Details of fiber samples for wadding use.
\r\n\tDiagnosis (clinical, radiological, cytogenetic, and molecular criteria), pathogenesis (risk factors, pre-myeloma conditions, and bone marrow microenvironment), cytogenetic abnormalities and molecular profiles disease staging and risk stratification, novel therapies such as proteasome inhibitors, immunomodulatory agents as well as monoclonal antibodies, drug resistance (primary and secondary resistance as well as evolution of new genetic mutations that may be disease or therapy-related), hematopoietic stem cell transplantation (HSCT) (autologous HSCT, allogeneic HSCT, and tandem transplantation), relapsed and refractory multiple myeloma, minimal residual disease (evaluation by flow cytometry or various sequencing techniques, importance of MRD in prognosis and prediction of disease relapse), chimeric antigen receptor (CAR) T-cell therapy, infectious complications in multiple myeloma (viral infections, bacterial infections, fungal infections, disease-related infections and therapy-related infections).
\r\n\r\n\tThe book chapters will intend to be written by scientists and experts in the field from various institutions around the world.
",isbn:"978-1-80356-093-9",printIsbn:"978-1-80356-092-2",pdfIsbn:"978-1-80356-094-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"c8e2b12df4fc2d313aced448fe08a63e",bookSignature:"Dr. Khalid Ahmed Al-Anazi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11600.jpg",keywords:"Risk Factors, Angiogenesis, Signaling Pathways, Therapeutic Targets, Drug Resistance, Genetic Mutations, Disease-Related Infections, Therapy-Related Infections, Complete Remission, Overall Survival, Immunomodulatory Agents, Bone Marrow Microenvironment",numberOfDownloads:13,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 26th 2022",dateEndSecondStepPublish:"March 29th 2022",dateEndThirdStepPublish:"May 28th 2022",dateEndFourthStepPublish:"August 16th 2022",dateEndFifthStepPublish:"October 15th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Khalid Al-Anazi established the Hematopoietic Stem Cell Transplantation Services in Saudi Arabia. He is a distinguished researcher in the fields of stem cell therapies & infections in immunocompromised individuals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"37255",title:"Dr.",name:"Khalid",middleName:"Ahmed",surname:"Al-Anazi",slug:"khalid-al-anazi",fullName:"Khalid Al-Anazi",profilePictureURL:"https://mts.intechopen.com/storage/users/37255/images/system/37255.jpg",biography:"Dr. Khalid Ahmed Al-Anazi is a consultant Hemato-Oncologist and the Chairman of the Department of Adult Hematology and Hematopoietic Stem Cell Transplantation (HSCT) at King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia. \r\nHe graduated from the college of medicine, King Saud University (KSU) in Riyadh in 1986. After having his Boards in Internal Medicine, he trained in clinical hematology and HSCT at King’s College Hospital, University of London, U.K. He has 4 year experience in internal medicine and 28 year experience in adult clinical hematology and HSCT at: Riyadh Armed Forces Hospital; King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Riyadh; King Khalid University Hospital (KKUH) and the College of Medicine, KSU in Riyadh; and KFSH in Dammam, Saudi Arabia. \r\nHe established the adult HSCT program at KFSH in Dammam in the year 2010. He received the award of the best teacher in the Department of Medicine, at the College of Medicine and KKUH in Riyadh in the year 2014. \r\n\r\nHe has more than 95 publications including retrospective studies, review articles, book chapters, and electronic books and he is a reviewer for 25 international medical journals. \r\nHe is the Editor-in-Chief of the Journal of Stem Cell Biology and Transplantation and the Journal of Molecular Genetics and Medicine in addition to being Associate Editor for 26 other medical journals in HSCT, hematology, cancer and infectious diseases. \r\nHe is a member of several international organizations including ECIL (European Conference of Infections in Leukemia).",institutionString:"King Fahad Specialist Hospital",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"King Fahad Specialist Hospital",institutionURL:null,country:{name:"Saudi Arabia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"82492",title:"Treatment of Patients with Newly-Diagnosed Multiple Myeloma",slug:"treatment-of-patients-with-newly-diagnosed-multiple-myeloma",totalDownloads:2,totalCrossrefCites:0,authors:[null]},{id:"82258",title:"Management of Renal Failure in Multiple Myeloma",slug:"management-of-renal-failure-in-multiple-myeloma",totalDownloads:11,totalCrossrefCites:0,authors:[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:"8026",title:"Update on Mesenchymal and Induced Pluripotent Stem Cells",subtitle:null,isOpenForSubmission:!1,hash:"48115afa72bcce1bde1e5b0e6c45f1b8",slug:"update-on-mesenchymal-and-induced-pluripotent-stem-cells",bookSignature:"Khalid Ahmed Al-Anazi",coverURL:"https://cdn.intechopen.com/books/images_new/8026.jpg",editedByType:"Edited by",editors:[{id:"37255",title:"Dr.",name:"Khalid",surname:"Al-Anazi",slug:"khalid-al-anazi",fullName:"Khalid Al-Anazi"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6710",title:"Update on Multiple Myeloma",subtitle:null,isOpenForSubmission:!1,hash:"229a96a2de131b3ac67f9f41b91de8f8",slug:"update-on-multiple-myeloma",bookSignature:"Khalid Ahmed Al-Anazi",coverURL:"https://cdn.intechopen.com/books/images_new/6710.jpg",editedByType:"Edited by",editors:[{id:"37255",title:"Dr.",name:"Khalid",surname:"Al-Anazi",slug:"khalid-al-anazi",fullName:"Khalid Al-Anazi"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{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:"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:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"60619",title:"Effective Thermal Conductivity of Cupra and Polyester Fiber Assemblies in Low Fiber Volume Fraction",doi:"10.5772/intechopen.75604",slug:"effective-thermal-conductivity-of-cupra-and-polyester-fiber-assemblies-in-low-fiber-volume-fraction",body:'In this chapter, a method to evaluate thermal insulation properties of staple fiber assembly for wadding use is proposed. Fiber assembly in low fiber volume fraction is a system which consists of a lot of air and a small amount of fiber, and its heat transfer mechanism is supposed to include convective and radiative heat transfer in addition to conductive heat transfer. Therefore, it is not suitable to use thermal conductivity to evaluate thermophysical properties of fiber assembly. Instead, a concept of effective thermal conductivity is suitable to evaluate thermophysical properties of fiber assembly which can include different types of heat transfer mechanisms such as conduction, convection, and radiation. In this chapter, the effective thermal conductivity is used to evaluate thermal insulation properties of fiber assembly in low Fiber volume fraction, Φ for wadding use. In addition, a model to explain heat transfer mechanism within fiber assembly is proposed.
Effective thermal conductivity is measured based on guarded hot plate (GHP) method using KES-F7II Thermo Labo II apparatus (Kato Tech Ltd.). Fiber sample is put uniformly into sample frame made of polystyrene foam. In the measurement, sample system made of fiber assembly and sample frame is placed between heat source plate and heat sink, and heat flow to keep heat source temperature constant is measured.
Samples used are polyethylene terephthalate and cupra staple fiber assembly. Effective thermal conductivity is measured under various sample thickness and fiber volume fraction. The results are analyzed based on nonlinear regression method, and heat transfer mechanism within fiber assembly is discussed based on the result of analysis. Finally, designing of fiber assembly-based thermal insulation materials is investigated.
Studies on heat conduction of fiber assembly have been conducted by Nogai et al. [1, 2], Fujimoto et al. [3], and Ohmura et al. [4] in Japan. Nogai et al. carried out theoretical and experimental study of heat transfer mechanism of polyester fiber assembly [1, 2]. Fujimoto et al. investigated the effective thermal conductivity of clothing materials for protection against cold [3]. Ohmura et al. studied the effective thermal conductivity of fibrous thermal insulation materials for building [4].
