Thermo-physical properties of the
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
Global and regional field magnetotelluric (MT) and geomagnetic deep sounding (GDS) results revealed that there existed many high electrical conductivity layers (HCL) in various geotectonic units in the deep Earth’s interior (the magnitude of electrical conductivity range is 10−2–100 S/m) [1, 2]. To investigate the cause of all of these available high conductivity layers, it is crucial to measure the electric transport properties of minerals and rocks at certain high-temperature and high-pressure conditions. As one of the crucial physical parameters of minerals, electrical conductivity (EC) is highly sensitive to temperature, pressure, and depth, which is strongly dependent on the physical and chemical environments in the deep Earth and other planetary interiors [3, 4, 5]. In particular, EC is dependent on several factors such as diffusion coefficients of alkali ion [6, 7], trace elemental contents [8], the spin transition of the electron [9, 10], anisotropic crystal orientation [11, 12, 13, 14, 15], contents of water and other volatile elements [16, 17, 18], partial melting [19, 20, 21], dehydration (or dehydrogenation) effects of minerals [22, 23, 24], impurity of high-conductivity phase [25, 26], salinity-bearing (or water-bearing) fluids [27, 28], and structural phase transformation (amorphization or metallization) [29, 30, 31, 32, 33, 34].
In the recent 20 years, with the development of measuring techniques and experimental methods of electrical conductivity in the
As we know, previously available classic “Pyrolite” mineralogical models have already confirmed that the nominally anhydrous minerals (NAMs, e.g., olivine, pyroxene, and garnet) are dominant mineralogical composition in the upper mantle of the deep Earth interior. In light of the FTIR result, these NAMs can contain a certain amount of structural water rather than absolutely “dry.” Whereas, it is general that the trace structural water in NAMs stably exists as a form of hydroxyl point defect of the crystalline site in these minerals. Due to the presence of trace structural water in NAMs, many physical and chemical properties of NAMs have been thoroughly changed accordingly, such as electrical conductivity [3, 8, 13, 14, 15, 16, 17, 18, 35], diffusivity [36, 37], plastic deformation [38, 39], seismic wave attenuation [40, 41], grain growth [42, 43], and kinetic recrystallization [44, 45]. In the world, by virtue of the theoretical calculations of Nernst-Einstein equation between the electrical conductivity and coefficient in mineral, Professor Shun-ichiro Karato from the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States firstly brought forward the viewpoint that the trace structural water in hydrous olivine can enhance several orders of magnitude in the EC of upper-mantle mineral, which can be used to reasonably explain the observed high conductivity anomaly in the region of asthenosphere [46]. In the following 20 years, as a research hotpoint in the field of solid Earth science, a large amount of research work of electrical conductivity of minerals and rocks from the laboratory high-pressure experiments and theoretical calculations investigated have been conducted to focus on this hypothesis of water for the NAMS in the upper-mantle zone (olivine: [8, 13, 14, 15, 47, 48, 49, 50], pyroxene: [51], and garnet [52, 53, 54]). In the year 1998, it is first that Xu Yousheng from the Bayerisches Geoinstitut, University of Bayreuth, Germany fetched in AC electrical impedance spectroscopy (EIS) technique and applied it to report a series of electrical conductivity of minerals, such as olivine, orthopyroxene, and garnet of the upper mantle; wadsleyite of mantle transition zone; as well as the silicate perovskite of the lower mantle under conditions of high temperatures and high pressures in the multi-anvil high-pressure apparatus [55, 56, 57, 58, 59, 60]. Generally, to explore the effect of water on the electrical conductivity, we need to obtain a series of starting materials of hydrous either hot-pressure sintering synthetic or natural hydrous samples. Then, at a fixed temperature and pressure condition, we can measure the electrical conductivity of hydrous minerals. Further, the functional relationship between the EC and water content can be established at HP and HT conditions, thereby providing constraints of the water content in the deep Earth’s and planetary interiors.
In this chapter, we reviewed some recent progress in the electrical conductivity of the main NAMs in the region of the upper mantle, that is, olivine, pyroxene, and garnet at conditions of high temperatures and high pressures. Then, some experimental methods, measurement techniques, and electrical transport conductions on the electrical conductivity of minerals are summarized in the multi-anvil high-pressure apparatus. The newest progress in the recently reported conductivity measurements is outlined in detail. Finally, some comprehensive remarks on the mineral electrical conductivity are discussed.
The electrochemical
Here, Zr stands for the real part of complex impedance spectroscopy, Zi stands for the imaginary part of complex impedance spectroscopy, |Z| stands for the magnitude of complex impedance spectroscopy, and φ stands for the phase angle of complex impedance spectroscopy. Representative complex impedance spectra for natural eclogite from the Dabie-Sulu ultrahigh-pressure metamorphic belt of eastern China are shown in Figure 1.
Representative complex impedance spectra for natural eclogite from Dabie-Sulu ultrahigh-pressure metamorphic belt of eastern China at conditions of 3.0 GPa, 873 K–1173 K and frequency range of 10−1–106 Hz (reproduced with permission from Dai et al., Geochem. Geophys. Geosyst.; published by American Geophysical Union, 2016 [
Detailed description of measurement theory and experimental method of impedance spectroscopy are given in our previous review chapter [9]. The equivalent electric circuit was selected to fit the impedance spectroscopy of the sample, which is composed of some fundamental electronic elements (e.g., resistor, capacitor, inductor, constant phase element (CPE), Gerischer element, Warburg element, etc.) [65, 66, 67, 68]. After that, the electrical conductivity of the sample was obtained by the sample resistance, the calculating formula is expressed as,
In here,
In here,
In the recent several years, many researchers developed the high-pressure electrical property experiments of minerals and rocks by virtue of various high-pressure experimental apparatuses. From lower to higher pressure conditions, some typical high-pressure apparatuses on the laboratory-based electrical conductivity measurements are mainly included autoclave, piston-cylinder, multi-anvil press, and diamond anvil cell. In this counterpart, we focus on two types of multi-anvil apparatuses—(i) YJ-3000 t multi-anvil press is equipped in the Key Laboratory of High-temperature and High-pressure Study of the Earth’s Interior (HTHPSEI), Institute of Geochemistry, Chinese Academy of Sciences, the People’s Republic of China and (ii) Kawai-1000 t multi-anvil Press is equipped in the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States.
Early on half a century ago, Xie Hongsen and his coworkers successfully set up one multi-anvil press of the YJ-3000 t in the Key Laboratory of HTHPSEI, Chinese Academy of Sciences, People’s Republic of China. All of these available high-pressure measurement methods including the direct current, single frequency
High-pressure conductivity measurement platform and experimental setup in the YJ-3000 t multi-anvil press is equipped in the Key Laboratory of High-temperature and High-pressure Study of the Earth’s interior (HTHPSEI), Institute of Geochemistry, Chinese Academy of Sciences, the People’s Republic of China. (a) The YJ-3000 t multi-anvil apparatus; (b) the Solartron-1260 and Solarton-1296 interface impedance spectroscopy analyzer operating in the two-electrodes configuration for complex EIS measurements in the frequency range 10−4 Hz–107 Hz; (c) the vertex-70v vacuum Fourier-transform infrared spectroscopy (FT-IR) analyzer to check the water content of sample.
