Categorization of deep domain adaptation based on whether the labels in the target domain are available.
\r\n\t- Traditionally accepted topics related to global health security,
\r\n\t- The impact of human activities and climate change on “planetary health”,
\r\n\t- The impact of global demographic changes and the emergence chronic health conditions as international health security threats.
\r\n\t- A theme dedicated to the COVID-19 Pandemic,
\r\n\t- Novel considerations, including the impact of social media and more recent technological developments on international health security.
\r\n\tThe goal of this book cycle is to provide a comprehensive compendium that will be able to stand on its own as an authoritative source of information on international health security.
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Miller",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10624.jpg",keywords:"Threats, Monitoring, Food Security, Emerging Infections, Transmission, Geopolitics, Climate Change, Cyber Health Security, COVID-19, Novel Coronavirus, Pandemic, Coronavirus",numberOfDownloads:167,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 20th 2020",dateEndSecondStepPublish:"November 5th 2020",dateEndThirdStepPublish:"January 4th 2021",dateEndFourthStepPublish:"March 25th 2021",dateEndFifthStepPublish:"May 24th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"An Associate Professor of Surgery at Temple University School of Medicine and a Chair of the Department of Research and Innovation, St. Luke's University Health Network. A member of multiple editorial boards and co-author of over 550 publications.",coeditorOneBiosketch:"An Associate Professor of Surgery & Integrative Medicine at Northeast Ohio Medical University and Cardiothoracic Surgeon at the Summa Health Care System. A prolific writer and presenter, with multiple books, hundreds of peer-reviewed articles, and innumerable presentations around the world.",coeditorTwoBiosketch:"A CEO of the INDUSEM Health and Medicine Collaborative, Global Executive Director. of the American College of Academic International Medicine (ACAIM) and head of the World Academic Council of Emergency Medicine.",coeditorThreeBiosketch:"A Director of Research in the Department of Emergency Medicine at Nazareth Hospital in Philadelphia, USA, and co-chief editor of the International Journal of Critical Illness and Injury Science. A recipient of numerous local, regional, and national awards.",coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"181694",title:"Dr.",name:"Stanislaw P.",middleName:null,surname:"Stawicki",slug:"stanislaw-p.-stawicki",fullName:"Stanislaw P. Stawicki",profilePictureURL:"https://mts.intechopen.com/storage/users/181694/images/system/181694.jpeg",biography:"Stanislaw P. Stawicki, MD, MBA, FACS, FAIM, is Chair of the Department of Research of Innovation, St. Luke\\'s University Health Network, Bethlehem, Pennsylvania, and Professor of Surgery at Temple University School of Medicine. Dr. Stawicki has edited numerous books and book series on the topics of clinical research, medical education, medical leadership, patient safety, health security, and various other subjects. He is a member of multiple editorial boards and has co-authored more than 650 publications. 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His academic experience over the last two decades includes consistent achievements and innovative strategies that have led to the creation of organizations, publication of landmark papers, and commendation with prestigious citations and honors for his works that have impacted academic medicine globally. He has played a defining role in founding and building internationally recognized interdisciplinary indexed journals. He is the CEO of the INDUSEM Health and Medicine Collaborative and heads the World Academic Council of Emergency Medicine (WACEM). Additionally, he serves as the Global Executive Director for the American College of Academic International Medicine (ACAIM). 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He completed residencies in Emergency Medicine and Internal Medicine at the State University of New York (SUNY) Downstate Medical Center (2010) where he served as Chief Resident for Research. He completed fellowships in Pulmonary Medicine at the University of Pittsburgh Medical Center (2013) and Critical Care Medicine at the National Institutes of Health (2014). He is active in the American College of Academic International Medicine, and is co-chief editor of the International Journal of Critical Illness and Injury Science. 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There are growing needs for high-performance transparent conducting films (TCFs) with flexibility to realize flexible displays or solar cells. Solution processing of conducting nanomaterials for TCFs has many challenging issues in order to achieve high performance, including the intrinsic properties of the materials, the dispersion of nanomaterials, and interfacial engineering of coating films on plastic substrates. Moreover, to mitigate the drawbacks of each conducting nanomaterial, we need a rational hybridization strategy to achieve the fabrication of high performance TCFs on plastic substrates (Figure 1).
Scheme of hybrid TCFs fabricated with 1D/1D hybrid materials and 1D/2D hybrid materials by solution processing.
Therefore, this chapter describes some of the research on the fabrication of high-performance TCFs based on single-walled CNTs (SWCNTs) and silver nanowires (AgNWs) over the past 8 years that addresses these and other challenges, with an emphasis on our own efforts. We begin with the realization of TCFs with high uniformity by spray deposition and then describe the interfacial engineering of TCFs on plastic substrates. Furthermore, we describe the fabrication of flexible TCFs with 1D/1D hybrid structures and 1D/2D hybrid materials with SWCNTs and AgNWs as 1D materials and graphene oxide as a 2D material. We conclude with some discussion of future directions and the remaining challenges in chemically exfoliated graphene technologies.
Spray coating methods can be used to fabricate flexible TCFs with aqueous single-walled carbon nanotube (SWCNT) solutions or silver nanowire (AgNW) solutions on plastic substrates. As shown in Figure 2, thin films were deposited on the substrate by the atomization of aqueous solution using high-pressure nitrogen gas through a spray nozzle. The gas flow rate, nozzle height, and pitch should be controlled to fabricate uniform films with high opto-electrical performance. As a model system, SWCNT solution dispersed in aqueous surfactant and aqueous AgNW solution containing a small amount (0.01 wt%) of polyvinylpyrrolidone (PVP) were used to investigate the spreading behavior on surface energy-controlled substrates. To control the surface energy of the substrate, plastic substrates were irradiated with UV-ozone (UVO). The wettability of coating inks is critical for fabrication of uniform films by spraying. Figure 3shows the contact angle (CA) change with an increase in UVO irradiation time of polycarbonate substrates. The CA of the SWCNT/surfactant solution decreases from 15 to 10°. The CA of the aqueous AgNW solution decreases from 68.7 to 36.6° with an increasing UVO irradiation time, and the size of the deposited liquid droplet increases from 8 to 13 mm. The nozzle height and the spraying pitch were optimized to 70 and 7 mm, respectively.
Schematic of the automatic spray coating system with mass flow controller, injection pump, and atomizing nozzle. The X- and Y-direction can be controlledautomatically by robotics [6].