In all papers, it is confirmed that effective thermal conductivity- fiber volume fraction curve in low fiber volume fraction (<10%) shows the shape of convex downward. The fact that effective thermal conductivity curve has a minimum value gives a reason that fiber assembly have been widely used for thermal insulation materials. The minimum value in effective thermal conductivity curve was explained by increasing radiative heat transfer through pore space with increasing porosity based on their experimental and theoretical results and heat transfer model. Nogai et al. [1, 2] derived these facts based on uniaxial-oriented fiber assembly model considering conduction in fiber and radiative heat transfer. Fujimoto et al. [3] derived these facts based on serial-parallel model of heat transfer which is composed of fiber and pore part including radiative heat transfer.
In this study, effective thermal conductivity of fiber assembly in low fiber volume fraction is analyzed using empirical equation by nonlinear regression method [4, 5, 6, 7]. The reason that fiber assembly has been used for thermal insulation materials is further investigated through the separation of heat transfer component of effective thermal conductivity.
Four kinds of fiber materials used in this experiment such as cupra fiber (CU), polyester fiber with round section (RPE), polyester fiber with heteromorphic (W-shape) section (WPE), and polytrimethylene terephthalate fiber (PTT) of which fineness and fiber length are almost the same. The web after opening and carding process is conditioned under the environment of 20°C and 65% RH for 24 h. Fiber assembly is served as sample after conditioning. The details of fiber samples are shown in Table 1.
Details of fiber samples for wadding use.
Main parameter for the measurement of effective thermal conductivity is fiber volume fraction in this experiment. Thermophysical properties for different fiber materials which have different specific gravity are compared under the same fiber volume fraction. Fiber volume fraction is defined as the ratio of fiber volume to apparent volume of fiber assembly. Fiber volume fraction Φ is calculated using following equation.
where W is sample weight (g),
Schematic diagram of filling fiber sample into frame made of polystyrene foam.
Frame made of polystyrene foam (a) top view and (b) bottom view.
In this experiment, the effect of fiber volume fraction and sample thickness on effective thermal conductivity of fiber assembly is investigated. Fiber volume fraction is changed by five stages such as 0.001, 0.005, 0.010, 0.025, and 0.030. Sample thickness is changed by three stages such as 2, 3, and 5 cm. One kind of fiber is, therefore, measured under 15 conditions. Sample weight for each fiber under different condition is shown in Table 2.
Sample weight for each measurement condition (unit: G).
Effective thermal conductivity is measured using KES-F7II Thermo Labo II apparatus (Kato Tech Ltd.) [8]. Heat flux which flows through fabric sample from heat source plate (BT-Box) to heat sink is measured based on guarded hot plate (GHP) method. Schematic diagram of measurement part (section) is shown in Figure 3. Fiber sample is put uniformly into frame made of polystyrene foam of which inner size is 5 cm square and constant thickness. Fiber assembly with sample frame is placed between heat source plate and heat sink and measurement of heat flow is carried out. The experiment is conducted in a controlled environment room of which temperature is 20°C and humidity is 65% RH.
Measurement of thermal conductivity.
Heat source temperature (BT-Box) is set at 30°C and temperature of heat sink is set at 20°C, and thus, temperature difference is 10°C. Heat source plate is placed on upper side of sample and heat sink is placed on lower side, and thus the direction of heat flow agrees with the direction of acceleration of gravity. Upper side of heat sink is made of metal plate and temperature of heat sink is controlled at 20°C by cooling device driven by Peltier element.
In the measurement, fiber assembly with sample frame is placed between heat source plate and heat sink, and electric power (W) is measured to keep heat source temperature steady state. Output signal of electric power is recorded with data logger which is connected to output terminal. Output signal is recorded at intervals of 1 s with time elapsed. An example of measurement is shown in Figure 4. Electric power is recorded against time from the contact between heat source and sample to the steady sate. Electric power, Q (W), is obtained from mean value between 4 and 5 min. Effective thermal conductivity, λ (W/mK), is calculated from the following equation:
where Q is electric power to keep steady state (W), d is sample thickness (m), A is area of heat source plate, and ΔT is temperature difference between heat source plate and heat sink (K).
Method to obtain heat flux in steady state.
In this experiment, a small amount of heat flow through insulation material is measured. Therefore, care must be taken so that measurement part (BT-Box and heat sink) is not affected by unexpected influence of heat caused by convective and radiative heat transfer. As for radiative heat transfer, board made of polystyrene foam is set up in the front, right, and left side of measurement part to block radiative heat transfer from operator. As for convective heat transfer, it is desirable that a device which may cause convection should be removed from the place where experiment is conducted.
Effective thermal conductivity of staple fiber assembly is measured under three different thickness and five different fiber volume fractions. Results obtained from the measurement are investigated in connection with fiber volume fraction, sample thickness, fiber materials, and so on. In this experiment, three times of measurement were carried out for each condition, and mean value and standard deviation were obtained.
In the first place, the relation between effective thermal conductivity and fiber volume fraction is investigated in relation to fiber material. Figures 5–7 show the results between effective thermal conductivity and fiber volume fraction. Figures 5–7 show the results of 2, 3, and 5 cm thickness, respectively. In each graph, the ordinate denotes effective thermal conductivity (W/mK) and the abscissa denotes fiber volume fraction (n.d.). The fact that the level of the magnitude of thermal conductivity increases with increasing thickness is observed and this will be discussed later. Here, we concentrate on the relation between effective thermal conductivity and fiber volume fraction for the result of 5-cm thickness. Magnitude of effective thermal conductivity is compared under constant thickness. CU is the largest and polyester fiber group (RPE, WPE, PTT) follows. There is no significant difference among RPE, WPE, and PTT within the range of this method.
Thermal conductivity plotted against fiber volume fraction when thickness of sample is 2 cm.
Thermal conductivity plotted against fiber volume fraction when thickness of sample is 3 cm.
Thermal conductivity plotted against fiber volume fraction when thickness of sample is 5 cm.
Next, the effect of fiber material on the relation between effective thermal conductivity and fiber volume fraction is investigated. For each material, the shape of thermal conductivity curve is convex downward. The feature of the curve for each fiber material is as follows.
For CU, the minimum value of thermal conductivity lies around φ = 0.005 and thermal conductivity increases with increasing φ. For RPE, WPE, and PTT, the minimum value of thermal conductivity lies around φ = 0.01 and thermal conductivity increases a little to φ = 0.03. For CU, thermal conductivity increases a little when φ decreases from 0.005 to 0.001. For RPE, WPE, and PTT, thermal conductivity increases a little when φ decreases from 0.01 to 0.001. With decreasing thickness from 3 to 2 cm, the level of thermal conductivity decreases and the shape of curve becomes flattened.
Figure 8 shows the relationship between effective thermal conductivity and sample thickness for φ = 0.01. The ordinate denotes effective thermal conductivity and the abscissa denotes sample thickness. Clearly, thermal conductivity increases linearly against thickness for all samples.
Thermal conductivity plotted against thickness when volume fraction of sample is 0.01.
Effective thermal conductivity obtained in this section is tentative value including leakage of heat from sidewall. In the following section, the separation of effective thermal conductivity into elemental process of heat transfer will be discussed. The thickness dependence of effective thermal conductivity shown in Figure 8 will be investigated after the discussion about the separation heat transfer component.
Because porosity of fiber assembly in this study is very large (97–99%), it is conjectured that the mechanism of heat transfer within fiber assembly is very much influenced by the effect originated from pore structure. The mechanism of heat transfer in fiber assembly consists of conduction in fiber, radiation in pore between fibers, and gas conduction (air), when forced convection does not occur. Here, it is supposed that heat flows through parallel model which is made of three components of heat transfer mentioned above. Measured value of effective thermal conductivity, λ (W/mK), is expressed by the following equation as a function of bulk density ρ(kg/m3) [4, 5, 6, 7]:
where A (Wm2/Kkg) and B (Wkg/m4K) are constant coefficients and C (W/mK) is constant. The first term at the right side denotes conductive heat transfer in fiber, the second term denotes radiative heat transfer, and the third term denotes conductive heat transfer through gas (air) in pore. (The details of parameters A, B, and C are shown in the Appendix.)