In addition to the
A representative high-pressure EC experimental assemblage in the Kawai-1000 t multi-anvil press installed in the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States [3, 8, 13, 14, 47, 48, 49, 51, 52] is displayed in Figure 3. Eight cubic WC anvils (it is corresponding to each edge length 26 × 26 × 26 mm3) with the 3–18 mm truncation was adopted to provide a high-temperature and high-pressure quasi-hydrostatic environment. Pressure calibrations were performed by pressure-induced structural phase transformations of some representative semiconducting materials (e.g., pure metallic bismuth, ZnTe, GaP, GaAs, etc.) at atmospheric pressure, the structural phase transformation between α-quartz, β-quartz, coesite, and stishovite, and as well as phase transitions between olivine, wadsleyite, ringwoodite, and bridgmanite. The pressure-transmitting medium is made of pure or Cr2O3-doped or Co-doped octahedral magnesium oxide. Two symmetric metallic discs are employed as electrodes of electrical conductivity measurement. A layer of metallic foil was installed to remove the signal disturbance of the measurement electric circuit and environmental noise. As usual, the ordinary heater, such as stainless steel slice, graphite, tantalum slice, and rhenium slice, is reasonably selected on the base of target temperature during a given electrical conductivity measurement. The temperature in the sample chamber is precisely measured by the B-type Pt70%Rh30%-Pt94%Rh6% thermocouple or K-type thermocouple. The experimental errors of pressure and temperature are not more than 0.5 GPa and 10 K, respectively.
Sample assembly for the electrical conductivity measurement of minerals and rocks in the Kawai-1000 t multi-anvil press from Shun-ichiro Karato’s group from the Karato high-pressure laboratory, Department of Earth and Planetary Sciences, Yale University, United States: (1) metallic Mo ring; (2) MgO octahedral pressure medium with its edge length of 14 mm; (3) sample; (4) zirconia; (5) Al2O3 cement; (6) electric grounding; (7) lead wire of metallic electrode and Al2O3 insulation tube; (8) insulation tube made of four hole alumina; (9) heater of lanthanum chromite; (10) two symmetric buffer electrodes; (11) MgO insulation tube; (12) metallic shielding case made of Ni, Fe, Re or Mo foil; and (13) thermocouple and Al2O3 insulation tube.
As one crucial cycling of deep Earth interior at the depth range from 80 km to 410 km, the upper mantle mainly contains three main dominant rock-forming minerals, that is olivine, pyroxene, and garnet. Previous available high-pressure experimental results have already confirmed that the electrical conductivity of upper-mantle minerals is highly sensitive to all of these influence ingredients, such as high temperature, high pressure, oxygen partial pressure, trace structural water, iron-bearing content, grain boundary state, graphite layer of grain boundary, magnetite-bearing impurity, titanium-bearing content, chromite-bearing content, sulfur-bearing content, the orientation of crystallographic axis, and partial melting, which is most concerned in the recent several years. Because the crucial effect of water content on EC of minerals and rocks in these representative regions of upper-mantle and mantle transition was neglected by other research groups, in the following paragraph, we mainly pay attention to some related results from Dai Lidong’s group from the high-pressure conductivity results from the Key Laboratory of High-temperature and High-pressure Study of the Earth’s Interior (HTHPSEI), Institute of Geochemistry, Chinese Academy of Sciences, the People’s Republic of China, and as well as Shun-ichiro Karato’s group from the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States.
As a major rock-forming silicate mineral and nominally anhydrous mineral, olivine occupies ∼60% of the volume proportion of upper-mantle minerals. Therefore, most of the previously available profile between the electrical conductivity and depth was successfully constructed by the laboratory-based electrical conductivity of olivine data at high-temperature and high-pressure conditions. Electrical conductivities on those of natural olivine single crystal, polycrystalline olivine aggregates, and hot-pressed sintered synthetic olivine have already been performed, especially considering the effects of pressure, oxygen partial pressure, iron-bearing content, and orientation of crystallographic axis on the EC of hydrous olivine.
In an early 1990 year, Roberts and Tyburczy [62] reported the room-pressure electrical conductivity of polycrystalline olivine aggregates using impedance electrochemical impedance spectroscopy (EIS) method under conditions of frequency of 10−4–104 Hz and 1073 K–1673 K. They discussed the influence of anisotropic thermal expansion-induced porosity and microfracture on the high-temperature electrical conductivity of polycrystalline olivine aggregates. At their measured olivine aggregates with the volume percentage of 2–8% inter- and intra-granular porosity and microfracturing, its effect becomes very feeble for the total electrical conductivity of olivine. In light of their findings, subsequent investigations on the electrical properties of upper-mantle polycrystalline samples do not need to consider the effects of porosity and microcracking. The previous investigation of pressure influence on the EC of olivine single crystal was reported by Xu et al. [55] using the EIS method in the Kawai-1000 t multi-anvil press. Detailed experimental conditions are controlled by pressures of 4–10 GPa, temperatures of 1273–1673 K, frequencies of 10−1–106 Hz, and as well as the solid oxygen buffer of molybdenum and molybdenum dioxide. The acquired pre-exponential factor (
The first research work for the effect of water on the EC of upper-mantle olivine was found by Karato [46] on the basis of the theoretical calculating of Nernst-Einstein equation, who brought forward that the trace structural water of nominally hydrous minerals plays a vital role in the EC of olivine in the upper mantle. To check this theoretical calculating hypothesis, the EC of hydrous synthetic polycrystalline olivine compacts was firstly conducted by Wang et al. [17] in Shun-ichiro Karato’s group from the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, the United States at conditions of a pressure of 4.0 GPa, temperature ranges from 873 K to 1273 K and water content ranges from 100 ppm wt% to 800 ppm wt%, using the Kawai-1000 t multi-anvil press and the Solarton-1260 EIS analyzer (Schlumberger, Houston, TX, USA). It is the first time that the ionized reaction model in hydrous synthetic polycrystalline olivine compacts is originated from the free proton-dominated conduction mechanism, as follows,
where, in the Kröger-Vink notation,
Furthermore, a series of hydrous electrical conductivity measurements on single-crystal olivine with different crystallographic orientations, synthetic polycrystalline olivine compacts, and synthetic hot-pressed polycrystalline olivine aggregates have been extensively studied by virtue of Kawai-1000 t multi-anvil press and the EIS methods in recent several years [8, 13, 14, 47, 48, 49]. Figure 4 shows the influences of temperature, pressure, oxygen fugacity, iron-bearing content and water-bearing content on hydrous synthetic polycrystalline olivine compacts, and hydrous hot-pressed synthetic polycrystalline olivine aggregates at the temperature ranges from 873 K to 1273 K and pressure ranges from 4.0 GPa to 10.0 GPa.