Contact angles and spread droplet sizes of (a) the aqueous SWCNT solution dispersed by sodium dodecylbenzene sulfonate and (c) the aqueous AgNW solution containing PVP on polycarbonate substratesby varying the UVO exposure time. The inset photoimages in (a) and (c) show the spread SWCNT and AgNW droplet sizes on the substrate by varying the UVO-irradiation time indicated by values. (b) and (d) Schematics of spreading of the SWCNT (b) and AgNW (d) droplets on substrates [6].
Another way to enhance the uniformity of the films is deposition of hydrophilic graphene oxide (GO) nanosheets onto the substrate. Figure 4 shows the sheet resistance (Rs) distribution of the SWCNT and the AgNW films spray-coated on surface energy-controlled substrates and after deposition of GO nanosheets onto the films. After UVO treatment, the Rs uniformity of AgNW films was dramatically improved and reached 7.2%, resulting in T = 98% and Rs = 100 Ω/sq for the highlyoxidized GO (HOGO)-coated AgNW films.
The sheet resistance (Rs) uniformity of (a–c) the SWCNT films and (d–f) the AgNW films on (a, d) pristine polycarbonate (PC), (b, e) UVO-irradiated PC substrates, and (c, f) after graphene oxide (HOGO) coating of the conducting films fabricated on UVO-treated substrates [6].
In this study, we investigated the effect of the interfacial tension between bare SWCNT network films and a top-coating of passivation materials on the Rs of the film. We demonstrated that the Rs of the SWCNT film can be affected by a thermal expansion coefficient (CTE) mismatch between the substrate and the SWCNT film.
The spray-coated SWCNT films have porous structures on a scale of tens of nanometers. The Rs and transmittance are related by [7].
where σDC and σOp are the DC and optical conductivities, respectively.
The conductivity, σDC, of the disordered nanotube films depends on the number density of the network junctions, Nj, which in turn scales with the network morphology through the film fill-factor, Vf, the mean diameter, <D>, of the bundles, and the mean junction resistance, <RJ> [8, 9, 10, 11],
Here, K is the proportionality factor that scales with the bundle length. Therefore, if we can reduce <RJ> and Vf, the sheet resistance of the SWCNT films can be improved. To realize this, the SWCNT films were coated with silane sols by considering their surface energy. Considering the interfacial tension between the SWCNT film and silane sols, two top-coating materials such as a tetraorthosilicate (TEOS) sol with silanol groups and methyltrimethoxysilane (MTMS) sol with hydrophobic methyl groups were used. It is worth noting that top-coating with TEOS sol unexpectedly decreased the Rs of the film to less than 80% of the Rs of the as-prepared film. However, the Rs values of MTMS sol-coated SWCNT films gradually increased. This large disparity between MTMS and TEOS sols can be explained by a change in the contact resistance between the bundles. Hydrophilic TEOS sol can densify the hydrophobic SWCNT networks, while MTMS sol, having methyl groups, can penetrate the hydrophobic SWCNT networks, resulting in an increase of the contact resistance of SWCNTs. This interfacial tension effect was minimized by deposition of gold chloride solution onto the SWCNT film (Figure 5b) to make it hydrophilic, as shown in Figure 5c.
(a) The Rs versus transmittance plot of SWCNT film deposited by spraying on PET substrates. (b) Wettability of pristine SWCNT film and SWCNT filmdoped with gold chloride. (c) The Rs change of pristine and doped SWCNT films by varying spray coating times of top-coating materials (methyl trimethoxysilane (MTMS) sol, tetraethoxysilane (TEOS) sol) after baking at 80°C for 1 h [12].
Figure 6 shows the Rs change after heating at 130°C and cooling. Bare PET, hard-coated PET, and glass substrates were used to illustrate the CTE mismatch effect on the Rs changes of SWCNT films. Interestingly, the Rs of the SWCNTs on bare PET substrates increased by 40% relative to the initial values, while the Rs increase was suppressed in bare SWCNT films on hard-coated PET and glass. These results imply that the CTE value should be considered in order to obtain highly stable SWCNT TCFs on plastic substrates. To illustrate this phenomenon, a Raman spectroscopic study was performed, and the G+ and G− peak positions related to the strain of SWCNTs were compared. The G-band frequencies for SWCNT films on bare PET were up-shifted by 1–2 cm−1 after heating at 130°C and cooling, which corresponds to a compressive strain of ~0.1%. This compressive strain may cause the increase of the Rs of the SWCNT film on bare PET.
(a, b) The Rs changes of SWCNT films with different transmittance values, after heating at 130°C, as a function of thermal treatment time. (c, d) Scheme of thermal expansion mismatch between the SWCNT layers and bare PET or hard-coated PET after heating and cooling. (e–g) Raman spectra (G band) of SWCNT films fabricated on (e) bare PET, (f) hard-coated PET, and (g) glass after heating at 130°C for 20 min, followed by cooling [12].
Plastic substrates are generally used to fabricate flexible TCFs by deposition of CNTs or metal nanowires. In particular, the electrical properties of SWCNT network films are sensitive to humidity and temperature. In this context, top-coating with passivation materials or hybridization with binder materials are applicable for improving the stability of TCFs. Another way to passivate TCFs is welding or embedding in plastic substrates by chemical or thermal treatments. Figure 7 shows the Rs change of the SWCNT films after deposition of solvents. To investigate the solvent effects, we used solvents with optimal polarity and affinity for the PET substrate. Moreover, the presence of electron-donating and electron-withdrawing groups in the solvent molecules can affect the electronic structure of the SWCNTs. Thus, nonpolar solvents were selected. In particular, aromatic hydrocarbon, benzene, and toluene can swell the PET substrate. Most interestingly, deposition of toluene or benzene decreased the Rs of the SWCNT films. After doping with gold chloride, the Rs and transmittance of the film were measured to be 85 Ω/sq and 90%, respectively. Moreover, I-V plots measured after solvent deposition show clearly that the electrical conductivity of the SWCNT films was enhanced after toluene deposition, which can swell PET substrates.
(a) Rs versus transmittance plots for pristine SWCNT films prepared from a SWNT/SDBS solution on PET surfaces, and after deposition of solvents and dopants. (b) In-situ conductivity measurements of SWCNT films after deposition of toluene and gold chloride. (c) Rs changes of bare SWCNT films in comparison with the same films treated with solvents: toluene (T), benzene (B), hexane (H), and cyclohexane (C) [13].
