Comparison of the some power density of Main energy harvesting methods.
\r\n\tb. The growth of digital environments which can educate and empower as well as exploit and destroy (mobile learning, STEM education, tablets, etc.).
\r\n\tc. Social, racial, class, and gender-based discriminations that restrict the developmental potential and the prosperity perspectives
\r\n\td. Health hazards and illnesses such as the laters COVID-19 pandemic.
\r\n\te. Armed conflicts with casualties and displacements of populations seeking refuge
\r\n\tf. Lack of physical spaces that will support and nourish development and learning, etc.
\r\n\tEducation in the post-modern era strives to address the above issues and develop policies, curricula, methodologies, and strategies to contribute to an environmentally and socially sustainable future. It embraces multiple perspectives and worldviews and seeks to touch on inequalities and discriminations in favor of equity. In this direction, children’s s agency lies at the heart of democratic approaches. Educational processes adopt forms of interactions that actualize learning as “becoming” and place it in a continuum between past, present, and future. This book intends to feature innovative approaches that employ transformative elements (targets, methods, materials, ideas, etc.) and embrace the concept of child development as “becoming” in an ever-changing and challenging world.
\r\n\r\n\tWe invite authors to contribute original research or research review papers that present innovative approaches addressing personal and social transformation. All aspects of early childhood education will be considered, including research methodology for the early years.
",isbn:"978-1-80355-949-0",printIsbn:"978-1-80355-948-3",pdfIsbn:"978-1-80355-950-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"351c41dca5c8c997f15e758f2e035178",bookSignature:"Dr. Maria Ampartzaki and Associate Prof. Michail Kalogiannakis",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11281.jpg",keywords:"Early Childhood Education, Preschool, STEAM, Environmental Sustainability, Social Sciences, Social Sustainability, ICT, Digital Devices, Education for Equity, Gender Issues, Post-modern Epistemology, Social Constructivism",numberOfDownloads:65,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 16th 2021",dateEndSecondStepPublish:"December 14th 2021",dateEndThirdStepPublish:"February 12th 2022",dateEndFourthStepPublish:"May 3rd 2022",dateEndFifthStepPublish:"July 2nd 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"8 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education.",coeditorOneBiosketch:"Michail Kalogiannakis is an Associate Professor of the Department of Preschool\r\nEducation, University of Crete in Greece. He graduated from the Physics Department\r\nof the University of Crete and continued his post-graduate studies at the University\r\nParis-7 and University Paris-5 and received his Ph.D. degree at the University Paris 5.\r\nHis research interests include science education in early childhood, science teaching\r\nand learning, e-learning, the use of ICT in science education, and games simulations.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"422488",title:"Dr.",name:"Maria",middleName:null,surname:"Ampartzaki",slug:"maria-ampartzaki",fullName:"Maria Ampartzaki",profilePictureURL:"https://mts.intechopen.com/storage/users/422488/images/system/422488.jpg",biography:"Dr Maria Ampartzaki is an Assistant Professor in Early Childhood Education in the Department of Preschool Education at the University of Crete. Her research interests include ICT in education, science education in the early years, inquiry-based and art-based learning, teachers’ professional development, action research, and the Pedagogy of Multiliteracies, among others. She has run and participated in several funded and non-funded projects on the teaching of Science, Social Sciences, and ICT in education. She also has the experience of participating in five Erasmus+ projects.",institutionString:"University of Crete",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Crete",institutionURL:null,country:{name:"Greece"}}}],coeditorOne:{id:"260066",title:"Associate Prof.",name:"Michail",middleName:null,surname:"Kalogiannakis",slug:"michail-kalogiannakis",fullName:"Michail Kalogiannakis",profilePictureURL:"https://mts.intechopen.com/storage/users/260066/images/system/260066.jpg",biography:"Michail Kalogiannakis is an Associate Professor of the Department of Preschool Education, University of Crete, and an Associate Tutor at School of Humanities at the Hellenic Open University. He graduated from the Physics Department of the University of Crete and continued his post-graduate studies at the University Paris 7-Denis Diderot (D.E.A. in Didactic of Physics), University Paris 5-René Descartes-Sorbonne (D.E.A. in Science Education) and received his Ph.D. degree at the University Paris 5-René Descartes-Sorbonne (PhD in Science Education). His research interests include science education in early childhood, science teaching and learning, e-learning, the use of ICT in science education, games simulations, and mobile learning. 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For example, WSs are used for ubiquitous structural monitoring [1]. Besides, one of WS’s current major applications is the Internet of Things, in which WSs send their data to a base station that makes it available on the internet. WS provides endless opportunities and poses formidable challenges, such as the fact that energy is limited due to its battery’s small size. In the quest for solutions to extend the lifespan of WSs, a new research field has evolved in recent years, known as Energy Harvesting (EH) [2]. Thanks to EH techniques, it is now possible to envisage WSs having a lifespan limited only by the hardware that constitutes them. These WSs are now well known in the literature as EH-WS [3]. The rise of the EH-WSs is due to the joint efforts in the fields of microelectronics and micro-mechanics, which today make it possible to dispose of ultra-low consumption WSs. An EH process involves identifying a primary energy source in the WS vicinity and converting it into electrical energy directly usable by the WS. This study deals with RF energy, which is ubiquitous due to the extension of telecommunications systems [4].
