328.65 | \n\t\t\t\t\t\t1.154826 | \n\t\t\t\t\t\t1635.84 | \n\t\t\t\t\t\t323.59 | \n\t\t\t\t\t\t0.6153 | \n\t\t\t\t\t\t317.65 | \n\t\t\t\t\t\t1.162643 | \n\t\t\t\t\t\t1661.97 | \n\t\t\t\t\t\t311.39 | \n\t\t\t\t\t\t0.6111 | \n\t\t\t\t\t||||||
328.40 | \n\t\t\t\t\t\t1.155003 | \n\t\t\t\t\t\t1636.43 | \n\t\t\t\t\t\t323.31 | \n\t\t\t\t\t\t0.6152 | \n\t\t\t\t\t\t317.41 | \n\t\t\t\t\t\t1.162820 | \n\t\t\t\t\t\t1662.56 | \n\t\t\t\t\t\t311.12 | \n\t\t\t\t\t\t0.6110 | \n\t\t\t\t\t||||||
328.15 | \n\t\t\t\t\t\t1.155181 | \n\t\t\t\t\t\t1637.02 | \n\t\t\t\t\t\t323.03 | \n\t\t\t\t\t\t0.6151 | \n\t\t\t\t\t\t317.15 | \n\t\t\t\t\t\t1.162998 | \n\t\t\t\t\t\t1663.16 | \n\t\t\t\t\t\t310.85 | \n\t\t\t\t\t\t0.6109 | \n\t\t\t\t\t||||||
327.90 | \n\t\t\t\t\t\t1.155360 | \n\t\t\t\t\t\t1637.61 | \n\t\t\t\t\t\t322.75 | \n\t\t\t\t\t\t0.6150 | \n\t\t\t\t\t\t316.91 | \n\t\t\t\t\t\t1.163174 | \n\t\t\t\t\t\t1663.75 | \n\t\t\t\t\t\t310.58 | \n\t\t\t\t\t\t0.6108 | \n\t\t\t\t\t||||||
327.66 | \n\t\t\t\t\t\t1.155535 | \n\t\t\t\t\t\t1638.20 | \n\t\t\t\t\t\t322.47 | \n\t\t\t\t\t\t0.6149 | \n\t\t\t\t\t\t316.65 | \n\t\t\t\t\t\t1.163352 | \n\t\t\t\t\t\t1664.35 | \n\t\t\t\t\t\t310.31 | \n\t\t\t\t\t\t0.6107 | \n\t\t\t\t\t||||||
316.15 | \n\t\t\t\t\t\t1.163706 | \n\t\t\t\t\t\t1665.55 | \n\t\t\t\t\t\t309.77 | \n\t\t\t\t\t\t0.6105 | \n\t\t\t\t\t\t303.90 | \n\t\t\t\t\t\t1.172408 | \n\t\t\t\t\t\t1695.01 | \n\t\t\t\t\t\t296.88 | \n\t\t\t\t\t\t0.6054 | \n\t\t\t\t\t||||||
315.90 | \n\t\t\t\t\t\t1.163885 | \n\t\t\t\t\t\t1666.15 | \n\t\t\t\t\t\t309.50 | \n\t\t\t\t\t\t0.6104 | \n\t\t\t\t\t\t303.65 | \n\t\t\t\t\t\t1.172587 | \n\t\t\t\t\t\t1695.62 | \n\t\t\t\t\t\t296.62 | \n\t\t\t\t\t\t0.6053 | \n\t\t\t\t\t||||||
315.65 | \n\t\t\t\t\t\t1.164062 | \n\t\t\t\t\t\t1666.74 | \n\t\t\t\t\t\t309.23 | \n\t\t\t\t\t\t0.6103 | \n\t\t\t\t\t\t303.40 | \n\t\t\t\t\t\t1.172764 | \n\t\t\t\t\t\t1696.23 | \n\t\t\t\t\t\t296.36 | \n\t\t\t\t\t\t0.6052 | \n\t\t\t\t\t||||||
315.40 | \n\t\t\t\t\t\t1.164240 | \n\t\t\t\t\t\t1667.34 | \n\t\t\t\t\t\t308.96 | \n\t\t\t\t\t\t0.6102 | \n\t\t\t\t\t\t303.15 | \n\t\t\t\t\t\t1.172937 | \n\t\t\t\t\t\t1696.81 | \n\t\t\t\t\t\t296.11 | \n\t\t\t\t\t\t0.6051 | \n\t\t\t\t\t||||||
315.15 | \n\t\t\t\t\t\t1.164417 | \n\t\t\t\t\t\t1667.94 | \n\t\t\t\t\t\t308.70 | \n\t\t\t\t\t\t0.6101 | \n\t\t\t\t\t\t302.90 | \n\t\t\t\t\t\t1.173120 | \n\t\t\t\t\t\t1697.43 | \n\t\t\t\t\t\t295.85 | \n\t\t\t\t\t\t0.6050 | \n\t\t\t\t\t||||||
314.90 | \n\t\t\t\t\t\t1.164597 | \n\t\t\t\t\t\t1668.54 | \n\t\t\t\t\t\t308.43 | \n\t\t\t\t\t\t0.6100 | \n\t\t\t\t\t\t302.65 | \n\t\t\t\t\t\t1.173295 | \n\t\t\t\t\t\t1698.04 | \n\t\t\t\t\t\t295.59 | \n\t\t\t\t\t\t0.6049 | \n\t\t\t\t\t||||||
314.65 | \n\t\t\t\t\t\t1.164774 | \n\t\t\t\t\t\t1669.14 | \n\t\t\t\t\t\t308.16 | \n\t\t\t\t\t\t0.6099 | \n\t\t\t\t\t\t302.40 | \n\t\t\t\t\t\t1.173473 | \n\t\t\t\t\t\t1698.64 | \n\t\t\t\t\t\t295.34 | \n\t\t\t\t\t\t0.6048 | \n\t\t\t\t\t||||||
314.40 | \n\t\t\t\t\t\t1.164951 | \n\t\t\t\t\t\t1669.73 | \n\t\t\t\t\t\t307.89 | \n\t\t\t\t\t\t0.6098 | \n\t\t\t\t\t\t302.15 | \n\t\t\t\t\t\t1.173648 | \n\t\t\t\t\t\t1699.25 | \n\t\t\t\t\t\t295.09 | \n\t\t\t\t\t\t0.6047 | \n\t\t\t\t\t||||||
314.15 | \n\t\t\t\t\t\t1.165128 | \n\t\t\t\t\t\t1670.33 | \n\t\t\t\t\t\t307.63 | \n\t\t\t\t\t\t0.6097 | \n\t\t\t\t\t\t301.90 | \n\t\t\t\t\t\t1.173826 | \n\t\t\t\t\t\t1699.86 | \n\t\t\t\t\t\t294.83 | \n\t\t\t\t\t\t0.6046 | \n\t\t\t\t\t||||||
313.90 | \n\t\t\t\t\t\t1.165305 | \n\t\t\t\t\t\t1670.94 | \n\t\t\t\t\t\t307.35 | \n\t\t\t\t\t\t0.6096 | \n\t\t\t\t\t\t301.65 | \n\t\t\t\t\t\t1.174003 | \n\t\t\t\t\t\t1700.47 | \n\t\t\t\t\t\t294.57 | \n\t\t\t\t\t\t0.6045 | \n\t\t\t\t\t||||||
313.65 | \n\t\t\t\t\t\t1.165485 | \n\t\t\t\t\t\t1671.54 | \n\t\t\t\t\t\t307.09 | \n\t\t\t\t\t\t0.6095 | \n\t\t\t\t\t\t301.40 | \n\t\t\t\t\t\t1.174180 | \n\t\t\t\t\t\t1701.07 | \n\t\t\t\t\t\t294.32 | \n\t\t\t\t\t\t0.6043 | \n\t\t\t\t\t||||||
313.40 | \n\t\t\t\t\t\t1.165661 | \n\t\t\t\t\t\t1672.13 | \n\t\t\t\t\t\t306.82 | \n\t\t\t\t\t\t0.6094 | \n\t\t\t\t\t\t301.15 | \n\t\t\t\t\t\t1.174361 | \n\t\t\t\t\t\t1701.68 | \n\t\t\t\t\t\t294.06 | \n\t\t\t\t\t\t0.6042 | \n\t\t\t\t\t||||||
313.15 | \n\t\t\t\t\t\t1.165839 | \n\t\t\t\t\t\t1672.72 | \n\t\t\t\t\t\t306.56 | \n\t\t\t\t\t\t0.6093 | \n\t\t\t\t\t\t300.90 | \n\t\t\t\t\t\t1.174535 | \n\t\t\t\t\t\t1702.29 | \n\t\t\t\t\t\t293.81 | \n\t\t\t\t\t\t0.6041 | \n\t\t\t\t\t||||||
312.90 | \n\t\t\t\t\t\t1.166018 | \n\t\t\t\t\t\t1673.34 | \n\t\t\t\t\t\t306.29 | \n\t\t\t\t\t\t0.6092 | \n\t\t\t\t\t\t300.65 | \n\t\t\t\t\t\t1.174714 | \n\t\t\t\t\t\t1702.90 | \n\t\t\t\t\t\t293.56 | \n\t\t\t\t\t\t0.6040 | \n\t\t\t\t\t||||||
312.65 | \n\t\t\t\t\t\t1.166194 | \n\t\t\t\t\t\t1673.94 | \n\t\t\t\t\t\t306.02 | \n\t\t\t\t\t\t0.6091 | \n\t\t\t\t\t\t300.40 | \n\t\t\t\t\t\t1.174891 | \n\t\t\t\t\t\t1703.50 | \n\t\t\t\t\t\t293.30 | \n\t\t\t\t\t\t0.6039 | \n\t\t\t\t\t||||||
312.40 | \n\t\t\t\t\t\t1.166372 | \n\t\t\t\t\t\t1674.54 | \n\t\t\t\t\t\t305.75 | \n\t\t\t\t\t\t0.6090 | \n\t\t\t\t\t\t300.15 | \n\t\t\t\t\t\t1.175070 | \n\t\t\t\t\t\t1704.12 | \n\t\t\t\t\t\t293.05 | \n\t\t\t\t\t\t0.6038 | \n\t\t\t\t\t||||||
312.15 | \n\t\t\t\t\t\t1.166549 | \n\t\t\t\t\t\t1675.14 | \n\t\t\t\t\t\t305.49 | \n\t\t\t\t\t\t0.6089 | \n\t\t\t\t\t\t299.90 | \n\t\t\t\t\t\t1.175247 | \n\t\t\t\t\t\t1704.73 | \n\t\t\t\t\t\t292.79 | \n\t\t\t\t\t\t0.6037 | \n\t\t\t\t\t||||||
311.90 | \n\t\t\t\t\t\t1.166726 | \n\t\t\t\t\t\t1675.74 | \n\t\t\t\t\t\t305.22 | \n\t\t\t\t\t\t0.6088 | \n\t\t\t\t\t\t299.65 | \n\t\t\t\t\t\t1.175425 | \n\t\t\t\t\t\t1705.33 | \n\t\t\t\t\t\t292.54 | \n\t\t\t\t\t\t0.6036 | \n\t\t\t\t\t||||||
311.65 | \n\t\t\t\t\t\t1.166903 | \n\t\t\t\t\t\t1676.34 | \n\t\t\t\t\t\t304.96 | \n\t\t\t\t\t\t0.6086 | \n\t\t\t\t\t\t299.40 | \n\t\t\t\t\t\t1.175602 | \n\t\t\t\t\t\t1705.95 | \n\t\t\t\t\t\t292.29 | \n\t\t\t\t\t\t0.6035 | \n\t\t\t\t\t||||||
311.40 | \n\t\t\t\t\t\t1.167085 | \n\t\t\t\t\t\t1676.95 | \n\t\t\t\t\t\t304.69 | \n\t\t\t\t\t\t0.6085 | \n\t\t\t\t\t\t299.15 | \n\t\t\t\t\t\t1.175780 | \n\t\t\t\t\t\t1706.55 | \n\t\t\t\t\t\t292.04 | \n\t\t\t\t\t\t0.6034 | \n\t\t\t\t\t||||||
311.15 | \n\t\t\t\t\t\t1.167260 | \n\t\t\t\t\t\t1677.55 | \n\t\t\t\t\t\t304.43 | \n\t\t\t\t\t\t0.6084 | \n\t\t\t\t\t\t298.90 | \n\t\t\t\t\t\t1.175955 | \n\t\t\t\t\t\t1707.16 | \n\t\t\t\t\t\t291.78 | \n\t\t\t\t\t\t0.6033 | \n\t\t\t\t\t||||||
310.90 | \n\t\t\t\t\t\t1.167437 | \n\t\t\t\t\t\t1678.14 | \n\t\t\t\t\t\t304.17 | \n\t\t\t\t\t\t0.6083 | \n\t\t\t\t\t\t298.65 | \n\t\t\t\t\t\t1.176133 | \n\t\t\t\t\t\t1707.77 | \n\t\t\t\t\t\t291.53 | \n\t\t\t\t\t\t0.6032 | \n\t\t\t\t\t||||||
310.65 | \n\t\t\t\t\t\t1.167617 | \n\t\t\t\t\t\t1678.74 | \n\t\t\t\t\t\t303.90 | \n\t\t\t\t\t\t0.6082 | \n\t\t\t\t\t\t298.40 | \n\t\t\t\t\t\t1.176311 | \n\t\t\t\t\t\t1708.39 | \n\t\t\t\t\t\t291.28 | \n\t\t\t\t\t\t0.6030 | \n\t\t\t\t\t||||||
310.40 | \n\t\t\t\t\t\t1.167794 | \n\t\t\t\t\t\t1679.35 | \n\t\t\t\t\t\t303.63 | \n\t\t\t\t\t\t0.6081 | \n\t\t\t\t\t\t298.15 | \n\t\t\t\t\t\t1.176489 | \n\t\t\t\t\t\t1709.00 | \n\t\t\t\t\t\t291.02 | \n\t\t\t\t\t\t0.6029 | \n\t\t\t\t\t||||||
310.15 | \n\t\t\t\t\t\t1.167970 | \n\t\t\t\t\t\t1679.94 | \n\t\t\t\t\t\t303.38 | \n\t\t\t\t\t\t0.6080 | \n\t\t\t\t\t\t297.90 | \n\t\t\t\t\t\t1.176666 | \n\t\t\t\t\t\t1709.61 | \n\t\t\t\t\t\t290.77 | \n\t\t\t\t\t\t0.6028 | \n\t\t\t\t\t||||||
309.90 | \n\t\t\t\t\t\t1.168149 | \n\t\t\t\t\t\t1680.55 | \n\t\t\t\t\t\t303.11 | \n\t\t\t\t\t\t0.6079 | \n\t\t\t\t\t\t297.65 | \n\t\t\t\t\t\t1.176842 | \n\t\t\t\t\t\t1710.22 | \n\t\t\t\t\t\t290.52 | \n\t\t\t\t\t\t0.6027 | \n\t\t\t\t\t||||||
309.65 | \n\t\t\t\t\t\t1.168325 | \n\t\t\t\t\t\t1681.15 | \n\t\t\t\t\t\t302.85 | \n\t\t\t\t\t\t0.6078 | \n\t\t\t\t\t\t297.40 | \n\t\t\t\t\t\t1.177019 | \n\t\t\t\t\t\t1710.84 | \n\t\t\t\t\t\t290.27 | \n\t\t\t\t\t\t0.6026 | \n\t\t\t\t\t||||||
309.40 | \n\t\t\t\t\t\t1.168502 | \n\t\t\t\t\t\t1681.75 | \n\t\t\t\t\t\t302.59 | \n\t\t\t\t\t\t0.6077 | \n\t\t\t\t\t\t297.15 | \n\t\t\t\t\t\t1.177201 | \n\t\t\t\t\t\t1711.45 | \n\t\t\t\t\t\t290.02 | \n\t\t\t\t\t\t0.6025 | \n\t\t\t\t\t||||||
309.15 | \n\t\t\t\t\t\t1.168680 | \n\t\t\t\t\t\t1682.35 | \n\t\t\t\t\t\t302.32 | \n\t\t\t\t\t\t0.6076 | \n\t\t\t\t\t\t296.90 | \n\t\t\t\t\t\t1.177373 | \n\t\t\t\t\t\t1712.06 | \n\t\t\t\t\t\t289.77 | \n\t\t\t\t\t\t0.6024 | \n\t\t\t\t\t||||||
308.90 | \n\t\t\t\t\t\t1.168859 | \n\t\t\t\t\t\t1682.96 | \n\t\t\t\t\t\t302.06 | \n\t\t\t\t\t\t0.6075 | \n\t\t\t\t\t\t296.65 | \n\t\t\t\t\t\t1.177553 | \n\t\t\t\t\t\t1712.67 | \n\t\t\t\t\t\t289.52 | \n\t\t\t\t\t\t0.6023 | \n\t\t\t\t\t||||||
308.65 | \n\t\t\t\t\t\t1.169036 | \n\t\t\t\t\t\t1683.55 | \n\t\t\t\t\t\t301.80 | \n\t\t\t\t\t\t0.6074 | \n\t\t\t\t\t\t296.40 | \n\t\t\t\t\t\t1.177729 | \n\t\t\t\t\t\t1713.28 | \n\t\t\t\t\t\t289.27 | \n\t\t\t\t\t\t0.6022 | \n\t\t\t\t\t||||||
308.40 | \n\t\t\t\t\t\t1.169213 | \n\t\t\t\t\t\t1684.16 | \n\t\t\t\t\t\t301.54 | \n\t\t\t\t\t\t0.6073 | \n\t\t\t\t\t\t296.15 | \n\t\t\t\t\t\t1.177905 | \n\t\t\t\t\t\t1713.90 | \n\t\t\t\t\t\t289.01 | \n\t\t\t\t\t\t0.6021 | \n\t\t\t\t\t||||||