The effects of (a) water content on the EC of XFe (Fe/(Fe + Mg)) = 41.2% hydrous hot-pressed synthetic polycrystalline olivine aggregates, (b) pressure on the EC of hydrous synthetic polycrystalline olivine compacts, (c) oxygen fugacity on the EC of hydrous synthetic polycrystalline olivine compacts, and (d) water-bearing content and temperature on the EC of XFe (Fe/(Fe + Mg)) = 41.2% hydrous hot-pressed synthetic polycrystalline olivine aggregates at the temperature ranges from 873 K to 1273 K and pressure ranges from 4.0 GPa to 10.0 GPa. Three oxygen buffers including Ni-NiO, Mo-MoO2, and Re-ReO2 were selected to oxygen fugacity during the process of high-pressure electrical conductivity measurements (reproduced with permission from Dai and Karato, Phys. Earth Planet. Inter.; published by Elsevier, 2009 [
For hydrous hot-pressed synthetic polycrystalline olivine aggregates with fixed iron-bearing content, XFe (= (Fe/(Fe + Mg))) = 41.2% (molar ratio percentage), the electrical conductivity of the sample increases with the rise of water contents from 45 ppm wt% to 620 ppm wt%, and one relatively a fixed activation enthalpy (Δ
On the other hand, Dai and Karato [13, 14] also measured the EC of hydrous San Carlos single-crystal olivine along with [001, 010, 100] three different crystallographic orientations at conditions of 573–1373 K and 4.0 GPa, as illustrated in Figure 5 in details. It is clearly observed that at lower temperatures range from 573 K to 900 K, one relatively feeble anisotropic EC in hydrous San Carlos single-crystal olivine with a lower activation enthalpy value (Δ
The influence of anisotropy on the EC of hydrous San Carlos single crystal olivine along [001, 010, 100] three different crystallographic orientations at conditions of 573–1373 K and 4.0 GPa. Three different average schemes of series (
In comprehensive considerations of geophysical field observations and geochemical models, the acquired EC results revealed that the high and highly anisotropic EC at the corresponding asthenospheric temperature and pressure conditions is reasonably explained by the high-water content in the region of the asthenosphere (100 ppm). On the other hand, the influence of the interconnected high conductive impurity phases (graphite, magnetite, chromite, sulfide impurity, etc.) and saline fluids (e.g., Ol-NaCl-H2O, Ol-KCl-H2O, Ol-CaCl2-H2O, etc.) on the EC of olivine has been also explored in details [25, 26, 27, 28, 105, 106, 107].
Except for high-pressure experimental measurements on the electrical conductivity of hydrous olivine, some important progress from the first-principles calculations based on density functional theory (DFT) has already been performed in order to deeply explore the microscopic electrical transport conduction within an atomic scale in the deep Earth interior. Recently, He et al. [15] firstly designed several point defect models (e.g.,
The extrapolated proton and total conductivities of Fe-bearing hydrous olivine as the function of inverse temperature and water content compared with experimental results [
As a secondary rock-forming mineral in the upper mantle, the volume percentage of pyroxene is close to the average content of 20–40% in a typical upper-mantle region. Thus, high-pressure electrical conductivity measurement on pyroxene is crucial to deeply explore the high conductivity anomalies in the regions of the upper mantle and mantle asthenosphere. For anhydrous clinopyroxene, Dai et al. [111] measured EC of dry single-crystal diopside along with [001, 010, 100] three main crystallographic orientations at 1.0–4.0 GPa, 1073–1373 K, and Ni-NiO oxygen buffer-controlled oxygen fugacity in the YJ-3000 t multi-anvil apparatus and the Solartron-1260 impedance spectroscopy analyzer. A feeble influence of anisotropy on the electrical conductivity of dry single-crystal diopside was observed. Similarly, the influence of oxygen fugacity on the electrical conductivity of dry orthopyroxene single crystal by Dai et al. [112] was performed at 1.0–4.0 GPa, 1073–1423 K, and different oxygen fugacities. The oxygen fugacity in the sample chamber of EC measurements is controlled by four solid buffers of nickel and nickel oxide, iron and magnetite, and as well as molybdenum and molybdenum dioxide. A positive dependence of oxygen fugacity on the EC of orthopyroxene single crystal is observed at a given temperature range from 1073 K to 1423 K and 2.0 GPa, which is highly related to the conduction mechanism of small polaron hopping in the anhydrous iron-bearing silicate mineral at high pressure.
On the other hand, EC measurements of anhydrous and hydrous orthopyroxene single crystals along with three main crystallographic orientations were performed by Dai and Karato [51] at conditions of 8 GPa, temperatures of 873–1273 K, and as well as the oxygen fugacity controlled by the molybdenum and molybdenum dioxide using the Kawai-1000 t multi-anvil apparatus and the Solartron-1260 impedance spectroscopy analyzer. Detailed experimental results were illustrated in Figure 7. According to the FT-IR spectroscopy results, the hydrous orthopyroxene single crystals contain 420 ppm wt% water. They found that trace structural water can enhance several orders of magnitude in the EC of orthopyroxene single crystals. The effect from the anisotropy of hydrous orthopyroxene on the EC of the sample can be neglected, which is consistent with the above-mentioned dry single-crystal diopside. As two main conduction mechanisms, small polaron and free proton play a crucial role in the electrical conductivity of anhydrous and hydrous orthopyroxene single crystals at high-temperature and high-pressure conditions.
The influence of anisotropy on the electrical conductivity of hydrous single-crystal orthopyroxene along [001, 010, 100] crystallographic orientations at conditions of 8 GPa, temperatures of 873–1273 K, and as well as the oxygen fugacity controlled by the molybdenum and molybdenum dioxide. The EC of dry single-crystal orthopyroxene is also included (modified from Dai and Karato [
In the recent days, Sun et al. [27] performed electrical conductivities of the polycrystalline clinopyroxene aggregates + sodium chloride + water system under conditions of 1 GPa, 673–973 K, the various salinity degree of fluid (5, 10, 15, 20, and 25%), and the volume fraction of fluid (5, 10, 15, 20, and 25%) using the YJ-3000 t multi-anvil apparatus and the Solartron-1260 impedance spectroscopy analyzer. They found that the electrical conductivity of polycrystalline clinopyroxene aggregates containing the salinity-bearing fluid with a certain salinity degree and volume fraction of fluid can be applied to explain the unusually high conductivities in some regional geotectonic units, such as southern Tibetan plateau, Dabie orogen, Grenville province, and central New Zealand.
As a type of important constituent silicate mineral, garnet can stably exist over a wide depth from lower crust Earth to the topmost lower mantle. With increasing temperature and pressure in the deep Earth interiors, the form of pyrope-rich garnet in the lower Earth crust gradually transforms into the high-pressure phase of majorite garnet in the mantle transition zone, which is of the complex chemical composition and stable crystalline structure. Furthermore, the mineralogical content of garnet in the deep Earth interiors will gradually increase with the rise of depth [113]. Therefore, when we try to construct the profile between electrical conductivity and depth in the deep mantle of Earth’s interior, it is indispensable to comprehensively assess the influence of electrical conductivity of garnet at high temperatures and high pressures.