In this study, bulk density, ρ in Eq. (3) is replaced by fiber volume fraction, φ as follows,
This is because φ plays an equivalent role of ρ which expresses quantity of fiber. Results of the effective thermal conductivity of fiber assembly are analyzed by nonlinear regression method based on Eq. (4). Nonlinear regression analysis is carried out using
Estimated values of parameters A, B, and C obtained by nonlinear regression analysis are shown in Table 3, where A is coefficient of conduction in fiber, B is coefficient of radiative heat transfer, and C is conductive heat transfer in gas (constant). Calculated values of effective thermal conductivity using estimated values of A, B, and C are shown in Figures 9–12. Figures 9–12 show the results of thickness of 2, 3, and 5 cm for CU, RPE, WPE, and PTT fiber, respectively. In each graph, the ordinate denotes effective thermal conductivity, λ(W/mK), and the abscissa denotes fiber volume fraction (n.d.). Symbols (○, △, □) and solid line show measured values, and broken line shows regression curve using estimated values of A, B, and C. As shown in figures, agreement between measured and calculated curves of effective thermal conductivity is very good.
List of coefficients A, B, and constant C.
Comparison between calculated and measured values for CU. Symbols ○, □, △, and straight line: Measured values; broken line: Calculated values obtained by nonlinear regression analysis.
Comparison between calculated and measured values for WPE. Symbols ○, □, △, and straight line: Measured values; broken line: Calculated values obtained by nonlinear regression analysis.
Comparison between calculated and measured values for RPE. Symbols ○, □, △, and straight line: Measured values; broken line: Calculated values obtained by nonlinear regression analysis.
Comparison between calculated and measured values for PTT. Symbols ○, □, △, and straight line: Measured values; broken line: Calculated values obtained by using nonlinear regression analysis method.
Calculated curves of separation of components using estimated values of A, B, and C are shown in Figures 13–16. Figures 13–16 show the results of 3-cm thickness for CU, RPE, WPE, and PTT fiber, respectively. The ordinate denotes effective thermal conductivity, λ(W/mK), and the abscissa denotes fiber volume fraction, φ(n.d.). Dashed line shows component of conduction in fiber, Aφ, dash-dotted line shows component of radiative heat transfer, B/φ, two-dot chain line shows component of gas conduction, C, and solid line shows the measured value of effective thermal conductivity. Based on these graphs, the ratio of each component to effective thermal conductivity and the effect of pore in fiber assembly can be discussed for each fiber materials. It is clear that the ratio among Aφ, B/φ, and C is different for different fiber materials.
Separation of heat transfer component by nonlinear regression analysis for CU.
Separation of heat transfer component by nonlinear regression analysis for WPE.
Separation of heat transfer component by nonlinear regression analysis for RPE.
Separation of heat transfer component by nonlinear regression analysis for PTT.
First, component of conduction in fiber, Aφ is investigated. Physical meaning of A is increasing rate of conduction in fiber against φ. As shown in Table 3, coefficient A varies with fiber materials. CU has large A value compared to polyester fibers (RPE, WPE, PTT), and increasing rate of conduction in fiber is also large. As a result, the ratio of Aφ to effective thermal conductivity of CU is 30 percent at φ = 0.03. In contrast, the ratio of Aφ of WPE is less than 20% at φ = 0.03. Large ratio of conduction in fiber and large increasing ratio to φ is the feature of CU fiber compared to polyester fibers.
Second, component of radiative heat transfer, B/φ, is investigated. Generally, the ratio of B/φ to effective thermal conductivity is negligibly small for all samples. Contribution of B/φ is slightly recognized below φ = 0.005. It is observed that component of radiative heat transfer increases with decreasing fiber volume fraction, φ, from 0.005 to 0.001. In this study, the ratio of radiative heat transfer is very small compared to the previous results obtained by Nogai and Fujimoto [1, 2, 3]. This may arise from the difference in the degree of fiber orientation in fiber assembly. While Nogai and Fujimoto [1, 2, 3] cover fiber assembly with high degree of fiber orientation, we concentrate on fiber assembly with random orientation. Because frequency of collision between radiant heat and fiber is very large for randomly oriented fiber assembly, decay of radiation energy by absorption may become large.
In this section, component of gas conduction, C, is investigated. Looking over Figures 13–16, it seems that component of gas conduction, C, occupies the dominant part in effective thermal conductivity in many cases. Therefore, physical meaning of gas conduction C is discussed, hereafter. Estimated values of C for samples of 2-, 3-, and 5-cm thickness are shown in the lower part of Table 3. It seems that C values for each thickness have almost constant value despite different samples. On the other hand, the value of thermal conductivity of air, λair, in this measurement condition (25°C, 1 atm) is 2.62 × 10−4 (W/mK) in Ref. [9]. Taking into consideration that parameter C has same value for same thickness and C must include thermal conductivity of air, λair, let us suppose that following relation holds:
where C′ is constant independent of fiber volume fraction, φ. Mean values of each thickness, Cmean, thermal conductivity of air, λair and C′ are shown in Table 4 for the discussion in this section. It seems that C′ increases with increasing thickness. Relation between C′ and thickness is analyzed by linear regression method. The results are shown in Figure 17. C′ and thickness, d, have good correlation, and the regression equation is given as follows,
Thickness (cm) | Cmean | λair | C′ |
---|---|---|---|
2 | 4.495 × 10−2 | 2.62 × 10−2 | 1.875 × 10−2 |
3 | 5.19 × 10−2 | 2.62 × 10−2 | 2.57 × 10−2 |
5 | 7.33 × 10−2 | 2.62 × 10−2 | 4.71 × 10−2 |
Figures for calibration of leakage of heat.
Unit: W/mK
Function for the calibration of leakage of heat.
Because intercept is almost 0, C′ can be expressed approximately as linear function of d:
On the other hand, a function which expresses leakage of heat from sidewall of the frame is derived from another point of view. As inner side length of sample frame is 5 cm, total area of sidewall, A is expressed as following equation:
Here, temperature gradient along thickness direction is ignored for the simplification. Thus, leakage of heat per unit area along horizontal direction is assumed to be constant. Total leakage of heat from sidewall, Q’, is proportional to total area of sidewall which is expressed as follows:
This equation predicts that total leakage of heat from sidewall is proportional to thickness, d, and equals 0 at d = 0. This property coincides with the expression, C′ = k d in Eq. (7). Summarizing the discussion above, C′ can be regarded as total leakage of heat from sidewall.
Based on the discussion above, total leakage of heat, C′, can be eliminated from C value. Here, C values in Figures 13–16 can be replaced by λair (=C-C′). The results are shown in Figures 18–21, where the abscissa denotes true effective thermal conductivity. It is concluded that large part of effective thermal conductivity of fiber assembly consists of thermal conductivity of air (still air), λair. It should be noted that the air is trapped by small amount of fiber (φ < 0.03). (Explanation of this effect is given in the Appendix.)
Estimated values of effective thermal conductivity and its component (sample: CU, d = 3 cm).
Estimated values of effective thermal conductivity and its component (sample: WPE, d = 3 cm).
Estimated values of effective thermal conductivity and its component (sample: RPE, d = 3 cm).
Estimated values of effective thermal conductivity and its component (sample: PTT, d = 3 cm).
Contribution of fiber material to the effective thermal conductivity is expressed by A, coefficient of conduction in fiber. Physical meaning of A is increasing rate of conduction in fiber and it consists of two parts, that is, heat conduction through fiber and heat conduction at contact point between fibers. It is conjectured that magnitude of A depends on contact effect between fibers compared to conduction through fiber. In this measurement, Aφ component of cupra fiber (CU) is relatively large and that of polyester fibers (RPE, WPE, PTT) is relatively small. Polyester fibers have advantage in thermal insulation material because Aφ component is small over the wide range of fiber volume fraction.