308.15 | \n\t\t\t\t\t\t1.169391 | \n\t\t\t\t\t\t1684.76 | \n\t\t\t\t\t\t301.28 | \n\t\t\t\t\t\t0.6072 | \n\t\t\t\t\t\t295.90 | \n\t\t\t\t\t\t1.178085 | \n\t\t\t\t\t\t1714.52 | \n\t\t\t\t\t\t288.76 | \n\t\t\t\t\t\t0.6019 | \n\t\t\t\t\t||||||
307.90 | \n\t\t\t\t\t\t1.169567 | \n\t\t\t\t\t\t1685.36 | \n\t\t\t\t\t\t301.02 | \n\t\t\t\t\t\t0.6071 | \n\t\t\t\t\t\t295.65 | \n\t\t\t\t\t\t1.178265 | \n\t\t\t\t\t\t1715.13 | \n\t\t\t\t\t\t288.51 | \n\t\t\t\t\t\t0.6018 | \n\t\t\t\t\t||||||
307.65 | \n\t\t\t\t\t\t1.169742 | \n\t\t\t\t\t\t1685.96 | \n\t\t\t\t\t\t300.76 | \n\t\t\t\t\t\t0.6070 | \n\t\t\t\t\t\t295.40 | \n\t\t\t\t\t\t1.178438 | \n\t\t\t\t\t\t1715.75 | \n\t\t\t\t\t\t288.26 | \n\t\t\t\t\t\t0.6017 | \n\t\t\t\t\t||||||
307.40 | \n\t\t\t\t\t\t1.169922 | \n\t\t\t\t\t\t1686.56 | \n\t\t\t\t\t\t300.50 | \n\t\t\t\t\t\t0.6069 | \n\t\t\t\t\t\t295.15 | \n\t\t\t\t\t\t1.178617 | \n\t\t\t\t\t\t1716.36 | \n\t\t\t\t\t\t288.01 | \n\t\t\t\t\t\t0.6016 | \n\t\t\t\t\t||||||
307.15 | \n\t\t\t\t\t\t1.170102 | \n\t\t\t\t\t\t1687.17 | \n\t\t\t\t\t\t300.23 | \n\t\t\t\t\t\t0.6068 | \n\t\t\t\t\t\t294.90 | \n\t\t\t\t\t\t1.178798 | \n\t\t\t\t\t\t1716.97 | \n\t\t\t\t\t\t287.76 | \n\t\t\t\t\t\t0.6015 | \n\t\t\t\t\t||||||
306.90 | \n\t\t\t\t\t\t1.170276 | \n\t\t\t\t\t\t1687.77 | \n\t\t\t\t\t\t299.98 | \n\t\t\t\t\t\t0.6067 | \n\t\t\t\t\t\t294.65 | \n\t\t\t\t\t\t1.178971 | \n\t\t\t\t\t\t1717.58 | \n\t\t\t\t\t\t287.52 | \n\t\t\t\t\t\t0.6014 | \n\t\t\t\t\t||||||
306.65 | \n\t\t\t\t\t\t1.170454 | \n\t\t\t\t\t\t1688.37 | \n\t\t\t\t\t\t299.72 | \n\t\t\t\t\t\t0.6066 | \n\t\t\t\t\t\t294.40 | \n\t\t\t\t\t\t1.179148 | \n\t\t\t\t\t\t1718.20 | \n\t\t\t\t\t\t287.27 | \n\t\t\t\t\t\t0.6013 | \n\t\t\t\t\t||||||
306.40 | \n\t\t\t\t\t\t1.170632 | \n\t\t\t\t\t\t1688.98 | \n\t\t\t\t\t\t299.45 | \n\t\t\t\t\t\t0.6065 | \n\t\t\t\t\t\t294.15 | \n\t\t\t\t\t\t1.179325 | \n\t\t\t\t\t\t1718.81 | \n\t\t\t\t\t\t287.02 | \n\t\t\t\t\t\t0.6012 | \n\t\t\t\t\t||||||
306.15 | \n\t\t\t\t\t\t1.170810 | \n\t\t\t\t\t\t1689.58 | \n\t\t\t\t\t\t299.20 | \n\t\t\t\t\t\t0.6064 | \n\t\t\t\t\t\t293.90 | \n\t\t\t\t\t\t1.179505 | \n\t\t\t\t\t\t1719.42 | \n\t\t\t\t\t\t286.77 | \n\t\t\t\t\t\t0.6011 | \n\t\t\t\t\t||||||
305.90 | \n\t\t\t\t\t\t1.170986 | \n\t\t\t\t\t\t1690.18 | \n\t\t\t\t\t\t298.94 | \n\t\t\t\t\t\t0.6063 | \n\t\t\t\t\t\t293.65 | \n\t\t\t\t\t\t1.179682 | \n\t\t\t\t\t\t1720.04 | \n\t\t\t\t\t\t286.52 | \n\t\t\t\t\t\t0.6009 | \n\t\t\t\t\t||||||
305.65 | \n\t\t\t\t\t\t1.171165 | \n\t\t\t\t\t\t1690.79 | \n\t\t\t\t\t\t298.68 | \n\t\t\t\t\t\t0.6062 | \n\t\t\t\t\t\t293.40 | \n\t\t\t\t\t\t1.179858 | \n\t\t\t\t\t\t1720.66 | \n\t\t\t\t\t\t286.27 | \n\t\t\t\t\t\t0.6008 | \n\t\t\t\t\t||||||
305.40 | \n\t\t\t\t\t\t1.171343 | \n\t\t\t\t\t\t1691.39 | \n\t\t\t\t\t\t298.42 | \n\t\t\t\t\t\t0.6060 | \n\t\t\t\t\t\t293.15 | \n\t\t\t\t\t\t1.180037 | \n\t\t\t\t\t\t1721.27 | \n\t\t\t\t\t\t286.03 | \n\t\t\t\t\t\t0.6007 | \n\t\t\t\t\t||||||
305.15 | \n\t\t\t\t\t\t1.171518 | \n\t\t\t\t\t\t1691.99 | \n\t\t\t\t\t\t298.16 | \n\t\t\t\t\t\t0.6059 | \n\t\t\t\t\t\t292.90 | \n\t\t\t\t\t\t1.180210 | \n\t\t\t\t\t\t1721.88 | \n\t\t\t\t\t\t285.78 | \n\t\t\t\t\t\t0.6006 | \n\t\t\t\t\t||||||
304.90 | \n\t\t\t\t\t\t1.171699 | \n\t\t\t\t\t\t1692.60 | \n\t\t\t\t\t\t297.90 | \n\t\t\t\t\t\t0.6058 | \n\t\t\t\t\t\t292.65 | \n\t\t\t\t\t\t1.180390 | \n\t\t\t\t\t\t1722.50 | \n\t\t\t\t\t\t285.53 | \n\t\t\t\t\t\t0.6005 | \n\t\t\t\t\t||||||
304.40 | \n\t\t\t\t\t\t1.172053 | \n\t\t\t\t\t\t1693.80 | \n\t\t\t\t\t\t297.39 | \n\t\t\t\t\t\t0.6056 | \n\t\t\t\t\t\t292.15 | \n\t\t\t\t\t\t1.180744 | \n\t\t\t\t\t\t1723.72 | \n\t\t\t\t\t\t285.04 | \n\t\t\t\t\t\t0.6003 | \n\t\t\t\t\t||||||
304.15 | \n\t\t\t\t\t\t1.172230 | \n\t\t\t\t\t\t1694.41 | \n\t\t\t\t\t\t297.13 | \n\t\t\t\t\t\t0.6055 | \n\t\t\t\t\t\t291.90 | \n\t\t\t\t\t\t1.180923 | \n\t\t\t\t\t\t1724.34 | \n\t\t\t\t\t\t284.80 | \n\t\t\t\t\t\t0.6002 | \n\t\t\t\t\t||||||
291.65 | \n\t\t\t\t\t\t1.181104 | \n\t\t\t\t\t\t1724.95 | \n\t\t\t\t\t\t284.55 | \n\t\t\t\t\t\t0.6000 | \n\t\t\t\t\t\t279.40 | \n\t\t\t\t\t\t1.189760 | \n\t\t\t\t\t\t1755.38 | \n\t\t\t\t\t\t272.77 | \n\t\t\t\t\t\t0.5944 | \n\t\t\t\t\t||||||
291.40 | \n\t\t\t\t\t\t1.181278 | \n\t\t\t\t\t\t1725.57 | \n\t\t\t\t\t\t284.30 | \n\t\t\t\t\t\t0.5999 | \n\t\t\t\t\t\t279.15 | \n\t\t\t\t\t\t1.189935 | \n\t\t\t\t\t\t1756.03 | \n\t\t\t\t\t\t272.53 | \n\t\t\t\t\t\t0.5943 | \n\t\t\t\t\t||||||
291.15 | \n\t\t\t\t\t\t1.181453 | \n\t\t\t\t\t\t1726.18 | \n\t\t\t\t\t\t284.06 | \n\t\t\t\t\t\t0.5998 | \n\t\t\t\t\t\t278.90 | \n\t\t\t\t\t\t1.190108 | \n\t\t\t\t\t\t1756.62 | \n\t\t\t\t\t\t272.31 | \n\t\t\t\t\t\t0.5941 | \n\t\t\t\t\t||||||
290.90 | \n\t\t\t\t\t\t1.181631 | \n\t\t\t\t\t\t1726.80 | \n\t\t\t\t\t\t283.81 | \n\t\t\t\t\t\t0.5997 | \n\t\t\t\t\t\t278.65 | \n\t\t\t\t\t\t1.190288 | \n\t\t\t\t\t\t1757.23 | \n\t\t\t\t\t\t272.08 | \n\t\t\t\t\t\t0.5940 | \n\t\t\t\t\t||||||
290.65 | \n\t\t\t\t\t\t1.181809 | \n\t\t\t\t\t\t1727.43 | \n\t\t\t\t\t\t283.56 | \n\t\t\t\t\t\t0.5996 | \n\t\t\t\t\t\t278.40 | \n\t\t\t\t\t\t1.190464 | \n\t\t\t\t\t\t1757.88 | \n\t\t\t\t\t\t271.83 | \n\t\t\t\t\t\t0.5939 | \n\t\t\t\t\t||||||
290.40 | \n\t\t\t\t\t\t1.181990 | \n\t\t\t\t\t\t1728.05 | \n\t\t\t\t\t\t283.32 | \n\t\t\t\t\t\t0.5995 | \n\t\t\t\t\t\t278.15 | \n\t\t\t\t\t\t1.190632 | \n\t\t\t\t\t\t1758.50 | \n\t\t\t\t\t\t271.60 | \n\t\t\t\t\t\t0.5938 | \n\t\t\t\t\t||||||
290.15 | \n\t\t\t\t\t\t1.182162 | \n\t\t\t\t\t\t1728.67 | \n\t\t\t\t\t\t283.07 | \n\t\t\t\t\t\t0.5994 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
289.90 | \n\t\t\t\t\t\t1.182339 | \n\t\t\t\t\t\t1729.29 | \n\t\t\t\t\t\t282.83 | \n\t\t\t\t\t\t0.5993 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
289.65 | \n\t\t\t\t\t\t1.182515 | \n\t\t\t\t\t\t1729.91 | \n\t\t\t\t\t\t282.58 | \n\t\t\t\t\t\t0.5991 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
289.39 | \n\t\t\t\t\t\t1.182700 | \n\t\t\t\t\t\t1730.84 | \n\t\t\t\t\t\t282.24 | \n\t\t\t\t\t\t0.5990 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
289.15 | \n\t\t\t\t\t\t1.182877 | \n\t\t\t\t\t\t1731.59 | \n\t\t\t\t\t\t281.95 | \n\t\t\t\t\t\t0.5989 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
288.89 | \n\t\t\t\t\t\t1.183052 | \n\t\t\t\t\t\t1732.13 | \n\t\t\t\t\t\t281.73 | \n\t\t\t\t\t\t0.5988 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
288.64 | \n\t\t\t\t\t\t1.183228 | \n\t\t\t\t\t\t1732.78 | \n\t\t\t\t\t\t281.48 | \n\t\t\t\t\t\t0.5987 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
288.39 | \n\t\t\t\t\t\t1.183407 | \n\t\t\t\t\t\t1733.34 | \n\t\t\t\t\t\t281.25 | \n\t\t\t\t\t\t0.5986 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
288.15 | \n\t\t\t\t\t\t1.183574 | \n\t\t\t\t\t\t1733.91 | \n\t\t\t\t\t\t281.03 | \n\t\t\t\t\t\t0.5985 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
287.90 | \n\t\t\t\t\t\t1.183753 | \n\t\t\t\t\t\t1734.51 | \n\t\t\t\t\t\t280.79 | \n\t\t\t\t\t\t0.5983 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
287.64 | \n\t\t\t\t\t\t1.183941 | \n\t\t\t\t\t\t1735.04 | \n\t\t\t\t\t\t280.58 | \n\t\t\t\t\t\t0.5982 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
287.40 | \n\t\t\t\t\t\t1.184107 | \n\t\t\t\t\t\t1735.67 | \n\t\t\t\t\t\t280.33 | \n\t\t\t\t\t\t0.5981 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
287.15 | \n\t\t\t\t\t\t1.184289 | \n\t\t\t\t\t\t1736.27 | \n\t\t\t\t\t\t280.10 | \n\t\t\t\t\t\t0.5980 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
286.90 | \n\t\t\t\t\t\t1.184462 | \n\t\t\t\t\t\t1736.82 | \n\t\t\t\t\t\t279.88 | \n\t\t\t\t\t\t0.5979 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
286.65 | \n\t\t\t\t\t\t1.184637 | \n\t\t\t\t\t\t1737.45 | \n\t\t\t\t\t\t279.63 | \n\t\t\t\t\t\t0.5978 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
286.40 | \n\t\t\t\t\t\t1.184815 | \n\t\t\t\t\t\t1738.07 | \n\t\t\t\t\t\t279.39 | \n\t\t\t\t\t\t0.5977 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
286.15 | \n\t\t\t\t\t\t1.184986 | \n\t\t\t\t\t\t1738.68 | \n\t\t\t\t\t\t279.16 | \n\t\t\t\t\t\t0.5975 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
285.90 | \n\t\t\t\t\t\t1.185168 | \n\t\t\t\t\t\t1739.24 | \n\t\t\t\t\t\t278.93 | \n\t\t\t\t\t\t0.5974 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
285.65 | \n\t\t\t\t\t\t1.185344 | \n\t\t\t\t\t\t1739.86 | \n\t\t\t\t\t\t278.69 | \n\t\t\t\t\t\t0.5973 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
285.40 | \n\t\t\t\t\t\t1.185519 | \n\t\t\t\t\t\t1740.47 | \n\t\t\t\t\t\t278.46 | \n\t\t\t\t\t\t0.5972 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
285.15 | \n\t\t\t\t\t\t1.185700 | \n\t\t\t\t\t\t1741.08 | \n\t\t\t\t\t\t278.22 | \n\t\t\t\t\t\t0.5971 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
284.90 | \n\t\t\t\t\t\t1.185886 | \n\t\t\t\t\t\t1741.82 | \n\t\t\t\t\t\t277.94 | \n\t\t\t\t\t\t0.5970 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
284.64 | \n\t\t\t\t\t\t1.186059 | \n\t\t\t\t\t\t1742.42 | \n\t\t\t\t\t\t277.71 | \n\t\t\t\t\t\t0.5968 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
284.40 | \n\t\t\t\t\t\t1.186228 | \n\t\t\t\t\t\t1742.99 | \n\t\t\t\t\t\t277.49 | \n\t\t\t\t\t\t0.5967 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