The proposed techniques about EH rely fully on the nature of the used primary energy sources. The main sources are the sun, vibration, thermoelectric gradient, wind, internal light, radio-frequency energy, etc. Vibration and sun sources are most considered in the literature because they generate more significant amounts of energy compared to the WS energy requirements. Recently, with the growth of radio communication systems deployment, it has become possible to consider harvesting significant quantities of RF energy in different environments. Besides, RF sources do not rely on weather conditions, as is the case with the sun source or engine operation, as would be the case with the vibration source.
This chapter presents the design considerations of energy-independent wireless sensor nodes under the base of a radiofrequency energy harvesting process. The main objective is to analyze the end-to-end conversion chain of radiofrequency waves into DC energy to define the design issues related to this growing field of research. For each stage, it will be reviewing the principles through design equations and optimization solutions. This main goal will guide the writing of the chapter, with the following specific objectives:
Provide a taxonomy for WSs powered by radiofrequency energy.
Provide a classification of RF energy-harvesting techniques.
Provide state of the art on the design of the RF-EH system.
Recall the performance of the rectenna recently designed.
Firstly, the advantage of the RF energy source compared to the other commonly used primary energy sources is proposed in Section 2. The role of wireless sensors in the IoT and the capability of some currently marketed RF energy harvesters will be presented in Section 3. Section 4 dealt with the design issues of the RF energy harvesting systems. Each stage of the conversion chain will be analyzed, and the advantages and drawbacks of the proposed solutions will be established. Finally, Section 5 concludes this chapter.
The primary energy sources considered for EH are vibration, sun, RF Energy, airflow, internal light, heat, and wind. Most computing systems require small and light WSs to influence the measurement environment as little as possible. Therefore, the power density metric is widely used by many researchers as a criterion for comparing the performance of micro-generators [5]. Table 1 show some recent results obtained in the design of the various micro-generators [6]. These results indicate that the current RF micro-generators have power density comparable to sources such as airflow, heat, and indoor light.
Primary sources | Power densities |
---|---|
Vibration | |
Heat | |
Wind | |
Light (outdoor) | |
Light (indoor) | |
RF energy | |
Airflow |
Comparison of the some power density of Main energy harvesting methods.
Sources like vibrations and sunlight offer power densities 10 to 100 times higher than the RF source. However, in most studies on vibrations, to achieve these performances, it is necessary to increase the level of vibration, which is not desirable for many industrial applications, especially sensor applications. In [7], for instance, a piezoelectric micro-generator is designed to generate only
Regarding solar energy, the achieved performance is inherently impacted during the night. Also, for WSs being deployed in indoor environments like buildings or factories, solar energy may not be available. Wind micro-generators share this constraint. These also often involve a substantial aperture [9], which is not suited to accommodate IoT applications’ size design limitations. RF Energy, which, for some frequencies, can cross materials such as water, plastic, paper, and concrete, seems to be the only alternative in several situations. This research area is now expanding because this harvestable energy (RF energy) is almost always available, offering solutions to facilitate the supply of WS located in hard-to-reach environments.
Another asset of the RF source lies in the used transducer, as it can also be exploited to exchange data between sensors wirelessly. The primary transducers used are shown in Figure 1. The WS for transmitting and receiving information usually uses an antenna. The transducer for the RF source is, therefore, the same as that used by the WS to communicate. Thus, it is important for greater circuit miniaturization to consider using the RF source [10]. It is also the current trend of WS based on RF source referred to as Simultaneous Wireless Information and Power Transfer (SWIPT) technologies [11].