Dai and Karato [52] conducted the EC measurements of anhydrous and water-rich single-crystal pyrope-rich garnet [its corresponding chemical composition is close to 73 mole% of pyrope (Py), 14 mole% of almandine (Alm), and 13 mole% of grossular (Gr)] at conditions of temperature range from 873 K to 1473 K, pressure ranges from 4 GPa to 16 GPa, frequency ranges from 10−2 Hz to 106 Hz and water-bearing content range from less than 10 H/106Si to 7000 H/106Si using the Kawai-1000 t multi-anvil press installed in the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States. Figure 8 shows the effect of pressure on the dry and water-bearing (160 ppm wt) single crystal pyrope-rich garnet at temperatures of 873–1473 K. In comparisons with water-free water of pyrope-rich garnet, the electrical conductivity of the hydrous sample is higher, and the activation enthalpy of pyrope-rich garnet (Δ
The influence of pressure on the dry and water-bearing (160 ppm wt) single crystal pyrope-rich garnet at temperatures of 873–1473 K (reproduced with permission from Dai and Karato, Phys. Earth planet. Inter.; published by Elsevier, 2009 [
In subsequent investigations, a series of EC experiments on garnet with different chemical compositions (Py20Alm76Grs4, Py30Alm67Grs3, Py56Alm43Grs1, Py73Alm14Grs13, and Alm82Py15Grs3) were conducted by Dai et al. [53, 54] using the YJ-3000 t multi-anvil apparatus and the Solartron-1260 impedance spectroscopy analyzer to explore the influences of oxygen fugacity and mineralogical composition on the EC of garnet at 1.0–4.0 GPa and 873–1273 K. Five different solid buffers including Fe3O4-Fe2O3, Ni-NiO, Fe-Fe3O4, Fe-FeO, and Mo-MoO2 were employed to control the oxygen fugacity of the high-pressure sample chamber. Based on these obtained experimental results, some absolutely new models of electrical conductivity of garnet series as functions of the variation of oxygen fugacity and chemical compositions have already been constructed, as illustrated in Figure 9(a) in detail. Furthermore, in conjunction with previously available electrical conductivity of clinopyroxene results, the average models of rock and as well as the regionally geothermal gradient, the profile between the electrical conductivity of eclogite and depth is established from the EC of garnet series at high temperatures and high pressures, as shown in Figure 9(b) in detail. All of these acquired laboratory-based electrical conductivity profiles can be widely applied to systematically disclose and deeply explore the cause of the observable high conductivity anomalies from global and regional field magnetotelluric and geomagnetic deep sounding results in those representatively geotectonic units, for example, stable mid-lower continental Earth crust, Dabie-Sulu ultrahigh-pressure metamorphic belt of eastern China, Tibet plateau, and North China craton.
(a) The influence of volume percentage on the electrical conductivity of eclogite on the base on our obtained electrical conductivity results for single crystal pyrope-rich garnet at high temperatures and high pressures (modified from Dai et al. [
High-pressure laboratory-based measurement results on electrical properties of several dominant minerals (olivine, orthopyroxene, clinopyroxene, garnet, etc.) in the upper mantle regions are highly sensitive to some influential factors including temperature, pressure, oxygen fugacity, water content, crystallographic orientation, trace element of titanium content and iron content. The proper determination of EC of upper-mantle minerals in a broad temperature and pressure range requires the utilization of complex electrical electrochemical impedance spectroscopy measurements. The iron-related hopping of small polaron and the hydrogen-related defects are possibly the two dominant conduction mechanisms in anhydrous and hydrous Fe-bearing silicate minerals within the depth range of the upper mantle. The trace structural water of mantle minerals plays a crucial role in explaining the high conductivity anomaly and the water distribution in the deep mantle. In comprehensive considerations of some newest results from high-pressure laboratory-based conductivity measurements, geophysical field observations, and first-principles theoretical calculations, high and highly anisotropic EC at the corresponding asthenospheric temperature and pressure conditions can be used to reasonably explain by the 100 ppm wt% of water content for the high conductivity anomaly in the asthenospheric region. In addition, the influence of the interconnected high conductive impurity phases (graphite, magnetite, chromite, sulfide impurity, etc.) and saline fluids (e.g., Ol-NaCl-H2O, Ol-KCl-H2O, Ol-CaCl2-H2O, etc.) on the EC of olivine also needed to be considered in a special geotectonic environment. With the development and advancement of measurement techniques and experimental methods, more and more laboratory-based high-pressure conductivity experimental results will be indispensable to systematically disclose the cycle discontinuity and high conductivity anomaly in the upper-mantle region.
Almost all listed original high-pressure electrical conductivity data have already been obtained by Lidong Dai and his collaborators in the Chinese Academy of Sciences and Yale University. In here, please accept my best honest thanks and greetings to Professor Heping Li in the Key Laboratory of High-temperature and High-pressure Study of the Earth’s Interior (HTHPSEI), Institute of Geochemistry, Chinese Academy of Sciences, the People’s Republic of China, and Professor Shun-ichiro Karato in the Karato High-pressure Laboratory, Department of Earth and Planetary Sciences, Yale University, United States. This work was financially supported by the National Natural Science Foundation of China (Grant numbers 42072055, 41774099, and 41772042), Youth Innovation Promotion Association of Chinese Academy of Sciences (Grant number 2019390), Special Fund of the West Light Foundation of Chinese Academy of Sciences and as well as Special Fund from Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection.
The thermal energy storage systems can be classified into several main groups, namely thermochemical storage, sensible heat storage and latent heat storage, or combination of these both [1]. The energy is stored in the latent heat storage systems regarding the phase change of materials as a constant or nearly constant temperature. It is critically reviewed in several recent publications that the PCM thermal storage system is one of the most efficient heat storage methods because it provides a considerable amount of energy during the charging and discharging process compared to that of the conventional sensible heat energy storage.
This means that the latent heat storage system using a PCM requires a much smaller volume of materials to store a certain amount of energy. Recently, several studies have been carried out to study the thermal behaviour of PCM thermal storage systems using FLUENT. Al-Maghalseh [2] conducted a comprehensive review of the heat transfer enhancement methods in the thermal storage systems using PCM. Several techniques of heat transfer intensification methods were discussed in details, including both experimental and numerical studies for fins, fins materials and geometry, filling materials, nano-fluid, nano-particles, microencapsulation and thermal conductivity enhancement. Another study by the same author [3] numerically studied the effects of natural convection on the heat transfer of the PCM thermal storage system. He found that the natural convection has a considerable effect on the heat transfer inside the storage system, and therefore reducing the total melting time of the PCM. A detailed review of energy storage using PCMs has been performed in [4, 5, 6, 7, 8] as well as Perez-Raya [9], Joybari [10], Riahi [11], Kozak [12], Liu [13], and Mario [14].
Jian et al. [15] developed a numerical model to predict the transient thermal behaviour during charging and discharging processes of a latent thermal storage unit involving a triplex concentric tube with the PCM filling the middle channel Freeman et al. [16] carried out some investigations into a small-scale solar organic ranking cycle (ORC) with integrated PCM thermal energy storage(TES) unit. The system was examined for selected months in the contrasting climates of Cyprus and the UK. The performance indicator of the ORC engine and the required TES volume with and without the PCM are compared and discussed. It was found that the system with evacuated flat-plate collectors has a better performance compared with using low-cost evacuated-tube heat-pipe collectors. Furthermore, using PCMs for the TES shown better performance and a smaller equivalent storage volume than water.
Dal Magro et al. [17] used the PCM to improve the efficiency of the ORC system operating under thermal power fluctuations. He found that using the PCM allows the capacity factor to increase from 38–52% and the average thermal efficiency to increase from 15.5% to 16.4%. Sagar et al. [18] developed a numerical model for ORC based solar thermal power plant integrated with latent heat thermal energy storage system. Shell and tube latent heat thermal storage system was designed to generate 200 kW during the discharging process. However, the overall performance of the solar thermal power plant was evaluated for ten days of operation. Another study by Manfrida et al. [19] developed a simulation model for PCM thermal storage system coupled with solar-powered ORC. The study examined the thermal performance of the system over several conditions. Further, a case study for the operating of the system during one week was numerically examined. The results clearly showed that the system we able to provide power in 78.5% of the time, with weekly averaged efficiencies of 13.4% for the ORC unit, and 3.9% for the whole plant. A simple numerical method, called, the temperature and thermal resistance iterations, was used in the numerical calculation. The data from the numerical model was then compared with experimental results, and a good agreement was observed. Ho and Chen [20] also developed a numerical model for the melting of ice around a horizontal isothermal cylinder. The model’s results were compared with experimental data published by White in [21], and a good agreement was found. It was concluded that the melting process of ice is strongly affected by the changing recirculation occurring in the molten water. Another numerical model of melting around a horizontal pipe was developed by Rieger et al. [22]. The numerical solution was obtained for Rayleigh numbers (Ra) up to 1.5 × 105, Stefan numbers in the range of 0.005 ≤ Ste ≤0.08, and for Pr=50. It was found that the natural convection is the dominant process in the heat transfer mechanism throughout almost the entire melting process.