284.15 | \n\t\t\t\t\t\t1.186403 | \n\t\t\t\t\t\t1743.61 | \n\t\t\t\t\t\t277.25 | \n\t\t\t\t\t\t0.5966 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
283.90 | \n\t\t\t\t\t\t1.186582 | \n\t\t\t\t\t\t1744.21 | \n\t\t\t\t\t\t277.02 | \n\t\t\t\t\t\t0.5965 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
283.65 | \n\t\t\t\t\t\t1.186756 | \n\t\t\t\t\t\t1744.84 | \n\t\t\t\t\t\t276.78 | \n\t\t\t\t\t\t0.5964 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
283.40 | \n\t\t\t\t\t\t1.186933 | \n\t\t\t\t\t\t1745.46 | \n\t\t\t\t\t\t276.54 | \n\t\t\t\t\t\t0.5963 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
283.15 | \n\t\t\t\t\t\t1.187110 | \n\t\t\t\t\t\t1746.08 | \n\t\t\t\t\t\t276.30 | \n\t\t\t\t\t\t0.5961 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
282.90 | \n\t\t\t\t\t\t1.187288 | \n\t\t\t\t\t\t1746.70 | \n\t\t\t\t\t\t276.06 | \n\t\t\t\t\t\t0.5960 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
282.65 | \n\t\t\t\t\t\t1.187467 | \n\t\t\t\t\t\t1747.32 | \n\t\t\t\t\t\t275.82 | \n\t\t\t\t\t\t0.5959 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
282.40 | \n\t\t\t\t\t\t1.187641 | \n\t\t\t\t\t\t1747.95 | \n\t\t\t\t\t\t275.59 | \n\t\t\t\t\t\t0.5958 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
282.15 | \n\t\t\t\t\t\t1.187817 | \n\t\t\t\t\t\t1748.57 | \n\t\t\t\t\t\t275.35 | \n\t\t\t\t\t\t0.5957 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
281.90 | \n\t\t\t\t\t\t1.187991 | \n\t\t\t\t\t\t1749.20 | \n\t\t\t\t\t\t275.11 | \n\t\t\t\t\t\t0.5956 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
281.65 | \n\t\t\t\t\t\t1.188172 | \n\t\t\t\t\t\t1749.83 | \n\t\t\t\t\t\t274.87 | \n\t\t\t\t\t\t0.5954 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
281.40 | \n\t\t\t\t\t\t1.188344 | \n\t\t\t\t\t\t1750.39 | \n\t\t\t\t\t\t274.66 | \n\t\t\t\t\t\t0.5953 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
281.15 | \n\t\t\t\t\t\t1.188523 | \n\t\t\t\t\t\t1751.00 | \n\t\t\t\t\t\t274.42 | \n\t\t\t\t\t\t0.5952 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
280.90 | \n\t\t\t\t\t\t1.188699 | \n\t\t\t\t\t\t1751.60 | \n\t\t\t\t\t\t274.19 | \n\t\t\t\t\t\t0.5951 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
280.40 | \n\t\t\t\t\t\t1.189050 | \n\t\t\t\t\t\t1752.86 | \n\t\t\t\t\t\t273.72 | \n\t\t\t\t\t\t0.5948 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
280.15 | \n\t\t\t\t\t\t1.189231 | \n\t\t\t\t\t\t1753.49 | \n\t\t\t\t\t\t273.48 | \n\t\t\t\t\t\t0.5947 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
279.90 | \n\t\t\t\t\t\t1.189407 | \n\t\t\t\t\t\t1754.12 | \n\t\t\t\t\t\t273.24 | \n\t\t\t\t\t\t0.5946 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
279.65 | \n\t\t\t\t\t\t1.189580 | \n\t\t\t\t\t\t1754.75 | \n\t\t\t\t\t\t273.01 | \n\t\t\t\t\t\t0.5945 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t | ||||||
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t|||||||||||||||
T (K) | \n\t\t\t\t\t\tρ (gcm-3) | \n\t\t\t\t\t\tu (ms-1) | \n\t\t\t\t\t\tκS\n\t\t\t\t\t\t\t (TPa-1) | \n\t\t\t\t\t\t103 · α (K-1) | \n\t\t\t\t\t\tT (K) | \n\t\t\t\t\t\tρ (gcm-3) | \n\t\t\t\t\t\tu (ms-1) | \n\t\t\t\t\t\tκS\n\t\t\t\t\t\t\t (TPa-1) | \n\t\t\t\t\t\t103 · α (K-1) | \n\t\t\t\t\t||||||
338.15 | \n\t\t\t\t\t\t1.020672 | \n\t\t\t\t\t\t1468.15 | \n\t\t\t\t\t\t454.54 | \n\t\t\t\t\t\t-3.6736 | \n\t\t\t\t\t\t307.90 | \n\t\t\t\t\t\t1.039467 | \n\t\t\t\t\t\t1558.18 | \n\t\t\t\t\t\t396.24 | \n\t\t\t\t\t\t-3.8607 | \n\t\t\t\t\t||||||
337.90 | \n\t\t\t\t\t\t1.020820 | \n\t\t\t\t\t\t1468.77 | \n\t\t\t\t\t\t454.09 | \n\t\t\t\t\t\t-3.6729 | \n\t\t\t\t\t\t307.65 | \n\t\t\t\t\t\t1.039618 | \n\t\t\t\t\t\t1558.99 | \n\t\t\t\t\t\t395.77 | \n\t\t\t\t\t\t-3.8646 | \n\t\t\t\t\t||||||
337.65 | \n\t\t\t\t\t\t1.020969 | \n\t\t\t\t\t\t1469.46 | \n\t\t\t\t\t\t453.60 | \n\t\t\t\t\t\t-3.6723 | \n\t\t\t\t\t\t307.40 | \n\t\t\t\t\t\t1.039772 | \n\t\t\t\t\t\t1559.78 | \n\t\t\t\t\t\t395.31 | \n\t\t\t\t\t\t-3.8684 | \n\t\t\t\t\t||||||
337.40 | \n\t\t\t\t\t\t1.021126 | \n\t\t\t\t\t\t1470.18 | \n\t\t\t\t\t\t453.08 | \n\t\t\t\t\t\t-3.6716 | \n\t\t\t\t\t\t307.15 | \n\t\t\t\t\t\t1.039925 | \n\t\t\t\t\t\t1560.61 | \n\t\t\t\t\t\t394.83 | \n\t\t\t\t\t\t-3.8723 | \n\t\t\t\t\t||||||
337.15 | \n\t\t\t\t\t\t1.021280 | \n\t\t\t\t\t\t1470.87 | \n\t\t\t\t\t\t452.59 | \n\t\t\t\t\t\t-3.6710 | \n\t\t\t\t\t\t306.90 | \n\t\t\t\t\t\t1.040077 | \n\t\t\t\t\t\t1561.44 | \n\t\t\t\t\t\t394.35 | \n\t\t\t\t\t\t-3.8763 | \n\t\t\t\t\t||||||
336.90 | \n\t\t\t\t\t\t1.021436 | \n\t\t\t\t\t\t1471.58 | \n\t\t\t\t\t\t452.09 | \n\t\t\t\t\t\t-3.6705 | \n\t\t\t\t\t\t306.65 | \n\t\t\t\t\t\t1.040230 | \n\t\t\t\t\t\t1562.25 | \n\t\t\t\t\t\t393.89 | \n\t\t\t\t\t\t-3.8803 | \n\t\t\t\t\t||||||
336.65 | \n\t\t\t\t\t\t1.021593 | \n\t\t\t\t\t\t1472.29 | \n\t\t\t\t\t\t451.58 | \n\t\t\t\t\t\t-3.6700 | \n\t\t\t\t\t\t306.40 | \n\t\t\t\t\t\t1.040384 | \n\t\t\t\t\t\t1563.08 | \n\t\t\t\t\t\t393.41 | \n\t\t\t\t\t\t-3.8843 | \n\t\t\t\t\t||||||
336.40 | \n\t\t\t\t\t\t1.021745 | \n\t\t\t\t\t\t1473.00 | \n\t\t\t\t\t\t451.08 | \n\t\t\t\t\t\t-3.6695 | \n\t\t\t\t\t\t306.15 | \n\t\t\t\t\t\t1.040533 | \n\t\t\t\t\t\t1563.89 | \n\t\t\t\t\t\t392.94 | \n\t\t\t\t\t\t-3.8883 | \n\t\t\t\t\t||||||
336.15 | \n\t\t\t\t\t\t1.021898 | \n\t\t\t\t\t\t1473.73 | \n\t\t\t\t\t\t450.56 | \n\t\t\t\t\t\t-3.6690 | \n\t\t\t\t\t\t305.90 | \n\t\t\t\t\t\t1.040687 | \n\t\t\t\t\t\t1564.73 | \n\t\t\t\t\t\t392.47 | \n\t\t\t\t\t\t-3.8924 | \n\t\t\t\t\t||||||
335.65 | \n\t\t\t\t\t\t1.022205 | \n\t\t\t\t\t\t1475.12 | \n\t\t\t\t\t\t449.58 | \n\t\t\t\t\t\t-3.6683 | \n\t\t\t\t\t\t305.40 | \n\t\t\t\t\t\t1.040991 | \n\t\t\t\t\t\t1566.38 | \n\t\t\t\t\t\t391.52 | \n\t\t\t\t\t\t-3.9007 | \n\t\t\t\t\t||||||
335.40 | \n\t\t\t\t\t\t1.022364 | \n\t\t\t\t\t\t1475.83 | \n\t\t\t\t\t\t449.08 | \n\t\t\t\t\t\t-3.6679 | \n\t\t\t\t\t\t305.15 | \n\t\t\t\t\t\t1.041143 | \n\t\t\t\t\t\t1567.19 | \n\t\t\t\t\t\t391.06 | \n\t\t\t\t\t\t-3.9050 | \n\t\t\t\t\t||||||
335.15 | \n\t\t\t\t\t\t1.022520 | \n\t\t\t\t\t\t1476.54 | \n\t\t\t\t\t\t448.58 | \n\t\t\t\t\t\t-3.6677 | \n\t\t\t\t\t\t304.90 | \n\t\t\t\t\t\t1.041297 | \n\t\t\t\t\t\t1568.03 | \n\t\t\t\t\t\t390.59 | \n\t\t\t\t\t\t-3.9092 | \n\t\t\t\t\t||||||
334.90 | \n\t\t\t\t\t\t1.022671 | \n\t\t\t\t\t\t1477.24 | \n\t\t\t\t\t\t448.09 | \n\t\t\t\t\t\t-3.6674 | \n\t\t\t\t\t\t304.65 | \n\t\t\t\t\t\t1.041450 | \n\t\t\t\t\t\t1568.87 | \n\t\t\t\t\t\t390.11 | \n\t\t\t\t\t\t-3.9135 | \n\t\t\t\t\t||||||
334.65 | \n\t\t\t\t\t\t1.022828 | \n\t\t\t\t\t\t1477.95 | \n\t\t\t\t\t\t447.59 | \n\t\t\t\t\t\t-3.6672 | \n\t\t\t\t\t\t304.40 | \n\t\t\t\t\t\t1.041602 | \n\t\t\t\t\t\t1569.72 | \n\t\t\t\t\t\t389.63 | \n\t\t\t\t\t\t-3.9178 | \n\t\t\t\t\t||||||
334.40 | \n\t\t\t\t\t\t1.022986 | \n\t\t\t\t\t\t1478.66 | \n\t\t\t\t\t\t447.09 | \n\t\t\t\t\t\t-3.6670 | \n\t\t\t\t\t\t304.15 | \n\t\t\t\t\t\t1.041753 | \n\t\t\t\t\t\t1570.56 | \n\t\t\t\t\t\t389.16 | \n\t\t\t\t\t\t-3.9222 | \n\t\t\t\t\t||||||
334.15 | \n\t\t\t\t\t\t1.023146 | \n\t\t\t\t\t\t1479.37 | \n\t\t\t\t\t\t446.59 | \n\t\t\t\t\t\t-3.6669 | \n\t\t\t\t\t\t303.90 | \n\t\t\t\t\t\t1.041907 | \n\t\t\t\t\t\t1571.39 | \n\t\t\t\t\t\t388.69 | \n\t\t\t\t\t\t-3.9266 | \n\t\t\t\t\t||||||
333.90 | \n\t\t\t\t\t\t1.023305 | \n\t\t\t\t\t\t1480.07 | \n\t\t\t\t\t\t446.10 | \n\t\t\t\t\t\t-3.6668 | \n\t\t\t\t\t\t303.65 | \n\t\t\t\t\t\t1.042059 | \n\t\t\t\t\t\t1572.25 | \n\t\t\t\t\t\t388.21 | \n\t\t\t\t\t\t-3.9310 | \n\t\t\t\t\t||||||
333.65 | \n\t\t\t\t\t\t1.023463 | \n\t\t\t\t\t\t1480.78 | \n\t\t\t\t\t\t445.60 | \n\t\t\t\t\t\t-3.6667 | \n\t\t\t\t\t\t303.40 | \n\t\t\t\t\t\t1.042209 | \n\t\t\t\t\t\t1573.09 | \n\t\t\t\t\t\t387.74 | \n\t\t\t\t\t\t-3.9355 | \n\t\t\t\t\t||||||
333.40 | \n\t\t\t\t\t\t1.023622 | \n\t\t\t\t\t\t1481.49 | \n\t\t\t\t\t\t445.11 | \n\t\t\t\t\t\t-3.6667 | \n\t\t\t\t\t\t303.15 | \n\t\t\t\t\t\t1.042363 | \n\t\t\t\t\t\t1573.94 | \n\t\t\t\t\t\t387.26 | \n\t\t\t\t\t\t-3.9400 | \n\t\t\t\t\t||||||
333.15 | \n\t\t\t\t\t\t1.023780 | \n\t\t\t\t\t\t1482.20 | \n\t\t\t\t\t\t444.61 | \n\t\t\t\t\t\t-3.6667 | \n\t\t\t\t\t\t302.90 | \n\t\t\t\t\t\t1.042516 | \n\t\t\t\t\t\t1574.79 | \n\t\t\t\t\t\t386.79 | \n\t\t\t\t\t\t-3.9445 | \n\t\t\t\t\t||||||
332.90 | \n\t\t\t\t\t\t1.023940 | \n\t\t\t\t\t\t1482.92 | \n\t\t\t\t\t\t444.11 | \n\t\t\t\t\t\t-3.6667 | \n\t\t\t\t\t\t302.65 | \n\t\t\t\t\t\t1.042668 | \n\t\t\t\t\t\t1575.65 | \n\t\t\t\t\t\t386.31 | \n\t\t\t\t\t\t-3.9491 | \n\t\t\t\t\t||||||
332.65 | \n\t\t\t\t\t\t1.024100 | \n\t\t\t\t\t\t1483.63 | \n\t\t\t\t\t\t443.62 | \n\t\t\t\t\t\t-3.6668 | \n\t\t\t\t\t\t302.40 | \n\t\t\t\t\t\t1.042820 | \n\t\t\t\t\t\t1576.51 | \n\t\t\t\t\t\t385.83 | \n\t\t\t\t\t\t-3.9537 | \n\t\t\t\t\t||||||
332.40 | \n\t\t\t\t\t\t1.024257 | \n\t\t\t\t\t\t1484.34 | \n\t\t\t\t\t\t443.12 | \n\t\t\t\t\t\t-3.6669 | \n\t\t\t\t\t\t302.15 | \n\t\t\t\t\t\t1.042972 | \n\t\t\t\t\t\t1577.39 | \n\t\t\t\t\t\t385.34 | \n\t\t\t\t\t\t-3.9584 | \n\t\t\t\t\t||||||
332.15 | \n\t\t\t\t\t\t1.024414 | \n\t\t\t\t\t\t1485.06 | \n\t\t\t\t\t\t442.63 | \n\t\t\t\t\t\t-3.6671 | \n\t\t\t\t\t\t301.90 | \n\t\t\t\t\t\t1.043124 | \n\t\t\t\t\t\t1578.23 | \n\t\t\t\t\t\t384.88 | \n\t\t\t\t\t\t-3.9631 | \n\t\t\t\t\t||||||