Figure 8 shows that large SWCNT bundles were welded, and small bundles were embedded on the PET substrate after spraying aromatic hydrocarbons, while spraying cyclohexane did not trigger welding. The strain induced on the SWCNT networks during network formation on the substrate may cause an initial high resistance in the SWCNT network film. Thus, solvent-induced chemical welding of the SWCNT film can release their strain. The recovery of the G band in the Raman spectra of the SWCNT films demonstrates strain relaxation via chemical welding.
Atomic force microscope images of (a) an as-prepared film (99% transmittance at 550 nm), and the film after spraying of (b) cyclohexane and (c) toluene. (d) Height profile of the nanotube bundles indicated by the inverted triangles in (a–c). The left and right images in (a), (b), and (c) are the height and phase images, respectively. Deformed SWCNT bundles are indicated by arrows in (a). The green dotted circles in (c) indicate embedded SWCNT bundles after deposition of toluene because of swelling of PET [13].
Thermal treatment is an alternative way to produce SWCNT film-substrate welding without any chemicals. In particular, fast selective heating of CNTs on plastic substrates can provide an interesting opportunity for thermal welding [14, 15]. Microwaves irradiate the SWCNT films inside the rectangular waveguide microwave applicator, within which the microwave electric field is well defined and controlled. The microwave mode in the applicator is a fundamental transverse electric (TE10) mode (Ez = 0) with a frequency of 2450 MHz, so the microwave electric field (Ey) is sinusoidally distributed along the x- and z-axes and constant along the y-axis. Immediate flash Ohmic heating with an energy conversion of greater than 99% can be realized because the microwave electric field is parallel to the overall SWCNT film and can efficiently induce a fast oscillating current in the film. The amplitude of the conduction current density, Js, induced on the CNT film by the microwave electric field intensity, EMW, may be described as follows [16]:
where σCNT is the electric conductivity of the SWCNT film.
Figure 9a shows the surface temperature and Rs changes of the SWCNT film by varying the irradiation time. The surface temperature of the SWCNT film is dramatically increased after 7 s irradiation at 40 W without heat deflection. Of interest is that the Rs decreased after 7 s of irradiation, due to the occurrence of chemical welding. The Raman spectra in Figure 9e show the strain relaxation of the SWCNT network. The SEM image also shows clearly that the SWCNTs are welded or embedded in the plastic substrate. Importantly, the MW-irradiated SWCNT networks are protected by a self-passivation layer that protects the nanotubes from water molecules. The Rs values of the SWCNT films increase by less than 10% at 80°C and 90% relative humidity, despite embedding of the nanotubes in the plastic substrates.
(a) Measured surface temperatures and Rs changes of SWCNT films on PC substrates as a function of microwave irradiation time. (b) The SWCNT film on PC heated in a conventional heating oven at 150°C. (c) The SWCNT film irradiated with microwaves. (d) Scheme of microwave-irradiated selective heating of CNTs on a plastic substrate, wherein a rapidly oscillating current induced along the CNTs is efficiently generated by the microwave electric field parallel to the SWCNT film. (e) Raman spectra of SWCNT powder and SWCNT films on PC before and after microwave irradiation for 7 s. Inset SEM image shows the microwave-nanowelded SWCNT film on the PC substrate [17].
AgNW-based TCFs are not very environmentally stable without some form of passivation. If the AgNW network can be welded onto a thermoplastic substrate, it can be self-passivated, as was accomplished with SWCNT film. However, the surface tension of AgNWs (~500 mN/m of liquid silver in air) is much different from that of the hydrophobic PC substrate (~34.2 mN/m), which prevents the AgNWs from completely embedding in the plastic substrate, as illustrated in Figure 10. This surface tension mismatch can be solved by deposition of SWCNTs onto the AgNW network because of the low surface tension of CNTs (40–80 mN/m). Therefore, SWCNTs can trigger the migration of AgNWs into plastic substrates by thermal or chemical treatment. Moreover, the high thermal electrical conductivity of the SWCNT can promote the self-passivation of AgNWs by stable Joule heating of the film with an applied DC voltage. Figure 11 shows the surface morphology of the SWCNT-overcoated AgNW film after a voltage of 20 V was applied. In stark contrast to AgNWs in AgNW film shown in Figure 10a, AgNWs were fully embedded in the plastic substrate by electrical heating. Atomic force microscopy (AFM) height profiles also demonstrate the embedding of the AgNW–SWCNT network in the plastic substrate. This self-passivation of AgNW networks assisted by SWCNTs with electrical heating improved the mechanical and hydrothermal stability of the film.
(a) AFM image and (b) height profile of the AgNW film after thermal treatment at 150°C for 3 h on a PC substrate. (c, d) Schematic illustration of the limited migration of AgNW networks into the plastic substrate due to a surface tension mismatch [18].
Field emission SEM images of AgNW overcoated with SWCNTs (a) before and (b) after heating under a current flow of thin film heater. AFM images of the same film (c) before heating and (d) after heating. (e) Height profile of the SWCNT-overcoated AgNW film under a current flow [18].
In terms of the applications of metal nanowire networks, interfacial engineering is an important step to improve their performance with respect to electrical conductivity, environmental stability, surface roughness, and work function modulation. In particular, interfacial engineering of AgNW film can affect the opto-electrical performance because of junction formation in the network. In this study, HOGO nanosheets were utilized for efficient thermal joining of AgNW networks on thermoplastic substrates (Figure 12a). Figure 12b shows the Rs changes of the AgNW network films on bare PC, GO-modified PC, and glass after heating at 150°C with increasing exposure time. The Rs was dramatically reduced by thermal treatment via a junction joining of the networks. Importantly, the Rs decrease of the AgNW film was more efficient on GO-modified PC than on bare PC and glass. Interestingly, the changed Rs of AgNW films on PC was stable even after heating for 180 min, while the Rs of the AgNW film on glass gradually increased, even after 30 min, due to air oxidation. This result provides an opportunity to obtain high-performance AgNW TCFs by a combination of thermal welding and junction joining of AgNW networks. SEM and AFM images in Figure 12 show clearly that on GO nanosheets, limited embedding or welding of AgNWs was observed. This demonstrates the more efficient reduction of Rs of AgNWs on the GO-modified PC.