Trp studied the transient heat transfer in the shell-and-tube thermal storage system in an experimental and numerical study [23, 24]. He developed a mathematical model based on the non-isothermal phase transition, and it was implemented as a FORTRAN computer code. The numerical results were validated with experimented data, and it was concluded that heat transfer from the HTF to the PCM was low due to the large Prandtl numbers of the HTF. Therefore, a large amount of heat was carried downstream with the HTF, whilst a small amount of heat was transferred to the PCM upstream. The same author [25] numerically investigated the effects of several geometrical parameters and different HTF operational conditions on heat transfer during both melting and solidification processes by measuring the transient temperature distribution of the HTF, PCM and tube wall.
This chapter presents the results of the 3-D CFD modelling of the PCM in the shell-and-tube thermal storage system. Then, the numerical results obtained by the CFD were compared with the experimental and numerical data from the literature. Finally, a detailed parametric study of heat transfer processes in the melting PCM was carried out and results were discussed.
Lacroix [26], conducted a series of experiments to study the heat transfer performance of the shell-and-tube thermal storage unit using PCM. The PCM installed on the shell side, while the Heat Transfer Fluid (HTF) flowing inside the tube. The effects of several thermal and geometric parameters on the heat process were investigated. The schematic diagram of the model is presented in Figure 1. The PCM fills the shell with the diameter of De, whereas the HTF flows through the tube with a diameter of Di. The PCM is commercially available material
Schematic representation of the test unit.
Properties | Value |
---|---|
Liquid density, | 814 kg/m3 |
Solid density, | 814 kg/m3 |
Liquid Thermal conductivity, | 0.148 W/(moC) |
Solid Thermal conductivity, | 0.358 W/(moC) |
Liquid specific heat, | 2200 J/Kg oC |
Solid specific heat, | 1900 J/Kg oC |
Latent heat, | 243.5 KJ/Kg |
Viscosity, | |
Thermal expansion coefficient, | 0.00091 1/K |
Melting Temperature, | 300.7 K |
Figure 2 illustrates the test unit scheme. Two concentric tubes were used. The inner tube (Di = 12.7 mm, Do = 15.8 mm, and L = 1 m) is made of copper, and outside tube (Di = 25.8 mm, and L = 1 m) is made of Plexiglas. Thick pipe insulation (Rubates Armstrong Armflex II) was used to isolate the system. The space between two tubes was filled with
Experimental test unit.
For the validation purpose, Lacroix’s experiments were numerically restudied using the ANSYS FLUENT software. In the preliminary simulations, different grid sizes and time steps were carefully examined to obtain computational grid convergence. The computational grid was constructed using 282504 hexahedral elements and boundary layers were used surrounding the pipe. Transient simulations were run using the
An enthalpy-porosity method is used for modelling the solidification/melting process [33]. This technique is described in detail by Voller and Prakash [32, 34].
The energy conservation equation for this case is written as:
The enthalpy of the material is calculated as the sum of the sensible heat,
The sensible heat is calculated as:
The latent heat is also calculated as:
The liquid fraction,
The solid and liquid temperatures are also calculated as
The source term in the momentum equation can be written as [33]:
Due to Darcy’s law damping terms as a source term are added to the momentum equation because of the effect of phase change on convection, whereas
The liquid velocity can be calculated by the following Equation [33]:
The validation of the CFD model was carried out by comparing numerical results from ANSYS FLUENT to experimental data obtained by Lacroix [26]. The comparison was carried out for three different cases during the melting process. These are for three different HTF inlet temperatures above the melting temperature of
The variation of the predicted and experimental temperature at locations T1 and T2 (Tin = Tm + 20 K), the mass flow rate = 0.0315 kg/s.
The variation of the predicted and experimental temperature at locations T1 and T2 (Tin = Tm + 10 K), the mass flow rate = 0.0315 kg/s.
The variation of the predicted and experimental temperature at locations T1 and T2 (Tin = Tm + 5 K), the mass flow rate = 0.0315 kg/s.
The CFD liquid fraction variation in time against the numerical liquid fraction from [
Temperature distribution in the PCM for (A) bottom section plane, (B) middle section plane, and (C) top section plane (Tin = 320 K, HTF mass flow rate = 0.0315Kg/s., the elapsed time is 350 sec).
PCM temperature versus time at the different radial positions: R = 0.001, 0.002, and 0.004 m from the axis of the computational domain at y = 0.5 m, Tin = 320 K, HTF mass flow rate = 0.0315Kg/s.
PCM temperature versus time along the axis (y = 0.05, 0.49, and 0.95 m) for the radial distances z = 0.001 and 0.004 m, Tin = 320 K, HTF mass flow rate = 0.0315Kg/s.
The heat transfer performance of the storage unit was numerically examined during the charging process. Figure 7 shows the numerical results of the temperature and liquid fractions along the storage unit axis during the charging process when the elapsed time is 350 seconds (the inlet temperature of the HTF is 320 K, the mass flow rate is 0.0315 Kg/s).
It can be seen that the highest temperature of the PCM can be observed at the domain’s top region close to the inlet of the HTF and can rise gradually in the regions closed to at the vicinity of the tube walls. Therefore, the top part of the domain converted into a liquid first and later on, melting expands to lower regions on the domain. Figure 8, illustrates the temperature variation in the PCM for three different radial locations. As expected, the higher temperatures are noticed in the regions close to the surface of the wall with the HTF, where the melting process takes place first. The temperature variations along the axis at the outer surface of the HTF tube are shown in Figure 9. It can be seen that higher temperatures exist at the domain’s top. This is mainly because the HTF flows from the top to the bottom and thus the temperature rises faster at the vicinity of the tube walls close to the inlet. At this period, the effect of natural convection is not profound yet.
The effect of HTF inlet temperature on the melting process is demonstrated in Figure 10. It can be seen that the inlet temperature of the HTF considerably affects the rate of the melting and the PCM temperature distribution. The increase in the inlet temperature of the HTF leads to a rise in the temperature difference between the tube walls and the bulk of the PCM and thus enhance the heat transfer rate. It results in the faster rise of the liquid fraction and decreases the total melting time. Figure 11 shows that the time for completion of melting for the HTF inlet temperature of 305 K is 2974 s, for 310 K this time is reduced to 1617s and finally, for 320 K, the time of melting is only 963 s. Therefore, the total melting time is reduced approximately by 68% when the inlet temperature is increased from 305 to 320 K and by 45.6% when the inlet temperature is increased from 305 to 310 K.
Effect of the HTF inlet temperature on the melting process: (A) location T1 (h = 0.51 m, r = 0.002 m) and (B) location T2 (h = 0.95 m, r = 0.001 m), Tintial = 282.5 K, HTF mass flow rate = 0.0315Kg/s.