331.90 | \n\t\t\t\t\t\t1.024574 | \n\t\t\t\t\t\t1485.77 | \n\t\t\t\t\t\t442.13 | \n\t\t\t\t\t\t-3.6673 | \n\t\t\t\t\t\t301.65 | \n\t\t\t\t\t\t1.043277 | \n\t\t\t\t\t\t1579.11 | \n\t\t\t\t\t\t384.39 | \n\t\t\t\t\t\t-3.9678 | \n\t\t\t\t\t||||||
331.65 | \n\t\t\t\t\t\t1.024732 | \n\t\t\t\t\t\t1486.48 | \n\t\t\t\t\t\t441.64 | \n\t\t\t\t\t\t-3.6675 | \n\t\t\t\t\t\t301.40 | \n\t\t\t\t\t\t1.043429 | \n\t\t\t\t\t\t1579.97 | \n\t\t\t\t\t\t383.92 | \n\t\t\t\t\t\t-3.9726 | \n\t\t\t\t\t||||||
331.40 | \n\t\t\t\t\t\t1.024890 | \n\t\t\t\t\t\t1487.19 | \n\t\t\t\t\t\t441.15 | \n\t\t\t\t\t\t-3.6678 | \n\t\t\t\t\t\t301.15 | \n\t\t\t\t\t\t1.043579 | \n\t\t\t\t\t\t1580.82 | \n\t\t\t\t\t\t383.45 | \n\t\t\t\t\t\t-3.9774 | \n\t\t\t\t\t||||||
331.15 | \n\t\t\t\t\t\t1.025050 | \n\t\t\t\t\t\t1487.90 | \n\t\t\t\t\t\t440.66 | \n\t\t\t\t\t\t-3.6681 | \n\t\t\t\t\t\t300.90 | \n\t\t\t\t\t\t1.043732 | \n\t\t\t\t\t\t1581.71 | \n\t\t\t\t\t\t382.96 | \n\t\t\t\t\t\t-3.9822 | \n\t\t\t\t\t||||||
330.90 | \n\t\t\t\t\t\t1.025207 | \n\t\t\t\t\t\t1488.62 | \n\t\t\t\t\t\t440.17 | \n\t\t\t\t\t\t-3.6684 | \n\t\t\t\t\t\t300.65 | \n\t\t\t\t\t\t1.043883 | \n\t\t\t\t\t\t1582.58 | \n\t\t\t\t\t\t382.49 | \n\t\t\t\t\t\t-3.9871 | \n\t\t\t\t\t||||||
330.65 | \n\t\t\t\t\t\t1.025363 | \n\t\t\t\t\t\t1489.35 | \n\t\t\t\t\t\t439.67 | \n\t\t\t\t\t\t-3.6688 | \n\t\t\t\t\t\t300.40 | \n\t\t\t\t\t\t1.044037 | \n\t\t\t\t\t\t1583.48 | \n\t\t\t\t\t\t382.00 | \n\t\t\t\t\t\t-3.9920 | \n\t\t\t\t\t||||||
330.40 | \n\t\t\t\t\t\t1.025523 | \n\t\t\t\t\t\t1490.05 | \n\t\t\t\t\t\t439.19 | \n\t\t\t\t\t\t-3.6692 | \n\t\t\t\t\t\t300.15 | \n\t\t\t\t\t\t1.044188 | \n\t\t\t\t\t\t1584.38 | \n\t\t\t\t\t\t381.51 | \n\t\t\t\t\t\t-3.9970 | \n\t\t\t\t\t||||||
330.15 | \n\t\t\t\t\t\t1.025679 | \n\t\t\t\t\t\t1490.79 | \n\t\t\t\t\t\t438.69 | \n\t\t\t\t\t\t-3.6697 | \n\t\t\t\t\t\t299.90 | \n\t\t\t\t\t\t1.044340 | \n\t\t\t\t\t\t1585.27 | \n\t\t\t\t\t\t381.02 | \n\t\t\t\t\t\t-4.0020 | \n\t\t\t\t\t||||||
329.90 | \n\t\t\t\t\t\t1.025838 | \n\t\t\t\t\t\t1491.51 | \n\t\t\t\t\t\t438.20 | \n\t\t\t\t\t\t-3.6702 | \n\t\t\t\t\t\t299.65 | \n\t\t\t\t\t\t1.044492 | \n\t\t\t\t\t\t1586.16 | \n\t\t\t\t\t\t380.54 | \n\t\t\t\t\t\t-4.0070 | \n\t\t\t\t\t||||||
329.65 | \n\t\t\t\t\t\t1.025997 | \n\t\t\t\t\t\t1492.23 | \n\t\t\t\t\t\t437.71 | \n\t\t\t\t\t\t-3.6707 | \n\t\t\t\t\t\t299.40 | \n\t\t\t\t\t\t1.044644 | \n\t\t\t\t\t\t1587.08 | \n\t\t\t\t\t\t380.04 | \n\t\t\t\t\t\t-4.0121 | \n\t\t\t\t\t||||||
329.15 | \n\t\t\t\t\t\t1.026310 | \n\t\t\t\t\t\t1493.70 | \n\t\t\t\t\t\t436.71 | \n\t\t\t\t\t\t-3.6719 | \n\t\t\t\t\t\t298.90 | \n\t\t\t\t\t\t1.044973 | \n\t\t\t\t\t\t1588.87 | \n\t\t\t\t\t\t379.07 | \n\t\t\t\t\t\t-4.0224 | \n\t\t\t\t\t||||||
328.90 | \n\t\t\t\t\t\t1.026467 | \n\t\t\t\t\t\t1494.41 | \n\t\t\t\t\t\t436.23 | \n\t\t\t\t\t\t-3.6726 | \n\t\t\t\t\t\t298.65 | \n\t\t\t\t\t\t1.045148 | \n\t\t\t\t\t\t1589.78 | \n\t\t\t\t\t\t378.57 | \n\t\t\t\t\t\t-4.0275 | \n\t\t\t\t\t||||||
328.65 | \n\t\t\t\t\t\t1.026627 | \n\t\t\t\t\t\t1495.14 | \n\t\t\t\t\t\t435.74 | \n\t\t\t\t\t\t-3.6732 | \n\t\t\t\t\t\t298.40 | \n\t\t\t\t\t\t1.045311 | \n\t\t\t\t\t\t1590.70 | \n\t\t\t\t\t\t378.07 | \n\t\t\t\t\t\t-4.0328 | \n\t\t\t\t\t||||||
327.90 | \n\t\t\t\t\t\t1.027097 | \n\t\t\t\t\t\t1497.32 | \n\t\t\t\t\t\t434.27 | \n\t\t\t\t\t\t-3.6755 | \n\t\t\t\t\t\t297.65 | \n\t\t\t\t\t\t1.045807 | \n\t\t\t\t\t\t1593.44 | \n\t\t\t\t\t\t376.60 | \n\t\t\t\t\t\t-4.0487 | \n\t\t\t\t\t||||||
327.65 | \n\t\t\t\t\t\t1.027255 | \n\t\t\t\t\t\t1498.06 | \n\t\t\t\t\t\t433.77 | \n\t\t\t\t\t\t-3.6764 | \n\t\t\t\t\t\t297.40 | \n\t\t\t\t\t\t1.045975 | \n\t\t\t\t\t\t1594.39 | \n\t\t\t\t\t\t376.09 | \n\t\t\t\t\t\t-4.0540 | \n\t\t\t\t\t||||||
327.40 | \n\t\t\t\t\t\t1.027411 | \n\t\t\t\t\t\t1498.78 | \n\t\t\t\t\t\t433.29 | \n\t\t\t\t\t\t-3.6772 | \n\t\t\t\t\t\t297.15 | \n\t\t\t\t\t\t1.046142 | \n\t\t\t\t\t\t1595.32 | \n\t\t\t\t\t\t375.59 | \n\t\t\t\t\t\t-4.0594 | \n\t\t\t\t\t||||||
327.15 | \n\t\t\t\t\t\t1.027568 | \n\t\t\t\t\t\t1499.51 | \n\t\t\t\t\t\t432.80 | \n\t\t\t\t\t\t-3.6781 | \n\t\t\t\t\t\t296.90 | \n\t\t\t\t\t\t1.046304 | \n\t\t\t\t\t\t1596.24 | \n\t\t\t\t\t\t375.10 | \n\t\t\t\t\t\t-4.0649 | \n\t\t\t\t\t||||||
326.90 | \n\t\t\t\t\t\t1.027725 | \n\t\t\t\t\t\t1500.24 | \n\t\t\t\t\t\t432.32 | \n\t\t\t\t\t\t-3.6791 | \n\t\t\t\t\t\t296.65 | \n\t\t\t\t\t\t1.046470 | \n\t\t\t\t\t\t1597.18 | \n\t\t\t\t\t\t374.60 | \n\t\t\t\t\t\t-4.0704 | \n\t\t\t\t\t||||||
326.65 | \n\t\t\t\t\t\t1.027883 | \n\t\t\t\t\t\t1500.98 | \n\t\t\t\t\t\t431.82 | \n\t\t\t\t\t\t-3.6801 | \n\t\t\t\t\t\t296.40 | \n\t\t\t\t\t\t1.046642 | \n\t\t\t\t\t\t1598.12 | \n\t\t\t\t\t\t374.10 | \n\t\t\t\t\t\t-4.0759 | \n\t\t\t\t\t||||||
326.40 | \n\t\t\t\t\t\t1.028039 | \n\t\t\t\t\t\t1501.70 | \n\t\t\t\t\t\t431.34 | \n\t\t\t\t\t\t-3.6811 | \n\t\t\t\t\t\t296.15 | \n\t\t\t\t\t\t1.046804 | \n\t\t\t\t\t\t1599.08 | \n\t\t\t\t\t\t373.59 | \n\t\t\t\t\t\t-4.0814 | \n\t\t\t\t\t||||||
326.15 | \n\t\t\t\t\t\t1.028194 | \n\t\t\t\t\t\t1502.44 | \n\t\t\t\t\t\t430.85 | \n\t\t\t\t\t\t-3.6821 | \n\t\t\t\t\t\t295.90 | \n\t\t\t\t\t\t1.046975 | \n\t\t\t\t\t\t1600.00 | \n\t\t\t\t\t\t373.10 | \n\t\t\t\t\t\t-4.0870 | \n\t\t\t\t\t||||||
325.90 | \n\t\t\t\t\t\t1.028352 | \n\t\t\t\t\t\t1503.16 | \n\t\t\t\t\t\t430.38 | \n\t\t\t\t\t\t-3.6832 | \n\t\t\t\t\t\t295.65 | \n\t\t\t\t\t\t1.047135 | \n\t\t\t\t\t\t1600.95 | \n\t\t\t\t\t\t372.60 | \n\t\t\t\t\t\t-4.0927 | \n\t\t\t\t\t||||||
325.65 | \n\t\t\t\t\t\t1.028508 | \n\t\t\t\t\t\t1503.88 | \n\t\t\t\t\t\t429.90 | \n\t\t\t\t\t\t-3.6844 | \n\t\t\t\t\t\t295.40 | \n\t\t\t\t\t\t1.047303 | \n\t\t\t\t\t\t1601.93 | \n\t\t\t\t\t\t372.08 | \n\t\t\t\t\t\t-4.0983 | \n\t\t\t\t\t||||||
325.40 | \n\t\t\t\t\t\t1.028665 | \n\t\t\t\t\t\t1504.64 | \n\t\t\t\t\t\t429.40 | \n\t\t\t\t\t\t-3.6855 | \n\t\t\t\t\t\t295.15 | \n\t\t\t\t\t\t1.047465 | \n\t\t\t\t\t\t1602.89 | \n\t\t\t\t\t\t371.58 | \n\t\t\t\t\t\t-4.1041 | \n\t\t\t\t\t||||||
325.15 | \n\t\t\t\t\t\t1.028822 | \n\t\t\t\t\t\t1505.36 | \n\t\t\t\t\t\t428.92 | \n\t\t\t\t\t\t-3.6868 | \n\t\t\t\t\t\t294.90 | \n\t\t\t\t\t\t1.047628 | \n\t\t\t\t\t\t1603.86 | \n\t\t\t\t\t\t371.07 | \n\t\t\t\t\t\t-4.1098 | \n\t\t\t\t\t||||||
324.90 | \n\t\t\t\t\t\t1.028976 | \n\t\t\t\t\t\t1506.11 | \n\t\t\t\t\t\t428.43 | \n\t\t\t\t\t\t-3.6880 | \n\t\t\t\t\t\t294.65 | \n\t\t\t\t\t\t1.047795 | \n\t\t\t\t\t\t1604.81 | \n\t\t\t\t\t\t370.58 | \n\t\t\t\t\t\t-4.1156 | \n\t\t\t\t\t||||||
324.65 | \n\t\t\t\t\t\t1.029135 | \n\t\t\t\t\t\t1506.84 | \n\t\t\t\t\t\t427.95 | \n\t\t\t\t\t\t-3.6893 | \n\t\t\t\t\t\t294.40 | \n\t\t\t\t\t\t1.047960 | \n\t\t\t\t\t\t1605.78 | \n\t\t\t\t\t\t370.07 | \n\t\t\t\t\t\t-4.1214 | \n\t\t\t\t\t||||||
324.40 | \n\t\t\t\t\t\t1.029289 | \n\t\t\t\t\t\t1507.58 | \n\t\t\t\t\t\t427.47 | \n\t\t\t\t\t\t-3.6906 | \n\t\t\t\t\t\t294.15 | \n\t\t\t\t\t\t1.048125 | \n\t\t\t\t\t\t1606.77 | \n\t\t\t\t\t\t369.56 | \n\t\t\t\t\t\t-4.1273 | \n\t\t\t\t\t||||||
324.15 | \n\t\t\t\t\t\t1.029445 | \n\t\t\t\t\t\t1508.32 | \n\t\t\t\t\t\t426.98 | \n\t\t\t\t\t\t-3.6920 | \n\t\t\t\t\t\t293.90 | \n\t\t\t\t\t\t1.048288 | \n\t\t\t\t\t\t1607.74 | \n\t\t\t\t\t\t369.05 | \n\t\t\t\t\t\t-4.1332 | \n\t\t\t\t\t||||||
323.90 | \n\t\t\t\t\t\t1.029602 | \n\t\t\t\t\t\t1509.05 | \n\t\t\t\t\t\t426.50 | \n\t\t\t\t\t\t-3.6934 | \n\t\t\t\t\t\t293.65 | \n\t\t\t\t\t\t1.048451 | \n\t\t\t\t\t\t1608.73 | \n\t\t\t\t\t\t368.54 | \n\t\t\t\t\t\t-4.1391 | \n\t\t\t\t\t||||||
323.65 | \n\t\t\t\t\t\t1.029757 | \n\t\t\t\t\t\t1509.79 | \n\t\t\t\t\t\t426.02 | \n\t\t\t\t\t\t-3.6948 | \n\t\t\t\t\t\t293.40 | \n\t\t\t\t\t\t1.048614 | \n\t\t\t\t\t\t1609.75 | \n\t\t\t\t\t\t368.02 | \n\t\t\t\t\t\t-4.1451 | \n\t\t\t\t\t||||||
323.15 | \n\t\t\t\t\t\t1.030071 | \n\t\t\t\t\t\t1511.28 | \n\t\t\t\t\t\t425.05 | \n\t\t\t\t\t\t-3.6978 | \n\t\t\t\t\t\t292.90 | \n\t\t\t\t\t\t1.048944 | \n\t\t\t\t\t\t1611.75 | \n\t\t\t\t\t\t366.99 | \n\t\t\t\t\t\t-4.1571 | \n\t\t\t\t\t||||||
322.90 | \n\t\t\t\t\t\t1.030226 | \n\t\t\t\t\t\t1512.02 | \n\t\t\t\t\t\t424.57 | \n\t\t\t\t\t\t-3.6993 | \n\t\t\t\t\t\t292.65 | \n\t\t\t\t\t\t1.049105 | \n\t\t\t\t\t\t1612.77 | \n\t\t\t\t\t\t366.47 | \n\t\t\t\t\t\t-4.1632 | \n\t\t\t\t\t||||||
322.65 | \n\t\t\t\t\t\t1.030381 | \n\t\t\t\t\t\t1512.75 | \n\t\t\t\t\t\t424.10 | \n\t\t\t\t\t\t-3.7009 | \n\t\t\t\t\t\t292.40 | \n\t\t\t\t\t\t1.049271 | \n\t\t\t\t\t\t1613.76 | \n\t\t\t\t\t\t365.96 | \n\t\t\t\t\t\t-4.1693 | \n\t\t\t\t\t||||||
322.40 | \n\t\t\t\t\t\t1.030537 | \n\t\t\t\t\t\t1513.50 | \n\t\t\t\t\t\t423.62 | \n\t\t\t\t\t\t-3.7025 | \n\t\t\t\t\t\t292.15 | \n\t\t\t\t\t\t1.049433 | \n\t\t\t\t\t\t1614.77 | \n\t\t\t\t\t\t365.45 | \n\t\t\t\t\t\t-4.1755 | \n\t\t\t\t\t||||||
322.15 | \n\t\t\t\t\t\t1.030693 | \n\t\t\t\t\t\t1514.23 | \n\t\t\t\t\t\t423.14 | \n\t\t\t\t\t\t-3.7042 | \n\t\t\t\t\t\t291.90 | \n\t\t\t\t\t\t1.049593 | \n\t\t\t\t\t\t1615.76 | \n\t\t\t\t\t\t364.94 | \n\t\t\t\t\t\t-4.1817 | \n\t\t\t\t\t||||||
321.90 | \n\t\t\t\t\t\t1.030846 | \n\t\t\t\t\t\t1514.98 | \n\t\t\t\t\t\t422.66 | \n\t\t\t\t\t\t-3.7059 | \n\t\t\t\t\t\t291.65 | \n\t\t\t\t\t\t1.049759 | \n\t\t\t\t\t\t1616.79 | \n\t\t\t\t\t\t364.42 | \n\t\t\t\t\t\t-4.1879 | \n\t\t\t\t\t||||||