Based on nonlinear regression model, measured value of effective thermal conductivity can be separated into three components, such as conduction in fiber, radiative heat transfer, and gas conduction. Schematic diagram of the model is shown in Figure 22. Gas conduction plays the most important role in thermal insulation properties, which originates from immovable air trapped by fibrous network. Component of conduction in fiber has secondary contribution to thermal insulation properties, especially in the range of higher fiber volume fraction. Although radiative heat transfer slightly appears in low range of fiber volume fraction, contribution of radiative heat transfer is negligible small to effective thermal conductivity of fiber assembly with random orientation. It is conjectured that relative ratio of each heat transfer component depends on fiber material and the effect of pore structure. These findings will be basic information for designing fiber assembly-based thermal insulation material.
Mechanism of heat transfer in fiber assembly.
In this study, the effective thermal conductivity of staple fiber assembly for wadding use is measured. Samples used are four kinds of fiber materials such as cupra fiber (CU), polyester fiber with round section (RPE), polyester fiber with heteromorphic section (WPE), and polytrimethylene terephthalate fiber (PTT).
Effective thermal conductivity is measured under five different fiber volume fractions, and effective thermal conductivity curve is obtained. Effective thermal conductivity curve is analyzed using empirical equation considering separation of heat transfer component. The results are analyzed by nonlinear regression method. Measurement is carried out including leakage of heat from sidewall of the sample frame. Calibration of leakage of heat is accomplished after the separation of heat transfer component by nonlinear regression analysis. Care must be taken so that the measurement system is not disturbed by radiative and convective heat transfer from outer environment. The results obtained are as follows.
The shape of effective thermal conductivity-fiber volume fraction curve is convex downward. For CU fiber, the minimum value of effective thermal conductivity lies around φ = 0.005. For polyester fibers (RPE, WPE, PTT), the minimum value lies around φ = 0.01.
Effective thermal conductivity can be separated into three components such as conduction in fiber, Aφ, radiative heat transfer, B/φ, and gas conduction, C.
Elimination of leakage of heat is accomplished after the separation of C component into thermal conductivity of air, λair, and leakage of heat from sidewall, C′.
Thermal conductivity of air, λair, has large contribution to total effective thermal conductivity, and conduction in fiber, Aφ follows in the range of higher fiber volume fraction. Contribution of radiative heat transfer is negligible small through this measurement.
Effective thermal conductivity of CU is the largest, and those of polyester fibers (RPE, WPE, PTT) follows.
With decreasing thickness, the shape of effective thermal conductivity curve becomes flattened, and the difference between fiber materials becomes small.
Precise determination of C value is required for the precise measurement of effective thermal conductivity. These findings will be basic information for designing fiber assembly-based thermal insulation materials.
As porosity of fiber assembly in this study is very large (>97%), it is expected that heat transfer within fiber assembly is affected by the effect concerning pore as well as conduction in fiber. Mechanism of heat transfer consists of conduction in fiber, radiative heat transfer in pore, and gas conduction in air. If it is supposed that three components of heat transfer are arranged parallel to heat flux (parallel model), the effective thermal conductivity of fiber assembly, λ(W/mK), is expressed as a function of bulk density ρ(kg/m3) as follows [4].
where λc is equivalent thermal conductivity of fiber, λr is equivalent thermal conductivity of radiative heat transfer, λg is equivalent thermal conductivity of gas conduction, A (Wm2/Kkg) and B (Wkg/m4K) are coefficients, and C (W/mK) is constant.
The first term in Eq. (A1b) denotes conduction in fiber, the second term denotes radiative heat transfer, and the third term denotes conductive heat transfer through gas. Here, the physical meaning of parameters A, B, and C are summarized based on literature [4] and other literatures concerning subjects in this study.
Conduction in fiber consists of two components that is, conduction through fiber and conduction at contact point between fibers. The factors concerning conduction in fibers, therefore, are numbers of fiber and numbers of contact point per unit volume. These factors are expected to be increase with increasing bulk density. If the relation between these factors and bulk density is assumed to be proportional for the simplification, equivalent thermal conductivity for conduction in fiber, λc, is expressed using coefficient A as follows:
Equivalent thermal conductivity of radiative heat transfer in pore within fiber assembly is expressed as follows:
where C0 is constant coefficient (n.d.), d is distance between parallel plate (m), σ is Stefan-Boltzmann constant (W/m2K4), ε is emissivity (n.d.), and T is absolute temperature (K).
Let M be the mass of fiber assembly filled into cell and S be area of section, d = M/Sρ. Substituting M/Sρ for d in Eq. (A3), λr is expressed as,
Nogai et al. [1, 2] classifies contribution of total radiative heat transfer in fiber assembly into following four elements: between heat source and fibers, (2) between heat source and heat sink, (3) between fibers, and (4) between fibers and heat sink.
Equation (A3) expresses radiative heat transfer between heat source and heat sink (2).
The third term, C (W/mK), is discussed in this section. It is confirmed that size of pore in this experiment is much larger than L, mean free path of air under atmospheric pressure. Therefore, it must be investigated that natural convection occurs or not by change of porosity.
To judge generation of natural convection in fiber assembly, it is not suitable to use Rayleigh number. Instead, modified Rayleigh number [10] to which shape factor of fiber assembly is added is used.
where g is acceleration of gravity (=9.8 m/s2), β is coefficient of body inflation (1/K), Δθ is temperature difference between heat source and heat sink (K), d is thickness of sample (m), ν is dynamic viscosity of air (m2/s), κ is thermal diffusivity of air (m2/s). k (m2) is Darcy’s transmission coefficient which depends on fiber diameter, t (m) and porosity, φ(n.d.).
If Ra is larger than critical modified Rayleigh number, Racr (=39.5), natural convection occurs in fiber assembly.
An example of calculation of Ra for sample RPE is shown. Parameters concerning sample RPE (φ = 0.03, T = 25°C) are as follows:
t = 1.276 × 10−5 (m), φ = 0.97, g = 9.8 (m2/s), β = 1/373 (1/K), Δθ = 10 (K), ν = 1.579 x 10−5 (m2/s), κ = 2.215 x 10−5 (m2/s).
Ra is calculated as follows:
Ra = 0.0202 for d = 0.02
Ra = 0.0304 for d = 0.03
Ra = 0.0513 for d = 0.05
As Ra < Racr (=39.5) for all cases, natural convection does not occur in fiber assembly in this case. Therefore, C is constant and λg = C. C is called as component of gas conduction.
In recent years, the Brazilian Judiciary has been advancing toward turning all its acts digital. Following this direction, the Brazilian Labour Court implemented in 2012 the Electronic Judicial Process (acronym in Portuguese for “
Knowing that human beings cannot promptly analyze a large set of data, especially when such data do not appear to correlate, a way to assist in the pattern-recognition process is through statistical, computational, and data analysis methods. From the perspective that an exponential increase in textual data exists, the analysis of patterns in legal documents has become increasingly challenging.
Currently, one of the major challenges in the legal area is to respond quickly to the growing judicial demand. The Brazilian legal system provides for ways to ensure the swift handling of judicial proceedings, such as the principle of the reasonable duration of a case, the principle of speed, the procedural economy, and due process to optimize the procedural progress [2]. Therefore, with the aid of some clustering mechanism, that is, the grouping of processes, with a good rate of similarity between the documents to be analyzed, it was possible to help in the distribution of work among the advisors of the office for which the process was drawn. In addition, it contributed to the search for case law1 for the judgment of the cases in point, to ensure a speedy trial, upholding the principle of legal certainty. According to Gomes Canotilho [3]:
Thus, this legal management tool created positive impacts such as the decrease of the operational costs of a legal proceeding, as a result of reducing its duration, meaning lower expenses on the allocation of the necessary resources for its judgment.
Recently, machine learning algorithms have demonstrated through research that they are powerful tools capable of solving high-complexity problems using natural language processing (NLP) [4]. In this sense, it is possible to highlight the works of [5, 6, 7, 8, 9], which apply the techniques of word-embedding generation, a form of vector representation of terms, and consequently of documents, taking into account their context. The use of these word embeddings is essential when analyzing a set of unstructured data presented in the form of large-volume documents in court.