(a) Scheme showing AgNW film on GO-modified PC. (b) Rs changes of AgNW films on bare PC and GO-modified PC, and on glass after heating at 150°C by varying the exposure time. (c, d) SEM images of AgNW films on (c) bare PC and (d) GO-modified PC substrates after heating at 150°C for 1 h. (e) AFM image of AgNW networks on GO-modified PC. (f) Height profiles of embedded AgNWs and AgNWs floated on the GO nanosheet indicated in (e) as numbers [19].
SWCNT-based TCFs with a low haze value are suitable for highly transparent opto-electronic devices. However, for achievement of a low Rs value of the films, one challenge is the development of an efficient and stable dopant. In addition, their high porosity and hydrophobic surface properties are a drawback as an electrode material in opto-electronic devices. In this context, we introduced easily deformable GO nanosheets containing electron-withdrawing groups on the basal plane and edges, which can give a p-type doping effect on the SWCNT film. Figure 13 shows that the Rs of the SWCNT film can be dramatically reduced by up to 40% compared to the as-prepared SWCNT film by deposition of GO solution onto the film by spraying. The efficiency of Rs reduction depends on the lateral sizes of the GO nanosheets. Small-sized GO nanosheets prepared by decanting the first supernatant (S1) by centrifugation were more efficient than larger GO nanosheets. As shown in Figure 14, the SWCNT bundles are easily wrapped with small GO nanosheets, while larger GO nanosheets can be freestanding between SWCNT networks. This means that densification of the SWCNT network is more efficient using small GO than large GO. The reduction of porosity and junction resistance of the SWCNT network can have a positive effect on the decrease of Rs. Moreover, the effect of p-type doping by GO is clearly shown in Raman spectra (Figure 14c and d). An upshift of 3.5 cm−1 in the G+ band for the semiconducting SWCNTs by small GO nanosheets (S1) demonstrates p-type doping of the SWCNTs from the GO nanosheets via a charge transfer mechanism.
(a) Rs versus transmittance plots of SWCNT films before and after deposition of GO nanosheets. (b) Relative Rs changes of SWCNT films by increasing the number of spray coatings of GO solution obtained by centrifugation (the first to fourth supernatant solutions are denoted as S1 to S4). (c) Relative Rs as a function of the SWCNT film transmittance showing thickness dependence of GO deposition on Rs changes of the film due to contact area change between GO and the SWCNT bundle. (d) The I-V measurement scheme performed on SWCNT films after deposition of the GO solution. (e) Photo image of a gold-patterned SWCNT film and I-V plots for SWCNT films by increasing the amount of deposited GO solution (in the direction of the arrow) [20].
Tilted SEM images of SWCNT surfaces coated with (a) S1-GO nanosheets and (b) S4-GO nanosheets. Inset schemes show the structure of the GO-coated SWCNT networks. (c) Raman spectra of a pristine SWCNT film and films coated with S1, S2, S3, and S4 using a spray-coater 20 times. (d) Raman spectra of SWCNT films coated with S1-GO by increasing the number of coating layers from 5 to 30. Values in brackets in (c) and (d) indicate G+ band position. Scale bars in (a) and (b) are 300 nm [20].
To evaluate the opto-electrical performance of a GO-SWCNT electrode on PET, organic photovoltaic (OPV) cells with a PET/GO-SWCNT/PEDOT-PSS/active layer/LiF/Al structure were fabricated (Figure 15). For fabrication of the layered structure of the OPV cells, the wettability of the electrode on the upper loaded aqueous PEDOT:PSS solution is important. As shown in Figure 15a, the hydrophobic SWCNT film was converted to hydrophilic by deposition of hydrophilic GO nanosheets. Moreover, importantly, the work function of the SWCNT film changed from 4.7 to 5.05 eV by deposition of S1-GO nanosheets, which induces a facile hole injection from the HOMO of P3HT (5.0 eV) to the electrode. The resultant device performance with the GO-modified SWCNT anodes shows a significant enhancement in overall photovoltaic performance compared to devices fabricated on pristine SWCNT electrodes, as shown in Figure 15d.
(a) PEDOT:PSS solution drop images on a, b are SWCNT surface and on the GO-coated area (dotted area). (b) Schematic structure and (c) photo image of OPV cell. (d) Current density (J) versus voltage (V) characteristics of pristine SWCNTs and GO-modified SWCNT photovoltaic cells under 100 mW/cm2 AM 1.5G spectral illumination at various transmittance and Rs values [20].
Under high current flow, metal NW scan be disrupted by Joule heating at the junction due to a relatively high junction resistance between metal NWs. Self-joining of NW network junctions can solve this problem via post-treatment. Another approach is to interconnect the NWs with other conducting materials or metal oxides. For more efficient processing of metal NW-based TCFs, we need to exclude additional steps, such as irradiation with light, heating at high temperatures, and the removal of surfactant molecules after the deposition of AgNWs or AgNW hybrid materials. Thus, we suggest that a small amount of SWCNTs can stabilize the AgNW networks under current flow without post-treatment. To realize this, the major challenge is the fabrication of a stable dispersion of SWCNTs in liquid medium without dispersant molecules that can be removed after deposition. To solve this issue, the SWCNTs were functionalized with quadruple hydrogen bonding (QHB) motifs of 2-ureido-4[1H]pyrimidinone (UHP) moieties through a previously reported sequential coupling reaction [21]. The AgNW/SWCNT mixture solution was easily prepared by direct mixing of the aqueous AgNW solution with a paste of SWCNTs functionalized with UHP (UHP-SWCNTs) by shaking, as shown in Figure 16a. The spray-coated AgNW/SWCNT hybrid film has an Rs value of ~20 Ω/sq. and T > 90% and was used to fabricate transparent film heaters to investigate the effect of SWCNTs on the electrical stability of the AgNW films under current flow. Notably, the breaking up of AgNWs at junctions was observed at 9 V (Figure 17a), which might have been induced by rapid joule heating at the junctions because of the high junction resistance of the AgNWs (R11 ≈ 103–109 Ω). In stark contrast, after hybridization with SWCNTs, a new current pathway through the AgNW-SWCNT junction may be formed because of the relatively low contact resistance between the AgNW and SWCNT (R12 ≈ 103 Ω) when compared to R11, resulting in the formation of stable network films even at 15 V. Moreover, a very small work function difference between AgNW and UHP-SWCNTs, based on the Φ values of AgNW (4.1 eV) and UHP-SWCNTs (4.3 eV), can promote the current pathway through the AgNW-SWCNT junction (Figure 18).