321.65 | \n\t\t\t\t\t\t1.031002 | \n\t\t\t\t\t\t1515.72 | \n\t\t\t\t\t\t422.18 | \n\t\t\t\t\t\t-3.7076 | \n\t\t\t\t\t\t291.40 | \n\t\t\t\t\t\t1.049921 | \n\t\t\t\t\t\t1617.83 | \n\t\t\t\t\t\t363.90 | \n\t\t\t\t\t\t-4.1942 | \n\t\t\t\t\t||||||
321.40 | \n\t\t\t\t\t\t1.031159 | \n\t\t\t\t\t\t1516.46 | \n\t\t\t\t\t\t421.71 | \n\t\t\t\t\t\t-3.7094 | \n\t\t\t\t\t\t291.15 | \n\t\t\t\t\t\t1.050082 | \n\t\t\t\t\t\t1618.87 | \n\t\t\t\t\t\t363.37 | \n\t\t\t\t\t\t-4.2005 | \n\t\t\t\t\t||||||
321.15 | \n\t\t\t\t\t\t1.031314 | \n\t\t\t\t\t\t1517.21 | \n\t\t\t\t\t\t421.23 | \n\t\t\t\t\t\t-3.7112 | \n\t\t\t\t\t\t290.90 | \n\t\t\t\t\t\t1.050244 | \n\t\t\t\t\t\t1619.95 | \n\t\t\t\t\t\t362.83 | \n\t\t\t\t\t\t-4.2068 | \n\t\t\t\t\t||||||
320.90 | \n\t\t\t\t\t\t1.031468 | \n\t\t\t\t\t\t1517.96 | \n\t\t\t\t\t\t420.75 | \n\t\t\t\t\t\t-3.7130 | \n\t\t\t\t\t\t290.65 | \n\t\t\t\t\t\t1.050407 | \n\t\t\t\t\t\t1620.99 | \n\t\t\t\t\t\t362.31 | \n\t\t\t\t\t\t-4.2132 | \n\t\t\t\t\t||||||
320.65 | \n\t\t\t\t\t\t1.031625 | \n\t\t\t\t\t\t1518.71 | \n\t\t\t\t\t\t420.27 | \n\t\t\t\t\t\t-3.7149 | \n\t\t\t\t\t\t290.40 | \n\t\t\t\t\t\t1.050566 | \n\t\t\t\t\t\t1622.02 | \n\t\t\t\t\t\t361.80 | \n\t\t\t\t\t\t-4.2196 | \n\t\t\t\t\t||||||
320.40 | \n\t\t\t\t\t\t1.031780 | \n\t\t\t\t\t\t1519.46 | \n\t\t\t\t\t\t419.79 | \n\t\t\t\t\t\t-3.7168 | \n\t\t\t\t\t\t290.15 | \n\t\t\t\t\t\t1.050730 | \n\t\t\t\t\t\t1623.16 | \n\t\t\t\t\t\t361.23 | \n\t\t\t\t\t\t-4.2261 | \n\t\t\t\t\t||||||
320.15 | \n\t\t\t\t\t\t1.031934 | \n\t\t\t\t\t\t1520.22 | \n\t\t\t\t\t\t419.31 | \n\t\t\t\t\t\t-3.7188 | \n\t\t\t\t\t\t289.90 | \n\t\t\t\t\t\t1.050889 | \n\t\t\t\t\t\t1624.19 | \n\t\t\t\t\t\t360.72 | \n\t\t\t\t\t\t-4.2326 | \n\t\t\t\t\t||||||
319.90 | \n\t\t\t\t\t\t1.032088 | \n\t\t\t\t\t\t1520.97 | \n\t\t\t\t\t\t418.83 | \n\t\t\t\t\t\t-3.7208 | \n\t\t\t\t\t\t289.65 | \n\t\t\t\t\t\t1.051050 | \n\t\t\t\t\t\t1625.29 | \n\t\t\t\t\t\t360.18 | \n\t\t\t\t\t\t-4.2391 | \n\t\t\t\t\t||||||
319.65 | \n\t\t\t\t\t\t1.032243 | \n\t\t\t\t\t\t1521.73 | \n\t\t\t\t\t\t418.35 | \n\t\t\t\t\t\t-3.7228 | \n\t\t\t\t\t\t289.40 | \n\t\t\t\t\t\t1.051211 | \n\t\t\t\t\t\t1626.38 | \n\t\t\t\t\t\t359.64 | \n\t\t\t\t\t\t-4.2457 | \n\t\t\t\t\t||||||
319.40 | \n\t\t\t\t\t\t1.032399 | \n\t\t\t\t\t\t1522.49 | \n\t\t\t\t\t\t417.87 | \n\t\t\t\t\t\t-3.7249 | \n\t\t\t\t\t\t289.15 | \n\t\t\t\t\t\t1.051372 | \n\t\t\t\t\t\t1627.47 | \n\t\t\t\t\t\t359.10 | \n\t\t\t\t\t\t-4.2523 | \n\t\t\t\t\t||||||
319.15 | \n\t\t\t\t\t\t1.032553 | \n\t\t\t\t\t\t1523.24 | \n\t\t\t\t\t\t417.40 | \n\t\t\t\t\t\t-3.7270 | \n\t\t\t\t\t\t288.90 | \n\t\t\t\t\t\t1.051531 | \n\t\t\t\t\t\t1628.60 | \n\t\t\t\t\t\t358.55 | \n\t\t\t\t\t\t-4.2590 | \n\t\t\t\t\t||||||
318.90 | \n\t\t\t\t\t\t1.032709 | \n\t\t\t\t\t\t1524.00 | \n\t\t\t\t\t\t416.92 | \n\t\t\t\t\t\t-3.7292 | \n\t\t\t\t\t\t288.65 | \n\t\t\t\t\t\t1.051691 | \n\t\t\t\t\t\t1629.70 | \n\t\t\t\t\t\t358.01 | \n\t\t\t\t\t\t-4.2656 | \n\t\t\t\t\t||||||
318.65 | \n\t\t\t\t\t\t1.032862 | \n\t\t\t\t\t\t1524.77 | \n\t\t\t\t\t\t416.44 | \n\t\t\t\t\t\t-3.7313 | \n\t\t\t\t\t\t288.40 | \n\t\t\t\t\t\t1.051853 | \n\t\t\t\t\t\t1630.82 | \n\t\t\t\t\t\t357.46 | \n\t\t\t\t\t\t-4.2724 | \n\t\t\t\t\t||||||
318.40 | \n\t\t\t\t\t\t1.033016 | \n\t\t\t\t\t\t1525.53 | \n\t\t\t\t\t\t415.96 | \n\t\t\t\t\t\t-3.7336 | \n\t\t\t\t\t\t288.15 | \n\t\t\t\t\t\t1.052010 | \n\t\t\t\t\t\t1631.92 | \n\t\t\t\t\t\t356.93 | \n\t\t\t\t\t\t-4.2791 | \n\t\t\t\t\t||||||
318.15 | \n\t\t\t\t\t\t1.033171 | \n\t\t\t\t\t\t1526.28 | \n\t\t\t\t\t\t415.49 | \n\t\t\t\t\t\t-3.7358 | \n\t\t\t\t\t\t287.90 | \n\t\t\t\t\t\t1.052170 | \n\t\t\t\t\t\t1633.05 | \n\t\t\t\t\t\t356.38 | \n\t\t\t\t\t\t-4.2859 | \n\t\t\t\t\t||||||
317.90 | \n\t\t\t\t\t\t1.033327 | \n\t\t\t\t\t\t1527.05 | \n\t\t\t\t\t\t415.01 | \n\t\t\t\t\t\t-3.7381 | \n\t\t\t\t\t\t287.65 | \n\t\t\t\t\t\t1.052330 | \n\t\t\t\t\t\t1634.18 | \n\t\t\t\t\t\t355.83 | \n\t\t\t\t\t\t-4.2928 | \n\t\t\t\t\t||||||
317.40 | \n\t\t\t\t\t\t1.033635 | \n\t\t\t\t\t\t1528.57 | \n\t\t\t\t\t\t414.06 | \n\t\t\t\t\t\t-3.7428 | \n\t\t\t\t\t\t287.15 | \n\t\t\t\t\t\t1.052647 | \n\t\t\t\t\t\t1636.52 | \n\t\t\t\t\t\t354.71 | \n\t\t\t\t\t\t-4.3065 | \n\t\t\t\t\t||||||
317.15 | \n\t\t\t\t\t\t1.033790 | \n\t\t\t\t\t\t1529.33 | \n\t\t\t\t\t\t413.59 | \n\t\t\t\t\t\t-3.7452 | \n\t\t\t\t\t\t286.90 | \n\t\t\t\t\t\t1.052803 | \n\t\t\t\t\t\t1637.66 | \n\t\t\t\t\t\t354.16 | \n\t\t\t\t\t\t-4.3135 | \n\t\t\t\t\t||||||
316.65 | \n\t\t\t\t\t\t1.034098 | \n\t\t\t\t\t\t1530.86 | \n\t\t\t\t\t\t412.64 | \n\t\t\t\t\t\t-3.7502 | \n\t\t\t\t\t\t286.40 | \n\t\t\t\t\t\t1.053121 | \n\t\t\t\t\t\t1639.97 | \n\t\t\t\t\t\t353.06 | \n\t\t\t\t\t\t-4.3275 | \n\t\t\t\t\t||||||
316.40 | \n\t\t\t\t\t\t1.034253 | \n\t\t\t\t\t\t1531.63 | \n\t\t\t\t\t\t412.16 | \n\t\t\t\t\t\t-3.7527 | \n\t\t\t\t\t\t286.15 | \n\t\t\t\t\t\t1.053282 | \n\t\t\t\t\t\t1641.17 | \n\t\t\t\t\t\t352.49 | \n\t\t\t\t\t\t-4.3345 | \n\t\t\t\t\t||||||
316.15 | \n\t\t\t\t\t\t1.034406 | \n\t\t\t\t\t\t1532.39 | \n\t\t\t\t\t\t411.69 | \n\t\t\t\t\t\t-3.7552 | \n\t\t\t\t\t\t285.90 | \n\t\t\t\t\t\t1.053440 | \n\t\t\t\t\t\t1642.36 | \n\t\t\t\t\t\t351.93 | \n\t\t\t\t\t\t-4.3416 | \n\t\t\t\t\t||||||
315.90 | \n\t\t\t\t\t\t1.034559 | \n\t\t\t\t\t\t1533.16 | \n\t\t\t\t\t\t411.22 | \n\t\t\t\t\t\t-3.7578 | \n\t\t\t\t\t\t285.65 | \n\t\t\t\t\t\t1.053595 | \n\t\t\t\t\t\t1643.59 | \n\t\t\t\t\t\t351.35 | \n\t\t\t\t\t\t-4.3488 | \n\t\t\t\t\t||||||
315.40 | \n\t\t\t\t\t\t1.034867 | \n\t\t\t\t\t\t1534.71 | \n\t\t\t\t\t\t410.26 | \n\t\t\t\t\t\t-3.7631 | \n\t\t\t\t\t\t285.15 | \n\t\t\t\t\t\t1.053914 | \n\t\t\t\t\t\t1645.91 | \n\t\t\t\t\t\t350.25 | \n\t\t\t\t\t\t-4.3632 | \n\t\t\t\t\t||||||
315.15 | \n\t\t\t\t\t\t1.035022 | \n\t\t\t\t\t\t1535.47 | \n\t\t\t\t\t\t409.80 | \n\t\t\t\t\t\t-3.7659 | \n\t\t\t\t\t\t284.90 | \n\t\t\t\t\t\t1.054069 | \n\t\t\t\t\t\t1647.20 | \n\t\t\t\t\t\t349.65 | \n\t\t\t\t\t\t-4.3704 | \n\t\t\t\t\t||||||
314.90 | \n\t\t\t\t\t\t1.035175 | \n\t\t\t\t\t\t1536.22 | \n\t\t\t\t\t\t409.34 | \n\t\t\t\t\t\t-3.7686 | \n\t\t\t\t\t\t284.65 | \n\t\t\t\t\t\t1.054227 | \n\t\t\t\t\t\t1648.38 | \n\t\t\t\t\t\t349.10 | \n\t\t\t\t\t\t-4.3777 | \n\t\t\t\t\t||||||
314.65 | \n\t\t\t\t\t\t1.035330 | \n\t\t\t\t\t\t1536.99 | \n\t\t\t\t\t\t408.86 | \n\t\t\t\t\t\t-3.7714 | \n\t\t\t\t\t\t284.40 | \n\t\t\t\t\t\t1.054384 | \n\t\t\t\t\t\t1649.68 | \n\t\t\t\t\t\t348.50 | \n\t\t\t\t\t\t-4.3850 | \n\t\t\t\t\t||||||
314.40 | \n\t\t\t\t\t\t1.035483 | \n\t\t\t\t\t\t1537.77 | \n\t\t\t\t\t\t408.39 | \n\t\t\t\t\t\t-3.7742 | \n\t\t\t\t\t\t284.15 | \n\t\t\t\t\t\t1.054542 | \n\t\t\t\t\t\t1650.96 | \n\t\t\t\t\t\t347.91 | \n\t\t\t\t\t\t-4.3924 | \n\t\t\t\t\t||||||
314.15 | \n\t\t\t\t\t\t1.035638 | \n\t\t\t\t\t\t1538.53 | \n\t\t\t\t\t\t407.92 | \n\t\t\t\t\t\t-3.7771 | \n\t\t\t\t\t\t283.90 | \n\t\t\t\t\t\t1.054697 | \n\t\t\t\t\t\t1652.23 | \n\t\t\t\t\t\t347.32 | \n\t\t\t\t\t\t-4.3998 | \n\t\t\t\t\t||||||
313.90 | \n\t\t\t\t\t\t1.035792 | \n\t\t\t\t\t\t1539.30 | \n\t\t\t\t\t\t407.46 | \n\t\t\t\t\t\t-3.7800 | \n\t\t\t\t\t\t283.65 | \n\t\t\t\t\t\t1.054853 | \n\t\t\t\t\t\t1653.49 | \n\t\t\t\t\t\t346.74 | \n\t\t\t\t\t\t-4.4072 | \n\t\t\t\t\t||||||
313.65 | \n\t\t\t\t\t\t1.035945 | \n\t\t\t\t\t\t1540.06 | \n\t\t\t\t\t\t406.99 | \n\t\t\t\t\t\t-3.7829 | \n\t\t\t\t\t\t283.40 | \n\t\t\t\t\t\t1.055012 | \n\t\t\t\t\t\t1654.78 | \n\t\t\t\t\t\t346.15 | \n\t\t\t\t\t\t-4.4147 | \n\t\t\t\t\t||||||
313.40 | \n\t\t\t\t\t\t1.036100 | \n\t\t\t\t\t\t1540.83 | \n\t\t\t\t\t\t406.53 | \n\t\t\t\t\t\t-3.7859 | \n\t\t\t\t\t\t283.15 | \n\t\t\t\t\t\t1.055166 | \n\t\t\t\t\t\t1656.17 | \n\t\t\t\t\t\t345.52 | \n\t\t\t\t\t\t-4.4222 | \n\t\t\t\t\t||||||
313.15 | \n\t\t\t\t\t\t1.036252 | \n\t\t\t\t\t\t1541.60 | \n\t\t\t\t\t\t406.06 | \n\t\t\t\t\t\t-3.7889 | \n\t\t\t\t\t\t282.90 | \n\t\t\t\t\t\t1.055325 | \n\t\t\t\t\t\t1657.46 | \n\t\t\t\t\t\t344.93 | \n\t\t\t\t\t\t-4.4297 | \n\t\t\t\t\t||||||
312.90 | \n\t\t\t\t\t\t1.036406 | \n\t\t\t\t\t\t1542.37 | \n\t\t\t\t\t\t405.60 | \n\t\t\t\t\t\t-3.7919 | \n\t\t\t\t\t\t282.65 | \n\t\t\t\t\t\t1.055479 | \n\t\t\t\t\t\t1658.73 | \n\t\t\t\t\t\t344.35 | \n\t\t\t\t\t\t-4.4373 | \n\t\t\t\t\t||||||
312.65 | \n\t\t\t\t\t\t1.036558 | \n\t\t\t\t\t\t1543.14 | \n\t\t\t\t\t\t405.13 | \n\t\t\t\t\t\t-3.7950 | \n\t\t\t\t\t\t282.40 | \n\t\t\t\t\t\t1.055637 | \n\t\t\t\t\t\t1660.17 | \n\t\t\t\t\t\t343.70 | \n\t\t\t\t\t\t-4.4449 | \n\t\t\t\t\t||||||
312.40 | \n\t\t\t\t\t\t1.036711 | \n\t\t\t\t\t\t1543.91 | \n\t\t\t\t\t\t404.67 | \n\t\t\t\t\t\t-3.7981 | \n\t\t\t\t\t\t282.15 | \n\t\t\t\t\t\t1.055795 | \n\t\t\t\t\t\t1661.49 | \n\t\t\t\t\t\t343.10 | \n\t\t\t\t\t\t-4.4526 | \n\t\t\t\t\t||||||
312.15 | \n\t\t\t\t\t\t1.036865 | \n\t\t\t\t\t\t1544.69 | \n\t\t\t\t\t\t404.20 | \n\t\t\t\t\t\t-3.8013 | \n\t\t\t\t\t\t281.90 | \n\t\t\t\t\t\t1.055948 | \n\t\t\t\t\t\t1662.83 | \n\t\t\t\t\t\t342.50 | \n\t\t\t\t\t\t-4.4603 | \n\t\t\t\t\t||||||