Nowadays, a specialist screens the documents and distributes among the team members the legal proceedings to be judged, setting up a deviation from the main activity of this specialist, which is the production of draft decisions. This contributed to an increase in the congestion rate (an indicator that measures the percentage of cases that remain pending solution at the end of the base year) and to the decrease in the meeting of demand index (acronym in Portuguese for “
Description | 2° Degree | 1° Degree | Total | |
---|---|---|---|---|
Magistrates | Legal authority | 559 | 3077 | 3636 |
Legal workers | Public administration employee | 6911 | 22,785 | 29,696 |
Stockpile | Number of pending cases | 792,223 | 3,741,548 | 4,533,771 |
New cases | Number of new cases | 898,104 | 2,632,093 | 3,530,197 |
Judged | Number of cases judged | 989,324 | 3,036,686 | 4,026,010 |
Closed | Number of cases with final decision | 941,356 | 3,244,652 | 4,185,708 |
IAD | Closed cases/new cases | 104.8% | 123.3% | 118.6% |
Congestion tax | Closed cases/(new cases + stockpile) | 45.7% | 53.6% | 52.0% |
Knowledge | Fact awareness phase | — | 35.1% | 35.1% |
Execution | Judgment enforcement phase | — | 72.7% | 72.7% |
New cases | Average number of new cases per magistrate | 1607 | 662 | 821 |
Workflow | Average number of cases per magistrate | 3583 | 2794 | 2,927 |
Judged cases | Average number of cases judged per magistrate | 1770 | 1103 | 1216 |
Closed cases | Average number of cases closed per magistrate | 1684 | 1179 | 1264 |
New cases | Average number of new cases per worker | 135 | 83 | 95 |
Judged cases | Average number of cases judged per worker | 300 | 351 | 339 |
Closed cases | Average number of cases closed per worker | 141 | 148 | 146 |
Report of indicators of Brazilian labor justice.
This work aims, therefore, to present the degree of similarity between the judicial documents that was achieved in the inferred groups through unsupervised learning
This degree of congruence signals the model’s performance and is set from the average similarity measure of the grouped files, based on the similarity cosine between the elements of the group to its centroid and, comparatively, by the average cosine similarity among all the documents of the group.
Aiming to delimit the scope of this research, a dataset containing information from documents of the Ordinary Appeal Interposed (acronym in Portuguese for “
For the present work, a literature review on unsupervised machine learning algorithms applied to the legal area was performed, using NLP, and an overview of recent techniques that use artificial intelligence (AI) algorithms in word-embedding generation. Then, we applied some methods until the results were obtained, comparing and discussing them, and finally, conclusions and future challenges were presented.
Machine learning algorithms have in the most recent research demonstrated a great potential to solve high-complexity problems, which follow the categories into (i) supervised machine learning algorithms; (ii) unsupervised; (iii) semi-supervised; and (iv) by reinforcement [11]. In the context of this chapter, the literature review focused on the search for the most recent research on unsupervised machine learning or clustering algorithms applied to the legal area using NLP.
The investigation revealed that there are not many works dealing with the highlighted topic, which proves its complexity. Thus, we sought to expand the research by removing the restriction to the legal area bringing light to other publications. In [12], we discussed the content recommendation system approaches based on grouping for similar articles that used TF-IDF to perform vector transformation of the document contents and, through cosine similarity, applied k-means [13] for clustering them. In [14], the authors automatically summarized texts using TF-IDF and k-means to determine the document’s textual groups used to create the abstract. Then, TF-IDF is considered the primary technique for vectorizing textual content and k-means the most used algorithm for unsupervised machine learning.
Therefore, we can assume that choosing the best technique of generating word embeddings requires investigation, experimentation, and comparison of models. Several recent pieces of research have demonstrated the feasibility of using word embeddings to improve the quality of AI algorithm results for pattern detection, classification, among other uses.
In 2013, Mikolov et al. [6] proposed two new architectures to calculate vector representations of words calling them Word2Vec, which was considered, at the time, as a reference in the subject. Subsequently, techniques of word embeddings based on the use of the long short-term memory network (LSTM) [15] became widely used for speech recognition, language modeling, sentiment analysis, and text prediction, and that, unlike the recurrent neural network (RNN) they can forget, remember and update the information thus taking a step forward from the RNNs [16]. Therefore, LSTM-based libraries, such as Embeddings from Language Models (Elmo) [17], Flair [18], and context2vec [19] created a different word embedding for each occurrence of the word, related to the context, that allowed to capture the meaning of the word.
In more recent years, new techniques of word embeddings have emerged, with emphasis on (i) Bidirectional Encoder Representations from Transformers (BERT) [9], context-sensitive model with architecture based on a transformer model [20]; (ii) Sentence BERT (SBERT) [21], a “Siamese” BERT model that was proposed to improve BERT’s performance when seeking to obtain the similarity of sentences; and (iii) Text-to-Text Transfer Transformer (T5) [22], a framework for treating NLP issues as a text-to-text problem, that is, input to the template as text and template output as text.
From this analysis, it was possible to advance in the current state of the art in the area of NLP applied to the legal sector, by conducting a comparative study and application of the techniques TF-IDF, Word2Vec CBoW, and Word2Vec Skip-gram to perform the grouping of labor legal processes in Brazil using the k-means algorithm and the cosine similarity.
This section presents each step necessary to achieve the results and to make it possible to analyze them comparatively. To perform all the implementations of the routines necessary for this study, the Python programming language (version 3.6.9) was used and, among other libraries, (i) Numpy (version 1.19.2) was used; (ii) Pandas (version 1.1.3); (iii) Sklearn (version 0.21.3); (iv) Spacy (version 2.3.2); and (v) Nltk (version 3.5).
Every processing flow (pipeline) consists of the phases: (i) data extraction; (ii) data cleansing; (iii) generation of word-embedding templates; (iv) calculation of the vector representation of the document; (v) unsupervised learning; and (vi) calculation of the similarity measure, as detailed in the following subsections.
The dataset used for these studies belongs to the Regional Labour Court of the 5th Region (acronym in Portuguese for “Tribunal Regional do Trabalho da 5ª Região”—TRT5). There are approximately 210 (two hundred and ten) thousand documents of the Ordinary Appeal Interposed type, incorporated into the Electronic Judicial Process (PJe) system, originally added to the PJe in portable document format (PDF) or hypertext markup language (HTML). As the PJe has a tool for extracting and storing the contents of documents, there was no need for further processing in obtaining the text of such files.
In addition to the content of the documents, the following information was extracted: (i) the name of the parts of the proceedings to which such documents belonged; (ii) the list of labor justice issues from the Unified Procedural Table2 (acronym in Portuguese for “
Preprocessing is a fundamental step for the application of artificial intelligence techniques and involves the following: (i) data standardization (when there is a large discrepancy between the values presented to the technique); (ii) the withdrawal of null values; and (iii) the reorganization and adequacy of the structure of the dataset. In this case, it is usually necessary for experts to conduct an exploratory analysis of the data used in advance to determine the direction of preprocessing.
For this phase, this study uses two forms of preprocessing: (i) detection of the subjects of the Unified Procedural Table (contained in the extracted documents) and (ii) cleaning the contents of the documents.
For the detection of the subjects of the TPU present in the extracted documents, regular expression matching was used as the search technique to measure the occurrences of these words in the files marking them with “tags” referring to the subject found.