(a) Preparation of AgNW/SWCNT solution by direct mixing of aqueous AgNW solution and UHP-functionalized SWCNTs. (b) Optical transmission of the AgNW and AgNW/SWCNT hybrid films with Rs ≈ 20 ohm/sq. fabricated by spraying. Inset image shows the lighting of an LED lamp at 3 V on bendable AgNW/SWCNT hybrid film on a polycarbonate substrate. (c) Raman spectra of the QHB-SWCNT film prepared by paste and AgNW/UHP-SWCNT hybrid films fabricated by mixture inks [22].
(a, b) Time-dependent temperature profiles of (a) AgNW and (b) AgNW/SWCNT hybrid films. The inset images are infrared thermal images of the film heaters. (c, d) Tilted SEM images of (c) AgNW and (d) AgNW/SWCNT hybrid films after heating at an input voltage of 9 V. (e) Schematic of AgNW/SWCNT hybrid networks showing possible current flow pathways (I, II). R1 and R2 indicate the resistivity of AgNWs and SWCNTs, respectively. R11 or R12 indicate the contact resistances between AgNWs or between AgNW and SWCNTs.
(a, b) Schematic diagram showing poor contact between AgNWs (a) and good contact between AgNW and UHP-SWCNTs. (c) Ultraviolet photoelectron spectroscopy spectra of AgNW, UHP-SWCNT, and thermally treated UHP-SWCNT films. (d) Schematic showing the reason for the current pathway through SWCNTs in terms of the work function.
We have briefly reviewed recent research progress on TCF technologies based on SWCNTs, AgNWs, and GO nanosheets via interfacial engineering and hybridization strategies. One-dimensional (1D) conducting nanomaterials such as CNTs and metal nanowires have been studied intensively because of their fascinating properties and offer tremendous potential for flexible opto-electronic applications in touch screen panels, flexible displays, solar cells, thin film heaters, signage, etc. To realize these applications, we need to develop high-performance TCFs with flexibility using a low-temperature process with scalable processing techniques on flexible plastic substrates. In this chapter, therefore, a scalable spray coating process using SWCNTs and AgNW solutions was introduced by demonstrating the wettability of the solution on surface energy-controlled substrates. One of the most important strategies for high-performance TCFs is interfacial engineering. Matching the interfacial tension between top-coating materials and the film is an important practical concept for fabrication of passivated TCFs that are environmentally stable at high humidity and temperature, as well as to improve their opto-electrical properties. Moreover, rational use of GO nanosheets and SWCNTs can improve AgNW network TCFs by welding in plastic substrates and efficient junction joining of AgNW junctions. Chemical or thermal welding of SWCNT networks is also useful for self-passivation of films on thermoplastic substrates.
In addition, recently developed AgNW/SWCNT hybrid TCF technologies can be commercially used to fabricate large area flexible TCFs by a roll-to-roll process because of fabrication of coating solutions without additional dispersant molecules.
For large opto-electronic devices with flexibility and stretchability, there are still many challenging issues for commercial application, including newly designed anisotropic conducting materials and their solution processing.
This work was supported by the Center for Advanced Soft-Electronics as Global Frontier Project (2014M3A6A5060953) funded by the Ministry of Science, ICT and Future Planning and by the Primary Research Program (18-12-N0101-18) of the Korea Electrotechnology Research Institute.
Inspired by the biological neurons, deep neural networks are well known for their ability to learn data representation from a huge amount of labeled data such as the famous convolutional neural networks (CNNs). Specifically, given a specific task such as image classification, we usually need to train a deep neural network from scratch with enough training data so that our model can achieve acceptable performance. However, sufficient training data for a new task is not always available as manually collecting and annotating data are labor-intensive and expensive. Especially in some specific domains such as healthcare, a privacy concern is also raised. Meanwhile, training a deep network with a large dataset is usually time-consuming and involves huge computational resources. Intuitively, it is not realistic and practical to learn from zero, because the real way we humans learn is that we usually try to solve a new task based on the knowledge obtained from past experiences. For example, once we have learned a programming language (e.g., Java), we can easily learn a new one (e.g., Python) as the basic programming fundamentals are the same.
Transfer Learning is an inspiring method that can help apply the knowledge gained from a source task to a new/target task. Specifically, the goal of transfer learning is to obtain some transferable representations between the source domain and target domain and utilize the stored knowledge to improve the performance on the target task. Note that transfer learning is an extensive research topic that involves many learning methods. In particular, deep domain adaptation gets the most attention in recent years among these methods. Therefore, after briefly introducing the transfer learning in this research, we pay our attention to analyzing and discussing the recent advances in deep domain adaptation.
The rest of this chapter is structured as follows. In Section 2, we give an overview and specific definitions of transfer learning. In Section 3, we summarize the main approaches for deep domain adaptation. In Section 4, 5 and 6, we discuss the details for conducting deep domain adaptation. The recent applications based on deep domain adaptation methods are also introduced in Section 7. Finally, we conclude this research and discuss future trends in Section 8.
We first give some notations and definitions which match those from the survey paper written by Pan et al. [1], and these notations are also widely adopted in many other survey papers such as [2, 3].
Definition 1 (Domain [1]) Given a specific dataset
Definition 2 (Task [1]) Given a specific dataset
Definition 3 (Transfer Learning [1]) Given a source domain
In short, transfer learning can be simply denoted as
Transfer learning is a very broad research subject in machine learning. In this research, we mainly focus on transfer learning based on deep neural networks (i.e., deep learning). Therefore, as shown in Figure 1, based on
Hierarchically-structured taxonomy of transfer learning in this survey.
When
When
Definition 4 (Domain Adaptation) Given a source domain
When
In summary, the above definitions give us the answer to what to transfer, and the four scenarios demonstrate the research issue of when to transfer. As shown in Figure 2, in contrast to the categorization of transfer learning that is introduced in the survey paper [1], our discussions mainly focus on transfer learning in deep neural networks. In the following sections, we pay our attention to how to transfer. Specifically, we will introduce and summarize three main methods for deep domain adaptation.
Categorization of transfer learning based on labels. (The image is from Pan [1]).