311.90 | \n\t\t\t\t\t\t1.037019 | \n\t\t\t\t\t\t1545.47 | \n\t\t\t\t\t\t403.73 | \n\t\t\t\t\t\t-3.8045 | \n\t\t\t\t\t\t281.65 | \n\t\t\t\t\t\t1.056104 | \n\t\t\t\t\t\t1664.24 | \n\t\t\t\t\t\t341.87 | \n\t\t\t\t\t\t-4.4680 | \n\t\t\t\t\t||||||
311.65 | \n\t\t\t\t\t\t1.037171 | \n\t\t\t\t\t\t1546.25 | \n\t\t\t\t\t\t403.26 | \n\t\t\t\t\t\t-3.8077 | \n\t\t\t\t\t\t281.40 | \n\t\t\t\t\t\t1.056260 | \n\t\t\t\t\t\t1665.61 | \n\t\t\t\t\t\t341.26 | \n\t\t\t\t\t\t-4.4758 | \n\t\t\t\t\t||||||
311.40 | \n\t\t\t\t\t\t1.037325 | \n\t\t\t\t\t\t1547.02 | \n\t\t\t\t\t\t402.80 | \n\t\t\t\t\t\t-3.8110 | \n\t\t\t\t\t\t281.15 | \n\t\t\t\t\t\t1.056416 | \n\t\t\t\t\t\t1667.01 | \n\t\t\t\t\t\t340.63 | \n\t\t\t\t\t\t-4.4836 | \n\t\t\t\t\t||||||
311.15 | \n\t\t\t\t\t\t1.037479 | \n\t\t\t\t\t\t1547.82 | \n\t\t\t\t\t\t402.33 | \n\t\t\t\t\t\t-3.8143 | \n\t\t\t\t\t\t280.90 | \n\t\t\t\t\t\t1.056572 | \n\t\t\t\t\t\t1668.41 | \n\t\t\t\t\t\t340.01 | \n\t\t\t\t\t\t-4.4914 | \n\t\t\t\t\t||||||
310.65 | \n\t\t\t\t\t\t1.037785 | \n\t\t\t\t\t\t1549.39 | \n\t\t\t\t\t\t401.39 | \n\t\t\t\t\t\t-3.8210 | \n\t\t\t\t\t\t280.40 | \n\t\t\t\t\t\t1.056883 | \n\t\t\t\t\t\t1671.29 | \n\t\t\t\t\t\t338.74 | \n\t\t\t\t\t\t-4.5072 | \n\t\t\t\t\t||||||
310.40 | \n\t\t\t\t\t\t1.037938 | \n\t\t\t\t\t\t1550.17 | \n\t\t\t\t\t\t400.93 | \n\t\t\t\t\t\t-3.8244 | \n\t\t\t\t\t\t280.15 | \n\t\t\t\t\t\t1.057038 | \n\t\t\t\t\t\t1672.76 | \n\t\t\t\t\t\t338.10 | \n\t\t\t\t\t\t-4.5152 | \n\t\t\t\t\t||||||
310.15 | \n\t\t\t\t\t\t1.038089 | \n\t\t\t\t\t\t1550.96 | \n\t\t\t\t\t\t400.46 | \n\t\t\t\t\t\t-3.8279 | \n\t\t\t\t\t\t279.90 | \n\t\t\t\t\t\t1.057192 | \n\t\t\t\t\t\t1674.21 | \n\t\t\t\t\t\t337.46 | \n\t\t\t\t\t\t-4.5232 | \n\t\t\t\t\t||||||
309.90 | \n\t\t\t\t\t\t1.038244 | \n\t\t\t\t\t\t1551.75 | \n\t\t\t\t\t\t400.00 | \n\t\t\t\t\t\t-3.8314 | \n\t\t\t\t\t\t279.65 | \n\t\t\t\t\t\t1.057349 | \n\t\t\t\t\t\t1675.59 | \n\t\t\t\t\t\t336.86 | \n\t\t\t\t\t\t-4.5312 | \n\t\t\t\t\t||||||
309.65 | \n\t\t\t\t\t\t1.038396 | \n\t\t\t\t\t\t1552.56 | \n\t\t\t\t\t\t399.52 | \n\t\t\t\t\t\t-3.8349 | \n\t\t\t\t\t\t279.40 | \n\t\t\t\t\t\t1.057504 | \n\t\t\t\t\t\t1677.18 | \n\t\t\t\t\t\t336.17 | \n\t\t\t\t\t\t-4.5393 | \n\t\t\t\t\t||||||
309.40 | \n\t\t\t\t\t\t1.038550 | \n\t\t\t\t\t\t1553.36 | \n\t\t\t\t\t\t399.05 | \n\t\t\t\t\t\t-3.8385 | \n\t\t\t\t\t\t279.15 | \n\t\t\t\t\t\t1.057659 | \n\t\t\t\t\t\t1678.69 | \n\t\t\t\t\t\t335.52 | \n\t\t\t\t\t\t-4.5474 | \n\t\t\t\t\t||||||
309.15 | \n\t\t\t\t\t\t1.038704 | \n\t\t\t\t\t\t1554.16 | \n\t\t\t\t\t\t398.58 | \n\t\t\t\t\t\t-3.8421 | \n\t\t\t\t\t\t278.90 | \n\t\t\t\t\t\t1.057816 | \n\t\t\t\t\t\t1680.20 | \n\t\t\t\t\t\t334.86 | \n\t\t\t\t\t\t-4.5556 | \n\t\t\t\t\t||||||
308.90 | \n\t\t\t\t\t\t1.038856 | \n\t\t\t\t\t\t1554.95 | \n\t\t\t\t\t\t398.12 | \n\t\t\t\t\t\t-3.8458 | \n\t\t\t\t\t\t278.65 | \n\t\t\t\t\t\t1.057971 | \n\t\t\t\t\t\t1681.62 | \n\t\t\t\t\t\t334.25 | \n\t\t\t\t\t\t-4.5637 | \n\t\t\t\t\t||||||
308.65 | \n\t\t\t\t\t\t1.039008 | \n\t\t\t\t\t\t1555.77 | \n\t\t\t\t\t\t397.64 | \n\t\t\t\t\t\t-3.8494 | \n\t\t\t\t\t\t278.40 | \n\t\t\t\t\t\t1.058124 | \n\t\t\t\t\t\t1683.11 | \n\t\t\t\t\t\t333.61 | \n\t\t\t\t\t\t-4.5720 | \n\t\t\t\t\t||||||
308.40 | \n\t\t\t\t\t\t1.039161 | \n\t\t\t\t\t\t1556.55 | \n\t\t\t\t\t\t397.18 | \n\t\t\t\t\t\t-3.8532 | \n\t\t\t\t\t\t278.15 | \n\t\t\t\t\t\t1.058279 | \n\t\t\t\t\t\t1684.75 | \n\t\t\t\t\t\t332.91 | \n\t\t\t\t\t\t-4.5802 | \n\t\t\t\t\t||||||
308.15 | \n\t\t\t\t\t\t1.039313 | \n\t\t\t\t\t\t1557.36 | \n\t\t\t\t\t\t396.71 | \n\t\t\t\t\t\t-3.8569 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t |
Densities (ρ), ultrasonic velocity (u), isentropic compressibilities (κS), isobaric expansibilities (α), 278.15-338.15K
The contrary effect is observed for conductivity. At the same temperature, higher viscosity was observed when the salt was of higher molecular weight. The effect of the temperature is similar for all salts.
\n\t\t\t\n\t\t\t\n\t\t\tA frequently applied derived property for industrial mixtures is the isobaric expansibility or thermal expansion coefficient (α), expressed as the temperature dependence of density. Thermal expansion coefficients are calculated by means of \n\t\t\t\t\t
taking into account the temperature dependence of density. The results gathered in Table 2 showed that a minimum of isobaric expansibility is obtained (in terms of negative values) at approximately the same temperature for all ILs. The smaller the size of the cation (monoethylene cation), the lower the value of isobaric expansibility was obtained.
\n\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | |||
278.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 2158.20 | \n\t\t\t\t\t\t83.6 | \n\t\t\t\t\t
288.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 3069.00 | \n\t\t\t\t\t\t143.3 | \n\t\t\t\t\t
298.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 4197.60 | \n\t\t\t\t\t\t239.6 | \n\t\t\t\t\t
308.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 5623.20 | \n\t\t\t\t\t\t453.4 | \n\t\t\t\t\t
318.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 6959.70 | \n\t\t\t\t\t\t632.6 | \n\t\t\t\t\t
328.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 8563.50 | \n\t\t\t\t\t\t910.8 | \n\t\t\t\t\t
338.15 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | 10404.90 | \n\t\t\t\t\t\t1202.9 | \n\t\t\t\t\t
Values of ionic conductivity (µS∙cm-1) of the 2-HEAF and 2-HEAPE in the range 278.15 – 338.15 K
The values of ionic conductivity are gathered in Table 3. These results show an increasing trend for higher temperatures in each case. This fact may be ascribed to the increasing mobility of the ions for increased temperatures. At the same time, the ionic conductivity values decrease when molecular weight increases, thus 2-HEAPE has a lower ionic conductivity than 2-HEAF, the shortest member of this IL family [23].
\n\t\t\tThe factor studied in this work is the chain length of the anion. The influence of anion residue is higher in terms of steric hindrance, due to its longer structure [2, 23]. This factor produces a higher disturbation on ion package. This fact may be observed in terms of higher values of densities and ultrasonic velocities for those salts of the lighter anion [37].
\n\t\t\tThe ILs studied in this work showed interesting properties for industrial use: low cost of preparation, simple synthesis and purification methods. Moreover, the very low toxicity and the degradability have been verified [38]. Thus, sustainable processes can be originated from their use.
\n\t\t\tWith this in mind, we decided to test their catalytic potential for several aldol condensation reactions with interest for fine chemicals synthesis. At industrial level aldol condensations are catalyzed by homogeneous alkaline bases (KOH or NaOH) [39,40] but with this kind of catalysts numerous disadvantages arise such as loss of catalysts due to separation difficulties, corrosion problems in the equipment and generation of large amounts of residual effluents which must be subsequently treated to minimize their environmental impact. Consequently, new technological solutions have to be developed in order to generate new and more environmental friendly processes.
\n\t\t\tThe condensation reaction between citral and acetone leads to the formation of pseudoionones which are precursors in the commercial production of vitamin A. In the last years, the aldol condensation between citral and acetone has been studied by several groups employing different types of catalysts: rehydrated hydrotalcites [41], mixed oxides derived from hydrotalcites [42, 43], organic molecules [44], ionic liquids [28] etc.
\n\t\t\tUsing the mixed oxides derived from hydrotalcites Climent et al. [42, 43] obtained a conversion of 83% and selectivity to pseudoionones of 82% in 1 h. Abello et al. obtained a citral conversion of 81% in only 5 min employing rehydrated hydrotalcites as catalysts [41] highlighting that Brønsted basic sites are more active than Lewis sites for aldol condensation reactions. In the study of Cota et al. [44] it was shown that 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) which has Lewis basic properties, is inactive for aldol condensation reactions; however when it reacts with equimolar amounts of water, this molecule transforms towards a complex that shows Brønsted basic properties and becomes active giving a conversion of 89.17% and a selectivity of 89.6% in 6 h. When choline hydroxide (ionic liquid) was used as catalyst a citral conversion of 93% and selectivity of 98.2% were obtained in 1 h [28].
\n\t\t\tAmong the ILs studied in this work, for citral and acetone condensation (entry 1, Table 4) the most active IL is 2-HEAA, which gives a conversion of 52%, the less active is 2-HEAiB which gives a conversion of 10%. The selectivity obtained in this reaction ranges between 49-83%. No traces of diacetone alcohol derived from the self-condensation of acetone were found but other secondary products coming from the self-condensation of citral and oligomers derived from citral are detected in small quantities in the reaction mixture.