For cleaning the contents of documents, usually using a regular expression, the steps were as follows:
HTML tags: removed the html tags found in the document, such as <script>, <body>, <style> etc.;
TPU subjects: replaced the subject text with a subject tag, for example, “
Related Persons: replaced the name of the individuals linked to the legal cases of the documents, such as the name of the author(s) and defendant(s), by the “tag” “
Judicial process number: replaced the number of the judicial process (according to the standard formatting defined nationally by the CNJ, NNNNNNN-NN.NNNN.N.NN.NNNN where N is a numeral) by the "tag" “
Standardization of abbreviations: replacement of abbreviations (acronyms) by the full translation as drawn STF list as reported in Section 3.1, for example, CLT was transformed into “
Addresses: replaced the addresses contained in the document with the “tag” “
Links: removed Internet links contained in the text;
Date and Time: replacement of date and time content with “
Time: replacement of the time content with the “
Days of the week: removed the days of the week found in the document;
Document ids: replacement of PJe document ids referenced in the document with “tag” “
Unit of measure: replaced the units of measurements and their values by the “tag” “
Numbers: replaced the numbers in full, ordinal numbers, and numerical sequences by the “tag” “
Judging bodies: replaced the judging bodies (e.g., “
Months of the year: removed the months of the year found in the document;
Judicial Stopwords: only when the technique employed is TF-IDF. The common words were removed in all texts of the judiciary, such as (i) “
Stopwords:
TF-IDF: removed all stopwords from the Portuguese language, such as “
Other techniques: removed only the non-adverbs of the Portuguese language, for example, the words “
Line breaks: replaced line breaks by space;
Punctuation marks:
TF-IDF: removed all the punctuation marks contained in the documents;
Other techniques: removed the punctuation marks except dot (.), comma (,), exclamation (!), and interrogation (?);
Lemmatization:
TF-IDF: applied the technique to replace words with its root, for example, words such as “
Other techniques: lemmatization has not been applied;
In addition to the preprocessing detailed above, when the technique used was TF-IDF, the tags inserted in the text during this phase were removed.
An essential technique in solving machine learning problems, involving NLP, is the use of vector representation of words, in which numerical values indicate some correlation of words in the text. This chapter uses word embeddings generated and shared for the Portuguese language, such as Word2Vec CBoW and Word2Vec template with Skip-gram. These templates were created based on more than 1 billion and 300,000 tokens, with results published in the article “Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks” presented at the Symposium in Information and Human Language Technology - STIL 2017 [23].
Different from the TF-IDF technique, which has the vector representation of the document based on the statistical measurement of each term of the document in relation to all known corpus, and whose vector dimension is equal to the size of the vocabulary of the corpus, the other techniques (i) Word2Vec CBoW ptBR and (ii) Word2Vec Skip-gram pt-BR need to go through a change to calculate the vector representation of the document (document embeddings). This happens because for these techniques what you can get is the vector representation of the word (word embeddings).
Thus, to calculate the vector representation for the documents some alternatives are suggested, such as (i) average of the word embeddings of the words of the document; (ii) sum of the word embeddings of the words in the document by pondering them with the TF-IDF and then dividing by the sum of the TF-IDF of the words of the document; and (iii) weighted average with the TF-IDF of the word embeddings of the words of the document, the latter being the technique chosen for presenting the best result.
The use of unsupervised learning techniques is relevant when the intention is to detect patterns among court documents. The k-means algorithm, whose basic concepts were proposed by MacQueen [13], is the technique adopted in this study. In general, this technique seeks to recognize patterns from the random choice of K initial focal points (centroid), where K is the number of groups that one wishes to obtain and, iteratively, position the elements whose Euclidean distance is the minimum possible concerning the centroid of the group.
Since one does not have an ideal K to offer the algorithm, an approach usually used to support such a decision is to calculate the inertia, based on how well the dataset was grouped through k-means.
The inertia calculation is based on the sum of the square of the Euclidean distance from each point to its centroid and seeks to obtain the lowest K with the lowest inertia. However, the higher the K value reaches, the tendency is that inertia will be lower, and then, the elbow method was used to find the point where the reduction in inertia begins to decrease.
Hence, 31 values for K were used within the range from 30 to 61, considering an interval for each unit, selecting the K that generated the best grouping. In addition, the strategy of creating submodels, limited to two, was used for the documents of the groups whose average similarity rate did not reach a value greater than 0.5.
The similarity measure is an important tool for the measurement of the quality of inferred groups. In this study, the cosine similarity measure is adopted, which is a measure that calculates the cosine of the angle between two vectors projected in the multidimensional plane, the result of which is between 0 and 1, in which 1 represents that the two vectors are totally similar, and 0 represents that they are totally different. Given two vectors, X and Y, the cosine similarity is presented using a scalar product according to Eq. (1).
Consequently, to decide whether, after the clustering of the chief model, it was necessary to generate up to two more submodels, using the average cosine similarity among all elements of the group. Although the computational cost of calculating the similarity between all files in the group is relevant, we sought to reduce the distance between documents that were part of the same group, although they were located near the centroid. To assess the final efficiency of the technique, another form of calculation was adopted, computing for each group the average cosine similarity between the group elements and its centroid. Thus, as a measure of global similarity of each approach, we calculated the average of the average of the groups, so that the one that reached a value closer to 1 (one) was considered the best technique.
This research shows, as per the methodology presented in the previous sections, how machine learning algorithms associated with NLP techniques are important allies in optimizing the operational costs of the judicial process. It is evidenced from the result, for example, of document screenings and procedural distribution, which allows an expert to devote oneself to their chief activity optimizing working time.
While using the k-means unsupervised learning algorithm, it was necessary to choose the best K for each NLP technique studied. In this scenario, the elbow method was applied based on the calculated inertia of each of the 31 K tested, as shown in Figure 1, thus achieving a better result for each technique.
Inertia charts constructed by using the elbow method for determining the best number of clusters for each approach.
From the attainment of the best K, the k-means model was trained and, from the grouping performed by this technique, we could reach the average similarity between the documents of each group. Those groups that did not make the cutting line of at least 0.5 of average had the group files submitted for creating up to two submodels. As expected, only for TF-IDF technique groupings is there a need to generate submodels to improve performance.
Table 2 shows the average similarity of the groups obtained using the TF-IDF technique, as well as the result of the Word2Vec CBoW pt-BR technique. It achieved a little better measure of similarity than the Word2Vec Skip-gram pt-BR technique; however, the latter achieved its result with a smaller number of groups, which places it, in general, as the best technique.
Model | Submodel 1 | Submodel 2 | Final | |||||
---|---|---|---|---|---|---|---|---|
Type | Groups | Mean | Groups | Mean | Groups | Mean | Groups | Mean |
TF-IDF | 37 | 0.3696 | 43 | 0.4001 | 48 | 0.4002 | 48 | 0.4002 |
Word2Vec CBoW ptBR | 59 | 0.9060 | — | — | 59 | 0.9060 | ||
Word2Vec Skip-gram ptBR | 34 | 0.9044 | — | — |
Mean cosine similarity between all elements of the group. The best results are highlighted in bold.
After the groups were formed, the statistical data resulting from each approach were calculated, as shown in Table 3 and in the comparative graph of distributions between the techniques (Figure 2). The cosine similarity of the group elements to its centroid was used as a metric, showing the proximity of the results between the techniques with Word2Vec and highlighting the technique Word2Vec Skip-gram ptBR for the smaller amount of generated groups.
Type | Groups | Mean | Std. | Min. | 25% | 50% | 75% | Max. |
---|---|---|---|---|---|---|---|---|
TF-IDF | 49 | 0.6241 | 0.1718 | 0.2466 | 0.5021 | 0.5864 | 0.1639 | 0.9644 |
Word2Vec CBoW ptBR | 59 | 0.9475 | 0.0632 | 0.7640 | 0.9352 | 0.9790 | 0.991 | 0.9999 |
Word2Vec Skip-gram ptBR |
Statistics of the cosine similarity of the group elements to the centroids. The best results are highlighted in bold.
Boxplots showing the distributions of the clusters calculated by each technique. The more cohesive the boxes and the less number of outliers, the better.
When comparing the values presented in Tables 2 and 3, it is noteworthy that the results presented in Table 2 are worse in all cases. It is inferable from this observation that the similarity measure calculations shown in Table 2 can reduce the similarity rates since there may be elements in the group positioned on completely opposite sides. From Figure 2, it is also possible to verify that the groupings generated by the Word2Vec technique were more cohesive than those generated by the TF-IDF technique, especially the Word2Vec Skip-gram technique, which created fewer groupings in the range of outliers than Word2Vec CBoW, demonstrating its superiority by allowing fewer groups but maintaining consistent quality and cohesion.