According to the definition of domain adaptation, we assume that the tasks of the source domain and target domain are the same, and the data in the source domain and target domain are different but related (i.e.,
Compared with the traditional shallow method, deep domain adaptation mainly focuses on utilizing deep neural networks to improve the performance of the predictive function
where
A natural way to reduce the domain shift is to fine-tune the pre-trained networks with the data in the target domain, as the past researches show that the internal representations of deep convolutional neural networks learned from large datasets, such as ImageNet, can be effectively used for solving a variety of tasks in computer vision. Specifically, for a pre-trained model such as VGG [4] or ResNet [5], we can keep its earlier layers fixed/frozen and only fine-tune the weights in the high-level portion of the network by continuing back-propagation. Or we can fine-tune all the layers if needed. The main idea behind this is that the learned low-level representations in the earlier layers mainly consist of generic features such as the edge detector. During fine-tuning the networks, the discrepancy between the source domain and target domain is usually measured by a criterion such as class labels based criterion, and statistic criterion. Instead of directly using the measurement as a criterion to adjust networks, regularization techniques can also be used for fine-tuning, which mainly includes parameter regularization and sample regularization.
Generative Adversarial Networks (GANs) are a promising method and get the most attention due to its unsupervised learning approach and the flexibility of generator design. Since the first version of GANs is proposed by Goodfellow et al. [6], many variants based on it have been proposed for solving different types of tasks. Specifically, there are normally two networks in GANs, namely a generator and a discriminator. The generator can synthesize fake examples from an input space called latent space and the discriminator can distinguish real samples from fake. By alternately training these two players, both of them can enhance their abilities. The fundamental idea behind GANs is that we want the data distribution learned by the generator is close to the true data distribution. And this is very similar to the principle of domain adaptation, which is that the learned data distribution between the source domain and the target domain is close to each other (i.e., domain confusion). For example, a representative work related to adversarial domain adaptation is [7], in which a generalized framework based on GANs is introduced. Instead of using GANs for domain-adversarial learning, a more simple but powerful method is to add a domain classifier into a general deep network for encouraging domain confusion [8].
Data-reconstruction approaches are a type of deep domain adaptation method that utilizes the deep encoder-decoder architectures, where the encoder networks are used for the tasks and the decoder network can be treated as an auxiliary task to ensure that the learned features between the source domain and target domain are invariant or sharing. There are mainly two types of methods to conduct data reconstruction: (1) A typical way is by utilizing an encoder-decoder deep network for domain adaptation such as [9]; (2) Another way is to conduct sample reconstruction based on GANs such as cycle GANs [10].
In general, the core idea of deep domain adaptation is to learn indiscriminating internal representations from the source domain and target domain with deep neural networks. Therefore, we can combine different kinds of approaches discussed above to enhance the overall performance. For example, in [11], they adopt both the encoder-decoder reconstruction method and the statistic criterion method.
Based on whether there are labels in the target domain datasets, we can further divide the above approaches into supervised learning and unsupervised learning. Note that the unsupervised learning methods can be generalized and applied to semi-supervised cases, therefore, we mainly discuss these two methods in this research. Table 1 shows the categorization of deep domain adaptation based on whether the labels are needed in the target domain. A similar categorization is also introduced in [12].
Supervised | Unsupervised | ||
---|---|---|---|
Fine-tuning | Label criterion | ✓ | |
Statistic criterion | ✓ | ||
Parameter regularization | ✓ | ✓ | |
Sample regularization | ✓ | ✓ | |
Adversarial-domain | Domain classifier | ✓ | |
Target data generating | ✓ | ||
Sample-reconstruction | Encoder-decoder-based | ✓ | |
GAN-based | ✓ |
Categorization of deep domain adaptation based on whether the labels in the target domain are available.
In some survey papers, the domain adaptation methods can also be categorized into two main methods based on the similarity of data space. (1) Homogeneous domain adaptation represents that the source data space and the target data space is the same (i.e.,
Categorization of domain adaptation based on feature space. (The image is from Wang [12]).
In the last section, we categorize the main methods to conduct domain adaptation with deep neural networks and give some high-level information. In this section, we firstly discuss the details of four approaches for fine-tuning networks in Table 1.
The most basic approach to conduct domain adaptation is to fine-tune a pre-trained network with labeled data from the target domain. Hence, we assume that the labels in the target dataset are available and we can utilize a supervised learning approach to adjust the weights/parameters in the network. Based on the definition of the task, our target task
where
As discussed in Section 3.1, a question is that how many layers in the neural network we should freeze. In general, there are two main factors that can influence the fine-tuning procedure: the size of the target dataset and its similarity to the source domain. Based on the two factors, some common rules of thumb are introduced in [13]. One typical work is [14], in which a unified supervised method for deep domain adaptation is proposed. Another problem is that what if there are no labels in the target dataset. Therefore, an unsupervised learning method must be applied to the target dataset for domain confusion.
From the definition of domain adaptation, we see that the fundamental goal is to reduce the domain divergence between the source domain and target domain so that the function
Maximum Mean Discrepancy (MMD) [15] is a well-known criterion that is widely adopted in deep domain adaptation such as [16, 17]. Specifically, MMD computes the mean squared difference between the two datasets, which can be defined as
where
where
Note that for fine-tuning networks with the label criterion or the statistic criterion, the weights in the networks are usually shared between the source domain and target domain. In contrast to these methods, some researchers argue that the weights for each domain should be related but not shared. Based on this idea, the authors in [20] propose a two-stream architecture with a weight regularization method. Two types of regularizers are introduced:
where
Alternatively, instead of adapting the parameters in the networks, we can re-weight the data in each layer of feed-forward neural networks. The typical method to reduce internal covariate shit in deep neural networks is to conduct batch normalization during training [22].
Note that
Instead of directly fine-tuning networks, adversarial domain adaptation is an appealing alternative to unsupervised learning. It mainly addresses the problem that there are abundant labeled data in the source domain but sparse/limited unlabeled samples in the target domain. The core idea of the adversarial domain adaptation is based on GANs. Specifically, a generalized architecture to implement this idea is proposed in [7]. In this section, we detail two main ideas: target data generating and domain classifier.