\n\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t
\n\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | \n\t\t\t\t\t\t | (h) | \n\t\t\t\t\t\t(%) | \n\t\t\t\t\t\t(%) | \n\t\t\t\t\t\t\n\t\t\t\t\t
1 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t2-HEAF 2-HEAP 2-HEAA 2-HEAB 2-HEAiB 2-HEAPE | \n\t\t\t\t\t\t 7 | \n\t\t\t\t\t\t35 40 52 33 10 38 | \n\t\t\t\t\t\t83 63 74 60 53 49 | \n\t\t\t\t\t
\n\t\t\t\t\t | |||||||
2 | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t2-HEAF 2-HEAP 2-HEAA 2-HEAB 2-HEAiB 2-HEAPE | \n\t\t\t\t\t\t4 3 4 2 2 2 | \n\t\t\t\t\t\t94 100 99 99 93 98 | \n\t\t\t\t\t\t82 86 85 85 85 77 | \n\t\t\t\t\t
Condensation reactions catalyzed by the studied ILs.
For the production of benzylideneacetone from the aldol condensation between acetone and benzaldehyde, Cota et al. [44] obtained a conversion of 99.9% and 93.97 selectivity in 2 h. When choline hydroxide was employed as catalyst [28] the total conversion was obtained in 0.1 hours but due to the production of dibenzylidenacetone the selectivity to benzylidenacetone decreased around 77%.
\n\t\t\tWhen ILs presented in this study were employed for this reaction (entry 2, Table 4), in 2 h of reaction, a conversion of 99% and a selectivity of 85% are obtained when using 2-HEAB as catalyst. Good conversion was also obtained with 2-HEAiB (93%) and 2-HEAPE (98%) with selectivity of 85% and 77% respectively. The decrease in the selectivity to benzylidenacetone is due to the formation of secondary products which include products of aldolisation of benzylidenacetone, like dibenzylidenacetone and other oligomers. The other studied ILs reached the maximum conversion in 3h (2-HEAP) and 4h (2-HEAF and 2-HEAA) and provided high selectivities between 82-86%.
\n\t\t\tFor the repeated runs experiments, we used 2-HEAB in the condensation reaction between acetone and benzaldehyde. The catalyst was recycled 3 times, and in all runs a very good conversion was obtained. The results are presented in Figure 2.
\n\t\t\tRepeated runs experiments using 2-HEAB in benzylideneacetone synthesis.
The loss of activity noticed in the second and third run can be attributed, on one hand to the loss of IL during the separation process and on the other hand due to the absorption of reaction products on the active sites of the catalyst. IL is partially soluble in the reaction product therefore during the separation procedure small quantities of IL can be dissolved in the organic phase and therefore lost during the separation process. This hypothesis is sustained by the evolution of the specific bands of the ILs which appear in the range 3500-2400 cm-1, almost disappearing in the re-used sample as Figure 3 shows.
\n\t\t\t\n\t\t\tA weak band around 1591 cm-1 is present in the re-used sample accounting for the carbonyl stretching and N-H plane bending vibrations. On the other hand, deactivation of the catalyst, moreover exhibiting a dark yellow color, is probably due to the adsorption of oligomers and other secondary products on the surface of the catalyst during the reaction. This hypothesis is supported by the appearance of new bands in the re-used IL spectrum. The bands detected in the 1700-1200 cm-1 region corresponding to the symmetric and stretching vibrations of CH modes can be assigned to oligomeric species adsorbed on the surface. On the other hand in the 1260-700 cm-1 region bands which are normally weak appear and can be assigned to the C-C skeletal vibrations.
\n\t\t\tFT-IR spectra for (a) 2-HEAB before reaction, (b) 2-HEAB after reaction (3 consecutive runs).
In order to facilitate the recovery and re-use of the ILs we decided to immobilize them on a solid support. Immobilization and supporting of ILs can be achieved by simple impregnation, covalent linking of the cation or the anion, polymerization etc [45-47]. Compared to pure ILs, immobilized ILs facilitate the recovery and re-use of the catalyst. Previous reports describe the immobilization of ILs by adsorption or grafting onto silica surface and their use as catalysts for reactions like Friedel-Crafts acylation [45], hydrogenation [48] and hydroformilation [49]. Organic polymers [30], natural polymers [50] and zeolites [51] have been also used as supports for ILs.
\n\t\t\tFor this purpose, the ILs were supported on alanine, a cheap readily available aminoacid. Their catalytic activity was tested in the same reactions as the pure ILs.
\n\t\t\tThe catalytic activity results of the a-ILs for the citral-acetone condensation are presented in Table 5. After 6 h of reaction, the two isomers of citral can be converted into the corresponding pseudoionone with conversion between 30-56% except for a-HEAiB for which a conversion of 9% was obtained. The most active IL for this reaction is a-2-HEAA which provides a conversion of 56%. The selectivity obtained in this reaction ranges between 48-80%. No traces of diacetone alcohol derived from the self-condensation of acetone were found, but other secondary products coming from the self condensation of citral and oligomers derived from citral are detected in the reaction mixture. The support (entry 1) is not catalytically active.
\n\t\t\t\n\t\t\tIn the condensation reaction of benzaldehyde and acetone the first step is the deprotonation of an acetone molecule to give the enolate anion whose nucleophilic attack on the C=O group of benzaldehyde leads to the β-aldol. This latter is easily dehydrated on weak acid sites and benzylidenacetone is obtained.
\n\t\t\t\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | (%) | \n\t\t\t\t\t\t(%) | \n\t\t\t\t\t
1 | \n\t\t\t\t\t\talanine | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t
2 | \n\t\t\t\t\t\ta-2-HEAF | \n\t\t\t\t\t\t30 | \n\t\t\t\t\t\t61 | \n\t\t\t\t\t
3 | \n\t\t\t\t\t\ta-2-HEAA | \n\t\t\t\t\t\t56 | \n\t\t\t\t\t\t74 | \n\t\t\t\t\t
4 | \n\t\t\t\t\t\ta-2-HEAP | \n\t\t\t\t\t\t49 | \n\t\t\t\t\t\t80 | \n\t\t\t\t\t
5 | \n\t\t\t\t\t\ta-2-HEAB | \n\t\t\t\t\t\t35 | \n\t\t\t\t\t\t63 | \n\t\t\t\t\t
6 | \n\t\t\t\t\t\ta-2-HEAiB | \n\t\t\t\t\t\t9 | \n\t\t\t\t\t\t52 | \n\t\t\t\t\t
7 | \n\t\t\t\t\t\ta-2-HEAPE | \n\t\t\t\t\t\t33 | \n\t\t\t\t\t\t48 | \n\t\t\t\t\t
Conversion at 6 h for citral-acetone condensation catalyzed by a-ILs
\n\t\t\t\t\t\t | \n\t\t\t\t\t\t | (%) | \n\t\t\t\t\t\t(%) | \n\t\t\t\t\t
1 | \n\t\t\t\t\t\talanine | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t
2 | \n\t\t\t\t\t\ta-2-HEAF | \n\t\t\t\t\t\t99 | \n\t\t\t\t\t\t83 | \n\t\t\t\t\t
3 | \n\t\t\t\t\t\ta-2-HEAA | \n\t\t\t\t\t\t99 | \n\t\t\t\t\t\t82 | \n\t\t\t\t\t
4 | \n\t\t\t\t\t\ta-2-HEAP | \n\t\t\t\t\t\t99 | \n\t\t\t\t\t\t85 | \n\t\t\t\t\t
5 | \n\t\t\t\t\t\ta-2-HEAB | \n\t\t\t\t\t\t99 | \n\t\t\t\t\t\t84 | \n\t\t\t\t\t
6 | \n\t\t\t\t\t\ta-2-HEAiB | \n\t\t\t\t\t\t78 | \n\t\t\t\t\t\t82 | \n\t\t\t\t\t
7 | \n\t\t\t\t\t\ta-2-HEAPE | \n\t\t\t\t\t\t98 | \n\t\t\t\t\t\t80 | \n\t\t\t\t\t
Conversion at 2 h for benzaldehyde-acetone condensation catalyzed by a-ILs
In 2 hours of reaction a conversion of 98-99% is achieved for the majority of a-ILs, while a lower conversion (78%) is obtained for a-2-HEAiB (Table 6). The selectivity toward benzylidenacetone is around 80-86% due to the formation of dibenzylidenacetone as secondary product. The support, alanine (entry 1) is not active for citral acetone condensation.
\n\t\t\tIt is noteworthy that, for both studied reactions, the conversions obtained with the a-ILs are in the same range as the ones obtained with free ILs (Figure 4 and 5).
\n\t\t\t\n\t\t\t\n\t\t\tThe a-ILs are easily separated from the reaction mixture and reused. For the consecutive runs experiments we chose condensation between benzaldehyde and acetone as model reaction. The catalysts were recycled for 3 consecutive runs and in all runs a very good conversion was obtained. The results are presented in Figure 6.
\n\t\t\tConversion at 6 h for citral-acetone condensation for free ILs and a-ILs.
Conversion at 2 h for benzaldehyde-acetone condensation for free ILs and a-ILs.
Consecutive runs experiments in benzaldehyde acetone condensation.
In the case of each IL, only a negligible loss of activity is detected in the second and third run which can be attributed to the possible adsorption of reactants or reaction products to the active sites of the catalyst.
\n\t\t\tFrom the comparison made with the aforementioned basic catalysts employed for these two aldol condensation reactions we can conclude that the ILs presented in this study are not the most active catalysts for these reactions but due to their green character and easy separation from the reaction media represent a convenient and environmental friendly alternative for the traditional homogeneous catalysts.
\n\t\t\n\t\tIn this work, we present a simple and efficient synthesis protocol for protic ionic liquids and the experimental data for density, ultrasonic velocity and ionic conductivity of these liquid salts. It was found that increased temperature diminishes the interaction among ions and therefore lower values of density, ultrasonic velocity, viscosity, surface tension and refractive index are obtained for increased temperatures in each case. The contrary effect is observed for conductivity.
\n\t\t\tThe influence of chain length of the anion on the physicochemical properties of the ILs has been also studied. The effect of the anion residue is higher in terms of steric hindrance, due to its longer structure. This factor produces a higher disturbation on ion package. The physicochemical data of ILs are important for both, designing cleaner technological processes and understanding the interactions in this kind of compounds
\n\t\t\tThe catalytic potential of these new ILs was tested for two aldol condensation reactions with interest for fine chemistry industry. Conversions ranging from 35 to 52% and selectivities up to 83% are obtained for the condensation of citral with acetone. In the synthesis of benzilidenacetone, conversions above 93% with selectivities around 85% are obtained. We also studied the optimization of the recovery process of the ILs and their reuse in repeated runs of experiments. The catalysts can be recycled and reused for three consecutive cycles without significant loss of activity.
\n\t\t\tIn addition, in order to improve the recovery process, the ILs were immobilized on alanine, a cheap readily available aminoacid. The catalytic activity of the alanine supported ILs was tested for citral-acetone and benzaldyde-acetone condensations. It is noteworthy that, for both studied reactions, the conversions obtained with the a-ILs are in the same range as the ones obtained with free ILs; moreover the catalysts can be recycled and reused for three consecutive cycles without significant loss of activity.
\n\t\t\tThe ILs studied in this work showed interesting properties for industrial use: low cost of preparation, simple synthesis and purification methods. Moreover, the very low toxicity and the degradability have been verified. Thus, sustainable processes can be originated from their use.
\n\t\tThis work has been financed by the MEC of Spain and the Generalitat of Catalunya (ICREA ACADEMIA AWARD).
\n\t\tRust diseases are the fungal diseases of plants, mainly grasses, caused by fungi. They affect the aerial plant parts especially leaves but can also attack stems and even flowers and fruits. They bear complex life cycles that require two alternative unrelated hosts. Rusts produce spore pustules which vary in color according to the rust species. About 7000 rust species are known to affect a variety of host plants globally. They can cause a wide range of symptoms depending upon the host species like the formation of Galls or swellings on the branches, formation of Canker on the trunks, and formation of Spores on the surface of the leaf. Leaf rust is also known as bown rust due to the brown color of circular urediniospores on the surfaces of the leaf of the crop. Yellow rust or stripe rust is characterized by the yellow color of stripes on the surfaces of the leaf. Stem rust is also brown and characterized by the patches of brown color on the surface of stems. Many approaches are being deployed to combat the problem of these diseases which involves accurate phenotyping which means characterization of the diseases at field level followed by genotyping to find out the genes responsible for its cause. Many germplasm resources are being explored and screened by scientists worldwide to find new sources of resistance. Precision phenotyping is the key requirement to achieve the goals. So far there are manual interventions involved to screen these diseases. But manual scoring of these diseases is a cumbersome job in large pre-breeding and breeding programs. Therefore, there is a strong need for high precision phenomics which involves imaging using high-quality cameras or equipment followed by image analysis using newly developed software and tools. In today’s era of artificial intelligence, it is possible to explore high-end phenomics to achieve better yields of important crops like wheat. Many machine learning and deep learning models have been tested and tried to analyze and characterize wheat fungal diseases [1, 2, 3, 4]. One of the main reasons for the popularity of these techniques is the use of GPUs (graphics processing units). The classification tools, computer vision, and GPUs are combined in a single framework called deep learning [5]. Deep learning-based models have been used in the various applications of agriculture for end-to-end learning. With the use of GPUs, deep learning can give a better solution to the given problem in a shorter time [6]. The process of building such models is computationally challenging but using GPU power becomes very easy [7, 8]. Fungal diseases have been identified using image processing techniques on different horticulture and agriculture crops. Various feature extraction and classification algorithm have been used to detect the different types of fruits, vegetables, and cereal crops.
Among the various rust diseases, soybean-, coffee-, and wheat-rusts are the most damaging diseases. Therefore, the constant efforts are being done worldwide, to combat this problem. Wheat is one of the staple food crops in addition to rice and maize. The total area under wheat in the world is around 220 million hectares with a production of 772.64 million metric tons (2020–2021). Wheat rusts especially leaf rust, stem rust, and yellow rusts are major fungal diseases that affect the production of the wheat crop throughout the world particularly in South Asian countries [9]. As per the prediction of the Food and Agricultural Organization (FAO) of the United Nations, wheat production might not be fulfilled the requirement in near future due to rapid population growth [10, 11]. In this chapter we discuss the usefulness of deep learning-based algorithms to identify rust using wheat as a case study.
Human perception is based on the interaction between the brain and the eye. On the other hand, computer vision system (CVS) is used to emulate human vision for gathering information without physical interaction [12, 13].
It is also defined as the process of automatic acquisition, and analysis from image data. CVS emulates the dynamic vision system whose operation is very transparent and natural. The data is processed in various stages such as capturing, processing, and analysis of images. Figure 1 depicts the steps involved during image processing. In the first stage, image acquisition and pre-processing are involved. The images can be acquired using high-resolution cameras and sensors. Further, the images are pre-processed through data cleaning, background removing, adding/removing noise, and also enhancing the quality of images. In the second stage, the images are segmented. The segmentation process involves extracting only important and useful information from the whole image that further helps in the discrimination of classes. In the third stage, the high-level analysis is performed in which direct emphasis is done on the recognition (objects) and interpretation (making results). In a CVS, the following attributes contribute to decision-making: shape, color, texture, and also size. Figure 2 depicts the utilization of various artificial intelligence algorithms in plant disease detection. These algorithms are further divided into machine learning and deep learning-based classifiers. The description of these algorithms is illustrated in the coming subsections.
Steps of image processing techniques.
Description of machine learning and deep learning algorithm used for plant disease detection.
Classification is the process of dividing the dataset into different categories or groups by adding labels. Nowadays, the machine learning and deep learning approaches are performing well for classifying the algorithm images based on their category. Following are the machine learning algorithms which are used to classify plant disease and are based on supervised learning. Supervised learning is a type of learning where labels (category of images) are given along with input images.
It is the machine learning algorithm used for classification and calculated by
It is the algorithm of machine learning which comes under supervised learning to solve regression and classification-based problems. The decision tree is the graphical representation of pre-defined rules along with the solution. The graph of the decision tree has two types of nodes: one is decision nodes and another is leaf nodes. Additionally, the edges store the information of the answers to the questions, and leaf nodes store the actual output. In Sabrol and Kumar [16], Chopda et al. [17] and Rajesh et al. [18], the authors reported appreciable results in plant disease classification and recognition.