Given the aforesaid, among all the techniques evaluated, the Word2Vec Skip-gram pt-BR technique presented itself as the best option for word embeddings for clustering legal documents of the Ordinary Appeal Interposed type. Although the Word2Vec CBoW pt-BR technique achieves slightly better rates, it stands out from the previous one for reaching a much smaller number of groups.
The result achieved by each approach can be visualized by projecting in two dimensions of the groups formed from the three techniques: (i) TF-IDF; (ii) Word2Vec CBoW pt-BR; and (iii) Word2Vec Skip-gram pt-BR, respectively, presented in Figures 3–5. It is evident in the figures that the groups formed from Word2Vec are much better defined, especially skip-gram, which confirms the findings previously explained in this work.
2D projection of the entire test dataset, showing for each document its corresponding group formed by TF-IDF.
2D projection of the entire test dataset, showing for each document its corresponding group formed by Word2Vec CBoW ptBR.
2D projection of the entire test dataset, showing for each document its corresponding group formed by Word2Vec skip-gram ptBR.
The use of AI as a standard detection tool based on documents from the judiciary has generally proved to be a viable and helpful solution in the scientific, technological, and practice of legal work. In this chapter, it was possible to present the results considered very promising due to the improvement in the average similarity rate. Thus, we demonstrate the possibility of using word-embedding generation techniques applied on clustering of Ordinary Appeal Interposed using AI algorithms.
Of all the techniques evaluated, the Word2Vec Skip-gram pt-BR technique presented itself as the best option for word embeddings for clustering legal documents of the Ordinary Appeal Interposed type.
We believe that specialized word embeddings have great potential in improving the results. Therefore, comes the suggestion for future study of Word2Vec specialized for the judiciary, in addition to evaluating whether the new embeddings generated provide an opportunity to improve the overall performance of clustering. In addition, using transformer-based techniques, such as BERT, can achieve promising results, using both the Portuguese language word-embedding model and training a specialized BERT model for the judiciary.
Moreover, new possibilities arise for using the techniques discussed in this chapter, such as the draft generation of decisions and classification of documents and processes.
The authors thank the Regional Labour Court of the 5th Region for making datasets available to the scientific community and contributing to research and technological development. The authors also thank the Artificial Intelligence Reference Centre and the Supercomputing Centre for Industrial Innovation, both from SENAI CIMATEC.
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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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VR provides a 3D and dynamic view of structures and the ability of the user to interact with them. The recent technological advances in haptics, display systems, and motion detection allow the user to have a realistic and interactive experience, enabling VR to be ideal for training in hands-on procedures. Consequently, surgical and other interventional procedures are the main fields of application of VR. AR provides the ability of projecting virtual information and structures over physical objects, thus enhancing or altering the real environment. The integration of AR applications in the understanding of anatomical structures and physiological mechanisms seems to be beneficial. Studies have tried to demonstrate the validity and educational effect of many VR and AR applications, in many different areas, employed via various hardware platforms. Some of them even propose a curriculum that integrates these methods. This chapter provides a brief history of VR and AR in medicine, as well as the principles and standards of their function. Finally, the studies that show the effect of the implementation of these methods in different fields of medical training are summarized and presented.",book:{id:"6211",slug:"medical-and-surgical-education-past-present-and-future",title:"Medical and Surgical Education",fullTitle:"Medical and Surgical Education - Past, Present and Future"},signatures:"Panteleimon Pantelidis, Angeliki Chorti, Ioanna Papagiouvanni,\nGeorgios Paparoidamis, Christos Drosos, Thrasyvoulos\nPanagiotakopoulos, Georgios Lales and Michail Sideris",authors:[{id:"211650",title:"M.D.",name:"Panteleimon",middleName:null,surname:"Pantelidis",slug:"panteleimon-pantelidis",fullName:"Panteleimon Pantelidis"},{id:"211654",title:"Ms.",name:"Angeliki",middleName:null,surname:"Chorti",slug:"angeliki-chorti",fullName:"Angeliki Chorti"},{id:"220557",title:"Ms.",name:"Ioanna",middleName:null,surname:"Papagiouvanni",slug:"ioanna-papagiouvanni",fullName:"Ioanna Papagiouvanni"},{id:"220558",title:"Mr.",name:"Georgios",middleName:null,surname:"Paparoidamis",slug:"georgios-paparoidamis",fullName:"Georgios Paparoidamis"},{id:"220559",title:"Mr.",name:"Georgios",middleName:null,surname:"Lales",slug:"georgios-lales",fullName:"Georgios Lales"},{id:"220560",title:"Mr.",name:"Thrasyvoulos",middleName:null,surname:"Panagiotakopoulos",slug:"thrasyvoulos-panagiotakopoulos",fullName:"Thrasyvoulos Panagiotakopoulos"},{id:"220561",title:"Mr.",name:"Christos",middleName:null,surname:"Drosos",slug:"christos-drosos",fullName:"Christos Drosos"},{id:"220562",title:"Dr.",name:"Michail",middleName:null,surname:"Sideris",slug:"michail-sideris",fullName:"Michail Sideris"}]},{id:"50915",doi:"10.5772/63266",title:"Doped Bioactive Glass Materials in Bone Regeneration",slug:"doped-bioactive-glass-materials-in-bone-regeneration",totalDownloads:3485,totalCrossrefCites:13,totalDimensionsCites:34,abstract:"In the arena of orthopaedic surgery, autograft is considered to be the gold standard for correction of fracture repair or other bone pathologies. But, it has some limitations such as donor site morbidity and shortage of supply, which evolved the use of allograft that also has some disadvantages such as immunogenic response to the host, low osteogenicity as well as possibilities of disease transmission. Despite the benefits of autografts and allografts, the limitations of each have necessitated the pursuit of alternatives biomaterials that has the ability to initiate osteogenesis, and the graft should closely mimic the natural bone along with regeneration of fibroblasts. A variety of artificial materials such as demineralised bone matrix, coralline hydroxyapatite and calcium phosphate-based ceramics such as hydroxyapatite (HA), β-tricalcium phosphate (β-TCP) and bioactive glass have been used over the decades to fill bone defects almost without associated soft tissue development. Most of them were having only the properties of osteointegration and osteoconduction. Only bioactive glass possesses osteogenic property that stimulates proliferation and differentiation of osteoprogenitor cells and in some cases influencing the fibroblastic properties. But, this material has also some disadvantages such as short-term and low mechanical strength along with decreased fracture resistance; but, this was further minimised by ion doping that positively enhanced new bone formation. There are many metal ions such as magnesium (Mg), strontium (Sr), manganese (Mn), iron (Fe), zinc (Zn), silver (Ag) and some rare earths that have been doped successfully into bioactive glass to enhance their mechanical and biological properties. In some of the cases, mesoporous bioactive glass materials with or without such doping have also been employed (with homogeneous distribution of pores in the size ranging between 2 and 50 nm). These biomaterials can be served as scaffold for bone regeneration with adequate mechanical properties to restore bone defects and facilitate healing process by regeneration of soft tissues as well. This chapter encompasses the use of bioactive glass in bulk and mesoporous form with doped therapeutic ions, their role in bone tissue regeneration, use as delivery of growth factors as well as coating material for orthopaedic implants.",book:{id:"5164",slug:"advanced-techniques-in-bone-regeneration",title:"Advanced Techniques in Bone Regeneration",fullTitle:"Advanced Techniques in Bone Regeneration"},signatures:"Samit Kumar Nandi, Arnab Mahato, Biswanath Kundu and Prasenjit\nMukherjee",authors:[{id:"60514",title:"Dr.",name:"Samit",middleName:null,surname:"Nandi",slug:"samit-nandi",fullName:"Samit Nandi"}]},{id:"37120",doi:"10.5772/29607",title:"Trigeminocardiac Reflex in Neurosurgery - Current Knowledge and Prospects",slug:"the-trigeminocardiac-reflex-in-neurosurgery-current-knowledge-and-prospects",totalDownloads:3425,totalCrossrefCites:10,totalDimensionsCites:27,abstract:null,book:{id:"749",slug:"explicative-cases-of-controversial-issues-in-neurosurgery",title:"Explicative