The effect of the inlet temperature of the HTF on the formation of the liquid fraction of the PCM and on the melting time.
Figure 12 demonstrates the effect of the HTF flow rate on the meting process. It can be seen that the flow rate accelerates the melting process due to the increased heat transfer rate. It can be seen from Figure 13 that when the flow rate increases from 0.000315 to 0.00315 kg/s, the PCM melting time is reduced from 2781 to 1173 s (reduction by 57%). Also, the melting time is reduced by 17.8% when the mass flow rate increases from 0.00315 to 0.0315 kg/s. The effect of the HTF mass flow rate rise is less profound when compared with the effect of the rise in the HTF inlet temperature. This is demonstrated in Figure 14 using temperature distribution counters and the PCM fluid fraction evolution diagrams.
Effect of the HTF flow rate on the melting process. The PCM temperature at locations (A) T1 (h = 0.51 m, r = 0.002 m) and (B) T2 (h = 0.95 m, r = 0.001 m), Tintial = 282.5 K, HTF mass flow rate = 0.0315Kg/s.
The effect of the HTF flow rate on the liquid fraction formation and the melting time, Tintial = 282.5 K, Tinlet = 320 K.
Temperature distribution and melting process at the bottom of the computational domain for the different HTF mass flow rates and inlet temperatures (elapsed time is 850 s).
To study the effect of the natural convection, the system needs to be installed in the horizontal position. Several points were created inside the computational domain to monitor the variation of the temperature inside the PCM during numerical CFD modelling. These monitoring points are placed in three planes, which are perpendicular to the axis of the system and located at distances of 0.07, 0.51 and 0.95 m from the front of the system (see Figure 1). Figure 15 indicates the locations of monitoring points in the plane at a distance of 0.07 m from the front of the system.
Locations of the monitoring points around the pipe.
Figure 16 shows the temperature variation at some of the monitoring points at the front plane of the system with the PCM for the case when the inlet temperature of the HTF is 320.7 K and the mass flow rate is 0.0315 kg/s. It can be seen that initially, the temperature increases rapidly from 280 to 299 K due to the heat transfer from the pipe walls to the solid PCM by conduction. The temperatures at monitoring points u1, r1, and b1 rise more rapidly due to their proximity to the pipe. Temperatures in monitoring points u3, r3, and b3 rise considerable slower since these points are located on the edge of the storage unit. During the melting process the temperature increases from 299 to 300.7 K and equalise at all monitoring points. Initially, during the melting process, a thin liquid layer is formed between the pipe and the solid PCM. Gradually, the solid–liquid interface expands in the axial and radial directions and the melting then is dominated by natural convection in the PCM’s liquid regions. The melting process is intensified in the upper regions of the container, resulting in higher temperature recordings at the top of the computational domain (point u1).
Variation of the base PCM temperature with time.
The velocity vectors in the liquid pure PCM are shown in Figure 17 for the elapsed time of 300, 400 and 550 s. It can be seen that the molten PCM ascends upwards from the top regions at the centre of the unit and then after cooling flows downwards to complete the natural convection circle. The convection is intensified as the liquid fraction volume increases. The velocity magnitude gradually decreases in time due to a reduction in the temperature difference in the molten PCM. These results are in good agreement with the results of several experimental and numerical investigations [35, 36, 37].
Velocity vectors in pure PCM.
The local heat transfer coefficient could not be estimated accurately for the present thermal storage system as there is a temperature difference between the outer surface of the HTF pipe and the PCM along with both axial and radial directions. Consequently, the average heat transfer coefficient for the melting process is calculated instead using the following Equations [38] for the temporal heat transfer coefficient
where the surface area of HTFP is calculated using the following equation:
The heat transfer rate (
where,
The average heat transfer coefficient is:
where Tu1 and Tu2 are the PCM temperature at points u1 (first measurement plane) and u2 (last measurement plane); Tw1 and Tw2 are the pipe wall temperatures at the same corresponding measurement planes.
Finally, the temporal Nusselt number (
The time-averaged Nusselt number
Different cases were analysed with various system’s geometrical and thermophysical parameters. To generalise results it is vital to characterise them in the dimensionless form. More details about the dimensional parameters calculation and analysis can be found at [32].
The heat transfer coefficient values were calculated for the top, side and bottom regions of the storage unit for several cases during the melting processes.. Figure 18 present results on the heat transfer coefficient changing as a function of time for the PCM during the case when the inlet temperature of the HTF is 320.7 K and the mass flow rate is 0.0315 kg/s. It can be seen that the heat transfer coefficients for both top and side regions increase with time. This agrees well with the initial solid PCM’s temperature rise followed by the melting process. The higher heat transfer coefficient values can be observed in the top regions of the system. This can be attributed to the effect of natural convection. For the longer elapsed times, the heat transfer value stabilises. This is because the temperature distribution becomes more established within the PCM body when the system reaches the steady-state operation. At this stage, the majority of the PCM is melted and a very small part of it, which is close to the bottom regions of the unit, maybe in the solid-state.
The heat transfer coefficient variation at the top, side, and bottom regions of the system.
The average heat transfer coefficient values for various HTF inlet temperature and flow rates were obtained and used to calculate the Nusselt number for the pure PCM. The flow rates of the HTF considered were 0.000315, 0.00315, 0.005, and 0.0063 Kg/s. The HTF inlet temperatures used in the modelling are 305, 310, and 320 K. To derive generic heat transfer correlations, the Nusselt numbers were calculated at the top, side and bottom regions of the storage unit. Thereafter, the average Nusselt number value for all regions is calculated. Figures 19-21 show the results of these CFD modelling. As it can be seen in Figure 19, the Nusselt number increases with the rise in the Stefan number (see Eq. 18) which is proportional to the difference between the inlet temperature of the HTF and the melting temperature of the PCM (the increase in the inlet temperature of HTF increases the Stefan number). Similar observations can be made concerning the Rayleigh number (see Eq. 19), see Figure 20. The Rayleigh number is also proportional to the difference between the inlet temperature of the HTF and the melting temperature of the PCM.
Variation in the Nusselt number as a function of Stefan number for (A) upper regions (B) side regions (C) bottom regions (D) the average Nusselt number (in the system).
Variation in the Nusselt number as a function of Rayleigh number for (A) upper regions (B) side regions (C) bottom regions (D) the average Nusselt number (in the system).
Variation in the Nusselt number as a function of Fourier number for (A) upper regions (B) side regions (C) bottom regions (D) the average Nusselt number (in the system).
Figure 21 show that the Nusselt number decreases with the rise in the Fourier number (see Eq. 20). The Fourier number is proportional to the melting time. However, an increase in the inlet temperature and flow rate of the HTF will lead to a decrease in the total melting time, and thus, reduce the Fourier number.
The average Nusselt numbers in the system are presented in Figure 19D,20D, and 21D. This data was used to derive the Nusselt number correlations for the pure PCM. The correlations are derived as a function of the Stefan, Fourier, and Rayleigh numbers.