Support vector machine (SVM) is a very popular classifier used in statistical learning. The classifier aims to discriminate the classes from each other. In SVM, a hyperplane is used to discriminate one class from another. Those points which are close to the hyperplane are referred to as support vectors. The task of the SVM is to classify the different categories based on some features. Additionally, this algorithm performs well in extreme classes. Let us consider, color, texture, shape are some features of a particular plant. If we consider two features such as color and texture to classify diseased and healthy leaves. To classify them, the optimal decision boundary is required. Optimal decision boundaries could result in greater misclassification for the new instance. Therefore, the boundary support vectors are very important than all the training examples. This algorithm works well for linearly separating data points whereas in some cases if the data points are not linearly separable then 2-dimensional (2D) feature spaces are converted into 3-dimensional feature spaces. But the only problem is that it is computationally very expensive. In addition to that, it provides kernel function which can reduce the computational cost to convert 2D feature space to 3-dimensional feature space. Using kernel function the dot product is performed between two vectors. Especially, this is used to transform non-linear to linear transformation space. Various popular kernel functions are polynomial, radial basis, sigmoid kernels used to change 2D data to high dimensional feature space. Choosing the best kernel is a non-trivial task and is a hyper-parameter that can be selected by performing various experiments on the data. The main benefit of using SVM is that it is memory efficient and effective for high-dimensional feature space data.
It is the special type of machine learning algorithm used for classification. The researchers have been working on artificial neural networks (ANNs) since the beginning of the 1980s [19]. ANNs are a special type of classification algorithm and their structure is inspired by the human brain. ANNs takes input from the external world in the form of feature vector or patterns. Each input value is multiplied by their corresponding weights that are summing with the bias value. Further, the result is mapped to the activation function (binary, sigmoid) and produced the output. Other than these algorithms, there are various algorithms available that reported appreciable results in image recognition such as Random Forest, Naive Bayes, many more. Initially, we started with the study of traditional computer vision approaches used for plant disease detection. Plant disease can be caused by fungi, bacteria, and viruses from which fungi are the common disease organism. It is the type of disease that can be formed by taking energy from plants. The fungal disease has been identified using image processing techniques on different horticulture/agriculture crops [20]. To detect the different types of fruits, vegetable, commercial, and cereal crops that have been utilized using various feature extraction and classification algorithms. They achieved appreciable classification accuracy to identify the disease from horticulture/agriculture crops. Han et al. proposed a novel technique for feature extraction using super-pixel and marker-controlled segmentation methods for the classification of yellow rust and septoria diseases. They have used SVM and ANN for these disease classifications. Their experimentation concludes that SVM classifiers outperformed well than ANN classifiers for the classification of disease [21]. Su et al. experimented with the detection of fungal yellow rust disease on wheat crops. The author collected RGB images with a high-resolution camera and there are a total of three different classes present in region of interest (RoI) as rust, healthy, and background. To monitor the yellow rust, they used the U-Net deep learning architecture and the results were compared with the Random Forest algorithm. They found that U-Net-based segmentation outperformed spectral images. In their work, the average precision of 81.06%, recall of 90.10%, and F1-score of 84.00% have been achieved to segment the disease from spectral images [22]. An application of Fuzzy C-Means clustering has been proposed as the model to identify the wheat leaf disease [23]. In their work, they extracted inter- and intra-class features and further combined them to build a model for identifying the different wheat plant diseases. Although the traditional machine learning-based techniques are performing well for image classification, still there are certain limitations such as it requires manual feature extraction and is only suitable for small datasets, which may lead to the over-fitting problem [23, 24].
Convolutional neural network (CNN) is a popular neural network, designed for solving computer vision problems. The architecture of CNNs is shown in Figure 3. The images are represented in the form of pixel values. In the convolution layers, the operation of convolution is performed i.e., the kernel is slide over the input image after choosing the padding and stride values at each layer. Thereafter, the power of non-linearity is to give the non-linear mapping with the input images in such a way that after the non-linear mapping it becomes linearly separable. ReLU activation function is used to change all the negative values to positive values. With this, the pooling layer is used to down sample the different feature maps for getting the most prominent features i.e., the convolution layer performs these triplet operations like convolution followed by ReLU and ReLU followed by pooling one after another. These triplets operations are typically stacked one after another and also based on these triplets, the depth of the neural network has been defined. After these layers, the network is followed by one or more fully connected layers which are responsible for classification.
The basic architecture of CNNs.
To build the CNN model, all the above-mentioned parameters play a very important role. To build the custom CNN model, the numbers of convolution layers, max-pool layer, number of filter values, filter size, stride, padding, number of fully connected layers need to be specified. Increasing the number of convolution layers will produce different feature maps and also increasing the fully-connected layers increase the training time of the model. Although, the custom CNN model reported appreciable accuracy. The process of creating a custom CNN model takes more time. Therefore, the concept of transfer learning comes into the picture. Transfer learning is a concept of deep learning where the weights of pre-trained models are reused for a new problem. Every year, there is a competition held on the ImageNet dataset. Many researchers developed new models to classify the different objects of the ImageNet dataset and reported good classification accuracy and reduced error rate. There are variants of transfer learning models such as ResNet, GoogleNet, and EfficientNet varied in terms of the number of layers, filter size, number of filters used, stride, padding, and so on. Some of the few models are elaborated as given below:
Modern deep learning architectures are significantly popular to solve agriculture-related problems. Sladojevic et al. developed a CNNs based model for plant disease classification. The model recognized 13 different types of plants. In their work, they used 30,880 images in the training and 2589 images for validation and reported a classification accuracy of 96.30% [25]. Zhang et al. proposed a deep learning model for the detection of rust disease of wheat crop from hyperspectral images. In their work, they automate the process of detecting yellow rust-captured images from unmanned aerial vehicle (UAV). Yellow rust is a fungal disease that can cause 100% loss for the wheat crop. The author used the Inception-ResNet model for feature extraction and reported the highest accuracy of 85.00% when compared with the random forest that was 77.00% [26]. A deep learning model has been built for grading wheat stripe rust disease [27]. In their work, they used different mobile devices to capture images and build their dataset, referred WSRgrading. It contained 5242 wheat leaf images at six different levels. They build and proposed the model by adding an attention layer in the pre-trained DenseNet model and build a new model named as C-DenseNet which has been reported a good classification accuracy of 97.99%. Genaev et al. classify the rust disease from the wheat crop. In their work, they used the CGIAR dataset, containing three classes (healthy wheat, leaf rust, and stem rust). They implemented the DenseNet transfer learning model and reported the F1-score and AUC of 0.90 and 0.98, respectively [28]. Jia et al. in proposed the model for detection and segmentation of fruit features for optimal harvesting of apples using Mask R-CNN. ResNet model was used as the backbone of this network. The model was tested on 120 images and reported precision and recall rates of 97.31% and 95.70%, respectively [29]. The shortage of the wheat disease dataset motivated the researchers to create the dataset which should be publicly available for all [30]. They are motivated to collect more data that will help the research community for conducting the research competitions on wheat diseases classification. Finally, they attempted to prepare their WFD2020 dataset which contains 2414 images. They performed their experiments using the EfficientNet CNN-based model and reported 94.20% classification accuracy.
In the recent decade, deep learning techniques are highly utilized for image processing. Deep learning models are producing appreciable results than machine learning methods [31]. Figure 4 depicts the utilization of computer vision approaches (i.e., old machine learning methods and modern deep learning approaches) for the wheat crop. These statistics have been built based on work done from the period (2015 to July 2021) for classifying most of the wheat crop diseases. Deep learning approaches include CNN-based architecture such as VGG16, ResNet, Faster R-CNN, and so on. In different circumstances, the traditional machine learning approaches include SVM, Random Forest, and so on. The analysis concludes that the modern deep learning architectures have been utilized more for classifying most of the wheat crops diseases as compared to traditional machine learning approaches.
Year-wise statistics publication of wheat disease detection.
There are standard datasets that are publicly available for research experimentation in the computer vision and image processing domain, such as PASCAL VOC [32], ImageNet [33], IMDB-Wiki [34], CIFAR [35], and PlantVillage [36]. CGIAR dataset is one of the dataset publicly available on https://www.kaggle.com/shadabhussain/cgiar-computer-vision-for-crop-disease [37]. This dataset was further distributed in three different classes of wheat rust i.e., healthy wheat, leaf rust, and stem rust. A sample of each class is shown in Figure 5. Most of the images in this dataset were collected by CIMMYT and its partners from Ethiopia and Tanzania. Additionally, a few images were sourced from the Google image database. The images in this dataset have the specific characteristics like (i) all are colored (ii) mixed format, (iii) different orientation, (iv) variable quality, and captured with different resolutions. The datasets are already classified into two categories i.e., 876 images and 610 images for training and testing, respectively. From the training dataset (i.e., 876 images) a total of 863 images have been filtered and considered for training the model. In the present study, the 863 images dataset was further split for training and validation in the ratio of 3:1 (i.e., 75% data in training and 25% into validation). Table 1 describes the class-wise distribution of this dataset. It is a challenging task to build an efficient model that is capable to classify all three classes of images accurately.
Sampled images of (a) healthy wheat plant, (b) leaf rust, and (c) stem rust.
Class label | Images | Training set | Validation set |
---|---|---|---|
Healthy wheat | 142 | 105 | 36 |
Leaf rust | 345 | 258 | 86 |
Stem rust | 376 | 283 | 95 |
Total images | 863 | 646 | 217 |
Class-wise distribution of image dataset.
Deep learning is a popular methodology used for image processing. In deep learning models, features are extracted automatically and little human intervention is required to train the model. Deep learning models are quite efficient to discover the internal structure or patterns of high-dimensional data. However, directly processing the original images leads to inappropriate recognition results, therefore, it is necessary to pre-process the images before feeding them to the model. Pre-processing involves e.g. resizing, enhancing, or removing noise of the input images. It is worth mentioning that CNNs perform better for image recognition and classification. There are various transfer learning models which are based on CNNs like AlexNet, VGG16, GoogleNet, and Inception V3, that are pre-trained on the ImageNet dataset. ImageNet is the standard dataset that contains 1000 different categories of objects. CNN’s based transfer learning models reported appreciable results to classify 1000 different objects present in the ImageNet dataset. In the present study, the VGG16 model has been utilized and the architecture is depicted in Figure 6. This model is the composition of 16-layers (13 convolution layers, and 3 fully connected layers). In this model, the images are processed in standard size i.e., 224 × 224. The reason for resizing the fixed image size is to extract the uniform or equal feature maps at the end of the convolution process. This model used a fixed size of kernel i.e., 3 × 3. Sometimes, the kernel is referred to as a filter that is responsible for extracting features from the given images. These extracted patterns or features might be horizontal edges, vertical edges, and a combination of both. Initially, a convolution process has been performed to extract the features, and thereafter the classification is done. In the convolution operation, the kernel/filter is sliding over the image starting from the top left to the bottom right corner to extract the features.
The architecture of VGG16 for wheat rust disease detection.
The movement of the kernel is either pixel-wise or by skipping some pixels using stride values. If the stride value is 1 then the movement of the kernel is shifted by one pixel after another and if the stride value is 2, then the movement of the kernel is shifted by two-pixel values during the operation of convolution. The convolution layers are used to identify the pattern or features from the images which further help in discriminating the classes. The initial layers extract the general features like edges and the subsequent layers extract the domain-specific features. Each convolution block is followed by the max-pool layer which is used for down-sampling the feature maps. In this process, the dimensionality of the image is reduced by retaining the most prominent feature. At the end of the convolution layers, different feature maps are generated as an output. These feature maps are further flattened and mapped with a fully connected layer in the classification module. Here, the model has a feature vector of size 4096 neurons also referred to as dense layer. This feature vector is further passed to the next dense layer of the same size. Finally, the last layer neurons are fully connected to the output neurons by using the soft-max activation function. However, in the current study, we considered the three classes classification problem. Therefore, the output layer changed to three classes using the soft-max probability function. The actual learning starts from data using forward and backward passes. In the forward pass, input neurons are multiplied with the weight values and also apply the activation function as ReLU. ReLU activation function adds non-linearity to the model i.e., all the negative pixel-values become positive after passing through it. On the other hand, in backward pass back-propagation is used to minimize the loss value. In this process, weights and biases are getting updated from the last to the initial layer by calculating the gradients at each layer using a convolution operator.
To summarize this model, the important and noticeable point is that this model has a total of 14,789,955 parameters but 75,267 are trainable parameters and the rest are non-trainable, the reason is that using transfer learning, the already trained weights have been used during building the model. Therefore, the model is trained in less time with fewer number parameters.
Hyper-parameter tuning is the backbone of any deep learning model. Finding the best parameters is a very tedious task, it needs many experiments to be performed while building the model. Hyper-parameters include learning rate, batch size, loss function, number of epochs, and optimizer is usually considered for tuning the model. To build the classification model for three classes each hyper-parameter is considered within a specific range. In this way, several experiments have been performed to build an efficient model. After performing some experiments with the variation in the given hyper-parameters, it was concluded that model accuracy is highly dependent on the batch size, learning rate, number of epochs, and size of the dataset. In the present study, the following hyper-parameters has been utilized:
As discussed in Section 3.1 image dataset of wheat disease classification has been utilized to train the model. We used the online google colab platform with GPU support. Among the performed experiments, we discuss the best one, which produces the highest training accuracy. Table 2 illustrates the training and validation accuracies obtained at different epochs (varied from 10 to 90) along with their loss values. Here, the training accuracy starts with 81.42% on 10 epochs and ends up with 99.54% on 80 epochs. We continued to compute the accuracy for the 90 epochs also but did not get any significant improvement in training accuracy. Although more experiments could be performed by increasing the number of epochs, the accuracy obtained at epoch 80 was quite promising. On the other hand, the validation accuracy fluctuating between 74.76% and 79.05% at different epochs, as shown in Figure 7. Similarly, it was observed that the training loss decreases at every increasing step of the epoch (from 10 to 80). Beyond that, the loss has started to increase. In contrast, the validation loss is fluctuating between 0.60 and 0.65 up to 40 epochs. Then, after 70 epochs it starts increasing rapidly (Figure 8).
Epochs | Training accuracy (in %) | Validation accuracy (in %) | Training loss | Validation loss |
---|---|---|---|---|
10 | 81.42 | 74.76 | 0.50 | 0.65 |
20 | 91.02 | 79.05 | 0.33 | 0.61 |
30 | 95.05 | 77.14 | 0.22 | 0.61 |
40 | 96.59 | 78.10 | 0.18 | 0.61 |
50 | 97.06 | 76.67 | 0.15 | 0.56 |
60 | 97.99 | 78.10 | 0.12 | 0.56 |
70 | 98.61 | 74.29 | 0.09 | 0.66 |
80 | 99.54 | 77.14 | 0.07 | 0.74 |
90 | 99.23 | 74.76 | 0.08 | 0.82 |
Comparison of training accuracy, validation accuracy, and training loss, and validation loss at different epochs.
Representation of the comparison of training and validation accuracy.
Representation of the comparison of training and validation loss.
To test the performance of the trained model, we performed the test experiments on the validation data (i.e., 25% of the total dataset). In this way, a total of 36 sample images of healthy leaf, 87 sample images of leaf rust, and 94 sample images of stem rust have been considered. The evaluation of the testing results was done using a confusion matrix. Figure 9 illustrates the accuracy and confusion with other intra-classes, wherein, it is shown that leaf rust class samples are confused with stem rust class samples due to less variation between classes.
Confusion matrix at epoch = 80.
To summarize this book chapter, different machine learning and deep learning-based models have been discussed to solve plant disease classification and detection problems. Considering a case study of wheat rust diseases, a deep learning-based model is proposed to classify the different wheat rust diseases using a pre-trained VGG16 model. Based on the CGIAR dataset with three classes (stem rust, leaf rust, and healthy wheat), the proposed model has been optimized and produced the classification accuracy of 99.54%, and when evaluated on unseen data it gave a validation accuracy of 77.14%. This model will further help farmers or experts to diagnose disease in the early stages. Although these models give good training accuracy, they were not appropriate to classify stem- and leaf rust when result plot on confusion metrics. This is due to the fact that some images in this dataset contained multiple diseases, meaning that one image contained the features of both leaf- and stem rust. Detection and classification of the wheat rust disease in the early stages lead to high yield at the production level [38]. In the future, we will extend this work by collecting real-time images of wheat rust disease and also incorporating object detection-based algorithms such as Yolov3, Faster R-CNN, and Mask R-CNN [39] to exactly localize the location of the disease in the image.
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. 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He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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