Some used transducers for the transformation of ambient energy.
Setting up the IoT is now possible thanks to the convenience of placing or deploying many different sensors in an environment. Figure 2 shows the end-to-end IoT basic architecture elements [12]. The WS is the element that lays the foundation for IoT. Unlike other elements in Figure 2 that can be placed in easily accessible locations, WSs must be able to be placed in locations such as battlefields, the deep ocean, or inhospitable terrains. Since WSs are battery-powered, it is often difficult and impossible to change or recharge their battery. Also, the WS’s role in the figure below is to measure, process, and transmit data to a base station. More and more IoT applications require fast computational times, increasing the WS’s energy budget. This further justifies the need for a ubiquitous charging solution such as the RF source for WS in mobile devices.
End-to end IoT architecture.
Regarding RF micro-generators, a product like the PCC110 [13] manufactured by Powercast, is a solution used to enable wireless power transmission. Its sensitivity is −17 dBm with a maximum conversion efficiency of 75%. Powercast also markets the P2110 [14], which harvests RF energy in the 915 MHz band while integrating efficient energy management solutions. This circuit can operate at incident powers below −11.5 dBm. It is also proposed in [15] the E-peas AEM40940, which offers RF energy harvesting solutions in three frequency bands 868 MHz, 915 MHz, and 2.45 GHz. This circuit offers usable DC output powers for incident RF powers between −19.5 dBm and 10 dBm. Due to the flexibility of the charging solution, these different circuits are a few examples that can be integrated into computing systems, particularly in mobile IoT applications.
When observing at the end-to-end conversion efficiency of a rectenna, it is necessary to consider the energy propagation models, the receiving antenna, the characteristics of the rectifying diodes (RF/DC Converter), the matching filter design, and finally, the Storage Element as shown in Figure 3.
Outline of design issues of rectenna.
Energy propagation models can be used to estimate the harvestable energy levels depending on the propagation environment [16]. The receiving antenna must be designed to be optimal in the frequency band of the harvestable RF signals. The used rectifying diode must have the least loss in the targeted frequency band. The matching filter must be optimized to minimize reflection losses. A DC/DC converter is added to achieve Maximum Power Point Tracking (MPPT). Finally, when the rectenna is designed, its modeling is necessary to offer an efficient management solution for the energy harvested. Note that once the components to achieve optimal performance are selected concerning the concepts overviewed, circuit manufacturing must be addressed; this chapter does not address this issue.
When considering the use of RF energy as a power source for WSs, it is important to distinguish the Ambient RF Energy Harvesting (A-RF-EH) from Wireless Power Transfer (WPT) [17, 18] (Figure 4). The A-RF-EH aims to recycle energy available in the environment that comes from wireless communication devices’ surrounding activity, as shown in Figure 4(a). Due to potential health concerns, the environment’s naturally available RF power levels are too low. However, several designers have been able to propose solutions for harvesting usable quantities of power. These solutions rely mainly on circuits’ design capable of harvesting RF energy through several frequency bands simultaneously [19].
The technique of RF-EH. (a) A-RF-EH and (b) WPT.
Another way to exploit RF energy is to use the WPT, as illustrated in Figure 4(b). The WPT can be done either using magnetic fields to carry the electrical energy with coils or by antennas. In the case of coils, the original proposal was made by Nicolas Tesla [20] and is based on the magnetic resonance of two coils to distribute large amounts of energy to locations far from the power source. Although this concept is used by many applications such as Radio Frequency IDentification (RFID) tags [21] and biomedical devices [22], it should be mentioned that its range is limited. It would, therefore, be challenging to implement for WSs placed in hard-to-reach locations. In addition to the constrained range, the power levels are too high, bringing health issues and effects [11] when someone is close to the transmitter.
The most popular way to power the WS by RF energy is by using antennas. As opposed to the near-field application, the use of antennas is known as the far-field application. Historically, this way of transferring energy via radio waves dates to the first works of Heinrich Hertz [23]. The block diagram of the conversion of RF into DC energy via antennas is depicted in Figure 5 [17]. A transmitting antenna sends a signal at a given power and frequency. A receiving antenna operating on the same frequency then picks up the emitted signal. An RF/DC converter is used to transform the RF signal into a DC signal. To ensure maximum power transfer between the antenna and the RF/DC converter, it is essential to use a matching circuit. The rectifier’s output DC voltage is generally very low and cannot be used directly for a given application. Moreover, the value of the output DC voltage changes depending on the input RF power level. A DC-to-DC converter is thus necessary to adapt the rectifier voltage to the storage element’s voltage requirement. The combination made up of these blocks is called
Generalized RF energy harvesting systems for WS.