The Nusselt number for the pure PCM is (
The correlation between the numerically obtained and calculated using Eqs. (20) is shown in Figure 22. It can be seen that the Nusselt number varies between 2 and 4.3 for the system under investigations. The lower Nusselt numbers are for the low HTF inlet temperature, and the high Nusselt numbers values are obtained at the high HTF inlet temperatures. The total melting time of the PCM can then be calculated using the following formula (R2 = 0.966)
The correlation between numerically obtained and calculated using
The 3D CFD simulation model was developed for shell-and-tube thermal storage system using FLUENT/ANSYS. For the validation purpose, the results from the numerical model were compared with the experimental results of Lacroix [26]. The results show that a considerable agreement between the numerical and experimental results. Therefore, the results demonstrate that the developed CFD model accurately describes processes taking place in the experimental test rig and therefore can be used with confidence for further transient heat transfer simulations in the shell-and-tube latent thermal storage unit. 3D CFD simulations were performed for a range of the HTF inlet temperature values and its mass flow rate. Thereby, the results were used to derive Nusselt number correlations as a function of Stefan, Rayleigh and Fourier numbers to take into account the effect of all design and operational conditions. The Nusselt number for the system with the pure PCM was found to be (
The total melting time of the PCM can be estimated by (R2 = 0.966)
Liquid
Effective
Fluid
Inlet
Melting
Outlet
Solid
Total
Wall
Density (
Coefficient of thermal expansion (1/K)
Viscosity (
Liquid fraction
Permeability
Volumetric fraction
A | Area (m2) |
AMush | Mushy zone constant |
CP | Specific heat (J/KgoC) |
D | Diameter |
g | Gravitational acceleration |
Gr | Grashof number |
h | Sensible enthalpy (J) |
H | Enthalpy |
H | Height |
href | Reference enthalpy |
k | Thermal conductivity (W/moC) |
keff | Effective thermal conductivity |
L | Latent heat (Kj/Kg) |
L | Length |
LMTD | The logarithmic mean temperature difference |
m | Mass in each space (Kg) |
ṁ | Mass flow rate |
Nu | Nusselt number |
Pr | Prandtl number |
q | Heat transfer rate |
r | Radial coordinate |
Ra | Rayleigh number |
Re | Reynolds number |
S | Source term |
Sh | The heat from any volumetric sources |
Sm | Mass source |
Ste. | Stefan number |
t | Time (s) |
T | Temperature |
Tref | Reference temperature |
u | X velocity components (m/s) |
v | Y velocity components (m/s) |
vn | Normal components of the velocity of the interface |
vr | Radial velocity component |
vx | Axial velocity component |
w | Z velocity components (m/s) |
W | Width |
x | Axial coordinate |
YM | Contribution of the fluctuating dilatation incompressible turbulence to The overall dissipation rate |
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. 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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. 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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Laptops, tablets, smartphones and wearable devices are the major source of this digital data transformation and are becoming the core part of our daily life. As a result of this transformation, we are becoming the soft target of various types of cybercrimes. Digital forensic investigation provides the way to recover lost or purposefully deleted or hidden files from a suspect’s device. However, current man power and government resources are not enough to investigate the cybercrimes. Unfortunately, existing digital investigation procedures and practices require huge interaction with humans; as a result it slows down the process with the pace digital crimes are committed. Machine learning (ML) is the branch of science that has governs from the field of AI. This advance technology uses the explicit programming to depict the human-like behaviour. Machine learning combined with automation in digital investigation process at different stages of investigation has significant potential to aid digital investigators. This chapter aims at providing the research in machine learning-based digital forensic investigation, identifies the gaps, addresses the challenges and open issues in this field.",book:{id:"8321",slug:"digital-forensic-science",title:"Digital Forensic Science",fullTitle:"Digital Forensic Science"},signatures:"Salman Iqbal and Soltan Abed Alharbi",authors:[{id:"304135",title:"Dr.",name:"Salman",middleName:null,surname:"Iqbal",slug:"salman-iqbal",fullName:"Salman Iqbal"},{id:"304140",title:"Dr.",name:"Soltan",middleName:null,surname:"Alharbi",slug:"soltan-alharbi",fullName:"Soltan Alharbi"}]}],onlineFirstChaptersFilter:{topicId:"91",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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He received his Ph.D. in Molecular Biology with his thesis “Genetic variability of the tick-borne encephalitis virus in natural foci of Novosibirsk city and its suburbs.” His primary field is molecular virology with research emphasis on vector-borne viruses, especially tick-borne encephalitis virus, Kemerovo virus and Omsk hemorrhagic fever virus, rabies virus, molecular genetics, biology, and epidemiology of virus pathogens.",institutionString:"Russian Academy of Sciences",institution:{name:"Russian Academy of Sciences",country:{name:"Russia"}}},{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.png",biography:"László Babinszky is Professor Emeritus, Department of Animal Nutrition Physiology, University of Debrecen, Hungary. He has also worked in the Department of Animal Nutrition, University of Wageningen, Netherlands; the Institute for Livestock Feeding and Nutrition (IVVO), Lelystad, Netherlands; the Agricultural University of Vienna (BOKU); the Institute for Animal Breeding and Nutrition, Austria; and the Oscar Kellner Research Institute for Animal Nutrition, Rostock, Germany. In 1992, Dr. Babinszky obtained a Ph.D. in Animal Nutrition from the University of Wageningen. His main research areas are swine and poultry nutrition. He has authored more than 300 publications (papers, book chapters) and edited four books and fourteen international conference proceedings.",institutionString:"University of Debrecen",institution:{name:"University of Debrecen",country:{name:"Hungary"}}},{id:"201830",title:"Dr.",name:"Fernando",middleName:"Sanchez",surname:"Davila",slug:"fernando-davila",fullName:"Fernando Davila",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/201830/images/5017_n.jpg",biography:"I am a professor at UANL since 1988. My research lines are the development of reproductive techniques in small ruminants. We also conducted research on sexual and social behavior in males.\nI am Mexican and study my professional career as an engineer in agriculture and animal science at UANL. Then take a masters degree in science in Germany (Animal breeding). Take a doctorate in animal science at the UANL.",institutionString:null,institution:{name:"Universidad Autónoma de Nuevo León",country:{name:"Mexico"}}},{id:"309250",title:"Dr.",name:"Miguel",middleName:null,surname:"Quaresma",slug:"miguel-quaresma",fullName:"Miguel Quaresma",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309250/images/9059_n.jpg",biography:"Miguel Nuno Pinheiro Quaresma was born on May 26, 1974 in Dili, Timor Island. He is married with two children: a boy and a girl, and he is a resident in Vila Real, Portugal. He graduated in Veterinary Medicine in August 1998 and obtained his Ph.D. degree in Veterinary Sciences -Clinical Area in February 2015, both from the University of Trás-os-Montes e Alto Douro. He is currently enrolled in the Alternative Residency of the European College of Animal Reproduction. He works as a Senior Clinician at the Veterinary Teaching Hospital of UTAD (HVUTAD) with a role in clinical activity in the area of livestock and equine species as well as to support teaching and research in related areas. He teaches as an Invited Professor in Reproduction Medicine I and II of the Master\\'s in Veterinary Medicine degree at UTAD. Currently, he holds the position of Chairman of the Portuguese Buiatrics Association. He is a member of the Consultive Group on Production Animals of the OMV. He has 19 publications in indexed international journals (ISIS), as well as over 60 publications and oral presentations in both Portuguese and international journals and congresses.",institutionString:"University of Trás-os-Montes and Alto Douro",institution:{name:"University of Trás-os-Montes and Alto Douro",country:{name:"Portugal"}}},{id:"38652",title:"Dr.