Cases of Controversial Issues in Neurosurgery",fullTitle:"Explicative Cases of Controversial Issues in Neurosurgery"},signatures:"Amr Abdulazim, Martin N. Stienen, Pooyan Sadr-Eshkevari, Nora Prochnow, Nora Sandu, Benham Bohluli and Bernhard Schaller",authors:[{id:"78171",title:"Prof.",name:"Bernhard",middleName:null,surname:"Schaller",slug:"bernhard-schaller",fullName:"Bernhard Schaller"},{id:"78525",title:"Mr.",name:"Amr",middleName:null,surname:"Abdulazim",slug:"amr-abdulazim",fullName:"Amr Abdulazim"},{id:"78530",title:"Dr",name:"Pooyan",middleName:null,surname:"Sadr-Eshkevari",slug:"pooyan-sadr-eshkevari",fullName:"Pooyan Sadr-Eshkevari"},{id:"126039",title:"Dr.",name:"Martin",middleName:"Nikolaus",surname:"Stienen",slug:"martin-stienen",fullName:"Martin Stienen"},{id:"126040",title:"Dr.",name:"Nora",middleName:null,surname:"Prochnow",slug:"nora-prochnow",fullName:"Nora Prochnow"},{id:"126041",title:"Dr.",name:"Benham",middleName:null,surname:"Bohluli",slug:"benham-bohluli",fullName:"Benham Bohluli"}]},{id:"26559",doi:"10.5772/28833",title:"Local Antibiotic Therapy in the Treatment of Bone and Soft Tissue Infections",slug:"local-antibiotic-therapy-in-the-treatment-of-bone-and-soft-tissue-infections",totalDownloads:6554,totalCrossrefCites:5,totalDimensionsCites:21,abstract:null,book:{id:"784",slug:"selected-topics-in-plastic-reconstructive-surgery",title:"Selected Topics in Plastic Reconstructive Surgery",fullTitle:"Selected Topics in Plastic Reconstructive Surgery"},signatures:"Stefanos Tsourvakas",authors:[{id:"75532",title:"Dr.",name:"Stefanos",middleName:null,surname:"Tsourvakas",slug:"stefanos-tsourvakas",fullName:"Stefanos Tsourvakas"}]}],mostDownloadedChaptersLast30Days:[{id:"65467",title:"Anesthesia Management for Large-Volume Liposuction",slug:"anesthesia-management-for-large-volume-liposuction",totalDownloads:5965,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The apparent easiness with which liposuction is performed favors that patients, young surgeons, and anesthesiologists without experience in this field ignore the many events that occur during this procedure. Liposuction is a procedure to improve the body contour and not a surgery to reduce weight, although recently people who have failed in their plans to lose weight look at liposuction as a means to contour their body figure. Tumescent liposuction of large volumes requires a meticulous selection of each patient; their preoperative evaluation and perioperative management are essential to obtain the expected results. The various techniques of general anesthesia are the most recommended and should be monitored in the usual way, as well as monitoring the total doses of infiltrated local anesthetics to avoid systemic toxicity. The management of intravenous fluids is controversial, but the current trend is the restricted use of hydrosaline solutions. The most feared complications are deep vein thrombosis, pulmonary thromboembolism, fat embolism, lung edema, hypothermia, infections and even death. The adherence to the management guidelines and prophylaxis of venous thrombosis/thromboembolism is mandatory.",book:{id:"6221",slug:"anesthesia-topics-for-plastic-and-reconstructive-surgery",title:"Anesthesia Topics for Plastic and Reconstructive Surgery",fullTitle:"Anesthesia Topics for Plastic and Reconstructive Surgery"},signatures:"Sergio Granados-Tinajero, Carlos Buenrostro-Vásquez, Cecilia\nCárdenas-Maytorena and Marcela Contreras-López",authors:[{id:"273532",title:"Dr.",name:"Sergio Octavio",middleName:null,surname:"Granados Tinajero",slug:"sergio-octavio-granados-tinajero",fullName:"Sergio Octavio Granados Tinajero"}]},{id:"42855",title:"Critical Care Issues After Major Hepatic Surgery",slug:"critical-care-issues-after-major-hepatic-surgery",totalDownloads:8909,totalCrossrefCites:2,totalDimensionsCites:2,abstract:null,book:{id:"3164",slug:"hepatic-surgery",title:"Hepatic Surgery",fullTitle:"Hepatic Surgery"},signatures:"Ashok Thorat and Wei-Chen Lee",authors:[{id:"52360",title:"Prof.",name:"Wei-Chen",middleName:null,surname:"Lee",slug:"wei-chen-lee",fullName:"Wei-Chen Lee"},{id:"157213",title:"Dr.",name:"Ashok",middleName:null,surname:"Thorat",slug:"ashok-thorat",fullName:"Ashok Thorat"}]},{id:"72175",title:"Fontan Operation: A Comprehensive Review",slug:"fontan-operation-a-comprehensive-review",totalDownloads:1252,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Since the first description of the Fontan operation in the early 1970s, a number of modifications have been introduced and currently staged, total cavopulmonary connection with fenestration has become the most commonly used multistage surgery in diverting the vena caval blood flow into the lungs. The existing ventricle, whether it is left or right, is utilized to supply systemic circuit. During Stage I, palliative surgery is performed, usually at presentation in the neonatal period/early infancy, on the basis of pathophysiology of the cardiac defect. During Stage II, a bidirectional Glenn procedure is undertaken in which the superior vena caval flow is diverted into the lungs at an approximate age of 6 months. During Stage IIIA, the blood flow from the inferior vena cava (IVC) is rerouted into the pulmonary arteries, typically by an extra-cardiac conduit along with a fenestration, generally around 2 years of age. During Stage IIIB, the fenestration is closed by transcatheter methodology 6–12 months after Stage IIIA. The evolution of Fontan concepts, the indications for Fontan surgery, and the results of old and current types of Fontan operation form the focus of this review.",book:{id:"9585",slug:"advances-in-complex-valvular-disease",title:"Advances in Complex Valvular Disease",fullTitle:"Advances in Complex Valvular Disease"},signatures:"P. Syamasundar Rao",authors:[{id:"68531",title:"Dr.",name:"P. Syamasundar",middleName:null,surname:"Rao",slug:"p.-syamasundar-rao",fullName:"P. 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Interventions meant to correct these conditions are commonly based on symmetrical models of appearance and do not take into account asymmetric organ weight distribution, asymmetries of respiratory mechanics, and dominant movement patterns that are reinforced in daily functional activities. A model of innate, human asymmetry derived from the theoretical framework of the Postural Restoration Institute® (PRI) explicitly describes the physiological, biomechanical, and respiratory components of human asymmetry. This model is important because it gives an accurate baseline for understanding predisposing factors for the development of postural disorders, which, without intervention, will likely progress to structural dysfunction. Clinical tests to evaluate tri-planar musculoskeletal relationships and function, developed by PRI, are based on this asymmetric model. These tests are valuable for assessing patient’s status in the context of human asymmetry and in guiding appropriate exercise prescription and progression. Balancing musculoskeletal asymmetry is the aim of PRI treatment. Restoration of relative balance decreases pain, restores improved alignment, and strengthens appropriate muscle function. It can also halt the progression of dysfunction and improve respiration, quality of life, and appearance. PRI’s extensive body of targeted exercise progressions are highly effective due to their basis in the tri-planar asymmetric human model.",book:{id:"5816",slug:"innovations-in-spinal-deformities-and-postural-disorders",title:"Innovations in Spinal Deformities and Postural Disorders",fullTitle:"Innovations in Spinal Deformities and Postural Disorders"},signatures:"Susan Henning, Lisa C. 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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. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of 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:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{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:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{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:"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:"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:"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:"Igor Victorovich Lakhno 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.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in 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 been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), 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 a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). 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: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",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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