To overcome the limitation of sparse unlabeled data, target data generating is an approach to directly generate samples with labels for the target domain so that we can utilize them to train a classifier for the new task. One representative work is the CoGANs [25], in which there are two GANs involved: one for processing the labeled data in the source domain and another for processing the unlabeled data in the target domain. Part of the weights in the two generators is shared/tied in order to reduce the domain divergence. In addition to two discriminators for classifying the fake and real samples, there is also an extra classifier to classify the samples based on the information of labels in the source domain. By jointly training these two GANs, we can generate unlimited pairs of data, in which each pair consists of a synthetic source sample and a synthetic target sample and each pair shares the same label. Therefore, after finishing jointly training the two GANs, the pre-trained extra classifier is the function
In summary, target data generating is a domain adaptation approach that focuses on generating target data, which can also be treated as an auxiliary task to reduce domain shift by a weight sharing mechanism between two GANs. The main disadvantage is that the training cost for generating synthesized samples with two GANs is expensive especially when the target datasets consist of large-size samples such as high-resolution images.
Instead of directly synthesizing labeled data for domain adaptation, an alternative way is to add an extra domain classifier to enough domain confusion. The role of domain classifier is similar to that of the discriminator in GANs, it can distinguish the data between the source domain and target domain (the discriminator in GANs is responsible for recognizing the fake from the real data). With the help of an adversarial learning approach, the domain classifier can help the network learn domain-invariant representation from the source domain and the target domain. In other words, the trained model can be directly used for the target/new task.
Therefore, the key is how to conduct adversarial learning with the domain classifier. In [8], a gradient reversal layer (GRL) before domain classifier is introduced to maximize the gradients for encouraging domain confusion (we normally minimize the gradients for reducing the scalar value of a loss function). In [27], a domain confusion loss is proposed beside the domain classifier loss.
The core idea of the data-reconstruction approach is to utilize the reconstruction as an auxiliary task for encouraging domain confusion in an unsupervised manner. In this section, we discuss two types of approaches that are mainly addressed in recent years, including the encoder-decoder-based method and the GANs-based method.
To reconstruct the samples, the basic method is that we can adopt an auto-encoder framework, in which there is an encoder network and decoder network. The encoder can map an input sample into a hidden representation and the decoder can reconstruct the input sample based on the hidden representation. In particular, the encoder-decoder networks for domain adaptation typically involve a shared encoder between the source domain and target domain so that the encoder can learn some domain-invariant representation. An earlier work can be found in [9], in which the stacked denoising auto-encoder is adopted for sentiment classification.
Recently, a typical work called deep reconstruction-classification networks is introduced in [11], in which the encoder and decoder are both implemented with convolutional networks. Specifically, the convolutional encoder is used for supervised classification of the labeled data from the source domain. Meanwhile, it also maps the unlabeled data from the target domain into hidden representation, which is further decoded by the convolutional encoder for reconstructing the input. By jointly training these networks with the data from the source and target domains, the shared encoder can learn some common representations from both datasets, which results in domain adaptation. Other similar work based on auto-encoder can also be found in [11, 28].
Traditionally, the GANs [6] consists of a generator and discriminator, where the generator can be seen as a decoder network which can decode some random noise into a fake sample and the discriminator can be treated as an encoder network which is used to encode the sample into some high-level features for classification (i.e., fake or real). Instead of just using a decoder network as the generator, a typical work known as Cycle GANs is proposed in [10], in which the generator is implemented with an encoder-decoder network. Specifically, this encoder-decoder generator is used for dual learning:
where
As shown in Figure 1, the scope of transfer learning is far beyond traditional machine learning. Theoretically, the problems addressed by deep learning can also be solved by transfer learning. In this section, we narrow the discussion to the typical real-world applications based on deep domain adaptation. In Section 7.1, we summarize the most methods discussed above for computer vision. In Section 7.2, we discuss the applications beyond the context of image processing, including natural language processing, speech recognition and other real-world applications based on processing time-serial data.
Classification is a fundamental and most basic problem in machine learning, most of the above methods are introduced to address this problem. Therefore, we pay our attention to the advances that deep domain adaptation can bring for image classification, rather than repeatedly introducing them. Probably the most well-known example is fine-tuning a giant network that is pre-trained with the ImageNet dataset for real-world applications such as pet recognition. Despite the fact that manually collecting data is time-consuming and expensive, the data collected from the real-world is usually imbalanced (e.g., there are only 100 images of class A but 10,000 images of class B). If we train a classifier from scratch, the performance can be poor because it cannot learn enough knowledge from the limited samples (e.g., class A). However, if we utilize a pre-trained model based on the well-collected ImageNet and fine-tune it, the problem caused by an imbalance dataset will be reduced because the model has already obtained rich knowledge from the source domain.
Another typical real-world application that we can gain benefits from domain adaptation is face recognition. A general approach to solve this problem is to train a model based on a dataset of labeled face images. In contrast, the large-scale unlabeled video datasets are always available. However, the divergence of data in the video is usually limited and there remains a clear gap between these two different domains. In order to utilize the rich information from video and overcome these challenges, the authors in [31] propose a framework for face recognition in unlabeled video based on the adversarial domain adaptation approach.
The recent object detection methods are mainly driven by two approaches: Faster R-CNN [32] and YOLO [33]. Specifically, two tasks are mainly involved in object detection: The first one is to detect whether there are objects in an input image (i.e., to output the bounding box of each object in the image); Meanwhile, the object in each bounding box is also classified. Object detection is a very common learning task in many real-world applications such as intelligent surveillance systems [34]. By utilizing domain adaptation approaches for the new task of object detection in the wild, the Domain Adaptive Faster R-CNN is introduced in [35]. And the core idea is also to utilize domain classifier with GRL to encourage domain confusion (i.e., in Section 5.2). Another recent similar work is also discussed in [36], in which the GRL is also adopted and the process of conducting domain adaptation is divided into two stages called progress domain adaptation.
The convolutional encoder-decoder architecture has achieved great success for image segmentation in recent years. Specifically, given an input image, the convolutional encoder-decoder network can map this image into a pixel-level classification image (i.e., each pixel is classified with a label). The problem of domain shifts can also appear in this task, which results in poor performance on a new domain. In [37], the researchers introduce a domain adversarial learning method which includes both global and category-specific techniques. They argue that two factors can cause domain shift: the global changes between the two distinct domains and the category-specific changes. (i.e., the distribution of cars from two different cities may be different.) Based on this assumption, two new loss functions are introduced, one is used for reducing the global distribution shift between the source images and target images and the other is used for adapting the category-specific divergence between the target images and the transferring label statistics. Instead of just using a simple adversarial objective, the authors in [38] propose an iterative optimization procedure based on GANs for addressing domain shift.