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",country:{name:"Portugal"}}},{id:"283019",title:"Dr.",name:"Oudessa",middleName:null,surname:"Kerro Dego",slug:"oudessa-kerro-dego",fullName:"Oudessa Kerro Dego",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/283019/images/system/283019.png",biography:"Dr. Kerro Dego is a veterinary microbiologist with training in veterinary medicine, microbiology, and anatomic pathology. Dr. Kerro Dego is an assistant professor of dairy health in the department of animal science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. He received his D.V.M. (1997), M.S. (2002), and Ph.D. (2008) degrees in Veterinary Medicine, Animal Pathology and Veterinary Microbiology from College of Veterinary Medicine, Addis Ababa University, Ethiopia; College of Veterinary Medicine, Utrecht University, the Netherlands and Western College of Veterinary Medicine, University of Saskatchewan, Canada respectively. He did his Postdoctoral training in microbial pathogenesis (2009 - 2015) in the Department of Animal Science, the University of Tennessee, Institute of Agriculture, Knoxville, Tennessee. Dr. Kerro Dego’s research focuses on the prevention and control of infectious diseases of farm animals, particularly mastitis, improving dairy food safety, and mitigation of antimicrobial resistance. Dr. Kerro Dego has extensive experience in studying the pathogenesis of bacterial infections, identification of virulence factors, and vaccine development and efficacy testing against major bacterial mastitis pathogens. Dr. Kerro Dego conducted numerous controlled experimental and field vaccine efficacy studies, vaccination, and evaluation of immunological responses in several species of animals, including rodents (mice) and large animals (bovine and ovine).",institutionString:"University of Tennessee at Knoxville",institution:{name:"University of Tennessee at Knoxville",country:{name:"United States of America"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón",slug:"juan-carlos-gardon",fullName:"Juan Carlos Gardón",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",biography:"Juan Carlos Gardón Poggi received University degree from the Faculty of Agrarian Science in Argentina, in 1983. Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. Routinely, he supervises students preparing their doctoral, master thesis or final degree projects.",institutionString:"Catholic University of Valencia San Vicente Mártir, Spain",institution:null},{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/125292/images/system/125292.jpeg",biography:"Katy Satué Ambrojo received her Veterinary Medicine degree, Master degree in Equine Technology and doctorate in Veterinary Medicine from the Faculty of Veterinary, CEU-Cardenal Herrera University in Valencia, Spain. She is a Full Professor at the Department of Medicine and Animal Surgery at the same University. She developed her research activity in the field of Endocrinology, Hematology, Biochemistry and Immunology of horses. She is a scientific reviewer of several international journals : American Journal of Obstetrics and Gynecology, Comparative Clinical Pathology, Veterinary Clinical Pathology, Journal of Equine Veterinary Science, Reproduction in Domestic Animals, Research Veterinary Science, Brazilian Journal of Medical and Biological Research, Livestock Production Science and Theriogenology. Since 2014, she has been the Head of the Clinical Analysis Laboratory of the Hospital Clínico Veterinario from the Faculty of Veterinary, CEU-Cardenal Herrera University.",institutionString:"CEU-Cardenal Herrera University",institution:{name:"CEU Cardinal Herrera University",country:{name:"Spain"}}},{id:"309529",title:"Dr.",name:"Albert",middleName:null,surname:"Rizvanov",slug:"albert-rizvanov",fullName:"Albert Rizvanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/309529/images/9189_n.jpg",biography:'Albert A. Rizvanov is a Professor and Director of the Center for Precision and Regenerative Medicine at the Institute of Fundamental Medicine and Biology, Kazan Federal University (KFU), Russia. He is the Head of the Center of Excellence “Regenerative Medicine” and Vice-Director of Strategic Academic Unit \\"Translational 7P Medicine\\". Albert completed his Ph.D. at the University of Nevada, Reno, USA and Dr.Sci. at KFU. He is a corresponding member of the Tatarstan Academy of Sciences, Russian Federation. Albert is an author of more than 300 peer-reviewed journal articles and 22 patents. He has supervised 11 Ph.D. and 2 Dr.Sci. dissertations. Albert is the Head of the Dissertation Committee on Biochemistry, Microbiology, and Genetics at KFU.\nORCID https://orcid.org/0000-0002-9427-5739\nWebsite https://kpfu.ru/Albert.Rizvanov?p_lang=2',institutionString:"Kazan Federal University",institution:{name:"Kazan Federal University",country:{name:"Russia"}}},{id:"210551",title:"Dr.",name:"Arbab",middleName:null,surname:"Sikandar",slug:"arbab-sikandar",fullName:"Arbab Sikandar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210551/images/system/210551.jpg",biography:"Dr. Arbab Sikandar, PhD, M. Phil, DVM was born on April 05, 1981. He is currently working at the College of Veterinary & Animal Sciences as an Assistant Professor. He previously worked as a lecturer at the same University. \nHe is a Member/Secretory of Ethics committee (No. CVAS-9377 dated 18-04-18), Member of the QEC committee CVAS, Jhang (Regr/Gen/69/873, dated 26-10-2017), Member, Board of studies of Department of Basic Sciences (No. CVAS. 2851 Dated. 12-04-13, and No. CVAS, 9024 dated 20/11/17), Member of Academic Committee, CVAS, Jhang (No. CVAS/2004, Dated, 25-08-12), Member of the technical committee (No. CVAS/ 4085, dated 20,03, 2010 till 2016).\n\nDr. Arbab Sikandar contributed in five days hands-on-training on Histopathology at the Department of Pathology, UVAS from 12-16 June 2017. He received a Certificate of appreciation for contributions for Popularization of Science and Technology in the Society on 17-11-15. He was the resource person in the lecture series- ‘scientific writing’ at the Department of Anatomy and Histology, UVAS, Lahore on 29th October 2015. He won a full fellowship as a principal candidate for the year 2015 in the field of Agriculture, EICA, Egypt with ref. to the Notification No. 12(11) ACS/Egypt/2014 from 10 July 2015 to 25th September 2015.; he received a grant of Rs. 55000/- as research incentives from Director, Advanced Studies and Research, UVAS, Lahore upon publications of research papers in IF Journals (DR/215, dated 19-5-2014.. He obtained his PhD by winning a HEC Pakistan indigenous Scholarship, ‘Ph.D. fellowship for 5000 scholars – Phase II’ (2av1-147), 17-6/HEC/HRD/IS-II/12, November 15, 2012. \n\nDr. Sikandar is a member of numerous societies: Registered Veterinary Medical Practitioner (life member) and Registered Veterinary Medical Faculty of Pakistan Veterinary Medical Council. The Registration code of PVMC is RVMP/4298 and RVMF/ 0102.; Life member of the University of Veterinary and Animal Sciences, Lahore, Alumni Association with S# 664, dated: 6-4-12. ; Member 'Vets Care Organization Pakistan” with Reference No. VCO-605-149, dated 05-04-06. :Member 'Vet Crescent” (Society of Animal Health and Production), UVAS, Lahore.",institutionString:"University of Veterinary & Animal Science",institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}},{id:"311663",title:"Dr.",name:"Prasanna",middleName:null,surname:"Pal",slug:"prasanna-pal",fullName:"Prasanna Pal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311663/images/13261_n.jpg",biography:null,institutionString:null,institution:{name:"National Dairy Research Institute",country:{name:"India"}}},{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. 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Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. 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We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. 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