As mentioned in Section 6.2, Cycle GANs [10] is a typical method for image-to-image translation based on deep domain adaptation. In general, image-to-image translation denotes that we can map an image from the source domain to the target domain and vice versa. One real task that is also addressed in Cycle GANs is the style transfer application. To our best knowledge, the algorithm of neural style transfer is firstly proposed in [39], the core idea in this paper is how to define the content loss and style loss between the source data and the target data. Actually, it can be treated as a statistic criterion approach which is discussed in Section 4.2. In the paper of demystifying neural style transfer [40], the authors show that matching Gram matrices (i.e., style loss) is equivalent to minimize the MMD (i.e., Eq. 4). Based on this argument, they introduce several style transfer methods by utilizing different types of kernel functions in the MMD and achieve impressive results.
An interesting but challenging task is to utilize deep neural networks to describe an input image with natural language, which is well known as the image caption. Specifically, the goal of image caption is to learn a mapping function
When we apply an image-caption model which is trained from image dataset A on image dataset B, the performance will degrade due to the distribution change or domain shift of two datasets. To address this problem, the work in [42] introduces an adversarial learning method to address unpaired data in the target domain for image caption (i.e., adversarial domain adaptation approach in Section 5). In [43], the authors propose a dual learning method for addressing this problem, which involves two steps: (1) A CNN-RNN model is trained with sufficient labeled data in the source domain. (2) The model is then fine-tuned with limited target data. The core idea of dual learning mechanism involved a reverse mapping process: the model firstly maps an input target image to text (i.e.,
Deep domain adaptation technique is also used for solving a variety of tasks in processing natural language. In [44], an effective domain mixing method for machine translation is introduced. The core idea is to jointly train domain discrimination and translation networks. The authors in [45] propose aspect-augmented adversarial networks for text classification. The main idea is to adopt a domain classifier, which has been discussed in Section 5.2. Recently, an interesting research area is to utilize neural models to automatically generate answers based on the input questions, which is also known as questions answering. However, the main challenge to train models is that it is usually difficult to collect a large dataset of labeled question-answer pairs. Therefore, domain adaptation is a natural choice to address this problem. E.g., in [46], a framework called generative domain-adaptive nets is introduced. Specifically, a generative model is used to generate questions from the unlabeled text for enhancing the model performance. Other applications of domain adaptation can also be found in sentence specificity prediction [47], where the specificity denotes the quality of a sentence that belongs to a specific subject.
A typical real-world application is to transcribe speech into text, which is also known as automatic speech recognition. Domain adaptation is also suitable for addressing the training-testing mismatch of speech recognition that is caused by the shift of data distribution between different datasets. For example, a neural model trained on a manually collected dataset may generalize poorly in the real-world application of speech recognition due to the environmental noises. In [48], an adaptive teacher-student learning method is proposed for domain adaptation in speech recognition systems. In [49], the domain classifier that is discussed above is also adopted for robust speech recognition. Similar work can also be found in [50], in which the adversarial learning method for domain adaptation is also used for addressing the unseen recoding conditions.
Domain adaptation can also enhance the performance of processing many other time-series datasets such as healthcare time-series datasets [51], in which the authors present a variational recurrent adversarial method for domain adaptation. The main idea is to learn domain-invariant temporal latent representations of multivariate time-series data. Another real-world task that involves time-series data is to build diver assistant systems. In [52], an auxiliary domain classifier is also adopted to enhance the performance of recurrent neural networks for driving maneuvers anticipation. And the core idea in this paper is also to learn sharing features from different datasets by the domain classifier. An interesting work related to inertial information processing is introduced in [53], in which a novel framework called MotionTransformer is proposed for extracting domain-invariant features of raw sequences.
In this chapter, we firstly introduce the background and explain why transfer learning is important for helping learn real-world tasks. Then we give a strict definition of transfer learning and its scope. In particular, we pay our attention to deep domain adaptation, which is a subset of transfer learning and it mainly addresses the situation where we have different but related datasets for a common learning task. Next, we categorize the deep domain adaptation based on three aspects: the specific implementing approaches, the learning methods, and the data space. In general, deep domain adaptation is one type of method that mainly utilizes deep neural networks to reduce the domain shift or data distribution so that we can enhance the performance of the target task with the help of the knowledge obtained from the source domain. Specifically, we mainly discuss the recent advanced methods for domain adaptation from the deep learning community, including fine-tuning networks, adversarial domain adaptation, and data-reconstruction approaches. Finally, we introduce and summarize the typical real-world applications in computer vision from recently published articles, from which we can see that the unsupervised learning approach based on GANs gets the most attention. In addition, we discuss many other applications beyond the context of image processing. And we notice that many deep domain adaptation methods that are initially proposed for processing images are also suitable for addressing a variety of tasks in natural language processing, speech recognition, and time-series data processing.
Although deep domain adaptation has been successfully used for solving various types of tasks, we should be careful to conduct transfer learning, as brute-force transfer may hurt the performance of our model. The above applications mainly focus on homogeneous domain adaptation, which means that the data between the source domain and the target domain is related and we assume that deep neural networks can find some shared representation from these two domains. However, the data collected from real-world may not always meet this requirement. Therefore, the future challenge is how to apply a heterogeneous domain adaptation method effectively. From the above analyses, we notice that transfer learning has been mainly applied to a limited scale of applications. Therefore, more challenges are also needed to address in the future such as logical inference and graph neural networks based tasks.
This work is supported by China Scholarship Council and Data61 from CSIRO, Australia.
The authors declare no conflict of interest.
Ove Odredbe i uvjeti ističu pravila i regulacije u svezi korištenja IntechOpenove stranice www.intechopen.com i svih poddomena u vlasništvu IntechOpena, tvrtke sa sjedištem u 5 Princes Gate Court, London, SW7 2QJ, Ujedinjeno Kraljevstvo.
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\\n\\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\\n\\nKlijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\\n\\nKompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\\n\\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\\n\\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
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\\n\\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\\n\\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
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\\n\\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\\n\\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
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\\n\\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\\n\\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\\n\\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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\n\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\n\nKlijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\n\nKompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\n\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\n\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\n\nOsim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
\n\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\n\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\n\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\n\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\n\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\n\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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