The energy amount and rate received by an antenna over time are two critical parameters to be considered before the circuit design [25]. Several propagation models exist to predict the average strength of the signal received at a given distance from the transmitting antenna [16]. These models are divided into two broad kinds: large-scale and small-scale fading models.
The large-scale models are used to assess the received signal’s strength over large distances between the transmitting antenna and the receiving antenna; they are then suitable for designing a WS based on the WPT. The basic model is the free space model; it is an ideal model used when the transmitting antenna and the receiving antenna have an unobstructed path. The received power is evaluated by the Friis equation as follows:
where
Practically, to evaluate the received power by an antenna, three basic mechanisms must be considered: reflection, diffraction, and scattering [16]. When considering ground reflection only, the Eq. (2) known as the two-ray model evaluates the received power as:
where
Considering all the factors influencing signal propagation (reflection, diffraction, and scattering) can be done through models derived from the combination of empirical and analytical methods [16], these models are widely used. The most popular is the path loss model, which defines the received power in a complex environment as follows:
The current trend of WS powered by rectenna is the SWIPT, referred to as Simultaneous Wireless Information and Power Transfer [11]. The small-scale fading models are used to quantify the received power by a node, from a node close to it. The fading models allow evaluating the rapid fluctuations of the emitted signal’s amplitude over a short period or for a short distance. Fading models consider the multiple versions of the emitted signal that reach the receiving antenna. If
where
The above summarizes some commonly used RF energy propagation models. Depending on the WPT or the A-RF-EH, deterministic models or stochastic models can be used, respectively. These models must be considered before circuit design because they make it possible to estimate the amount of harvestable energy.
Its role is to adequately capture the emitted signal with the right and high gain. However, the increase of the antenna gain goes with an increase in its dimensions through the equation:
To maximize the energy harvested by the antenna, particularly in the case of A-RF-EH, the studies report multi-band, broadband, and reconfigurable [27] antennas to overcome a lack of knowledge of the transmitting antennas’ location and frequency.
Another important feature of the receiving antenna is its polarization, which must be circular to offer the possibility of keeping a constant DC output voltage even if the transmitting antenna or the rectenna [28] are rotating. The most widely used antennas are the dipole antennas, and the patch antennas. Since most applications have congestion as a design criterion, the patch antenna allows for easy integration; it is lightweight, low-cost, and widely considered in rectenna design. Also, these antennas are adapted to future 5G communication specifications [29]. The well-known structure of a patch antenna is shown in Figure 6.
3D view of the rectangular patch antenna.
The resonance frequency of the antenna, which must be the same as that of the transmitted signal, is related to length
where
The thickness
The width
In most design strategies, the formulas (6) to (10) are used for the first sizing of the antenna, and then the optimization is done using an electromagnetic simulator. Table 2 shows the gain capabilities for some patch antennas recently designed for rectenna applications.
Frequency | Gain | Sizes | Used substrate @ permittivity | Ref |
---|---|---|---|---|
RO4003@ | [30] | |||
GSM 900 | — | FR4 @ | [32] | |
GSM 1800 | — | FR4 @ | [32] | |
3G | — | FR4 @ | [32] | |
FR4 @ | [33] | |||
— | [34] | |||
Thin Teflon @ | [31] | |||
Thin Teflon @ | [31] | |||
GSM 900 | RT/Duroid@ | [26] |
Recent patch antennas gain for rectennas design.
To be able to supply the WSs with DC power, the RF power harvested by the antenna needs to be rectified. The RF/DC converter assumes this function. The rectification function can be implemented either by transistors or with Schottky diodes. Transistors are least-used because although they are more efficient at very low levels of RF input power [35], the achieved maximum conversion efficiency remains too low compared to that obtained with Schottky diodes [24]. For this reason, the subsequent writing deals only with design issues based on Schottky diodes. The fast switching and low threshold voltage diodes are the most considered by considering the high frequencies and the low voltage level of the incident or input RF signals. The small-signal Schottky diode model shown in Figure 7 is very often used [36].
Small signal model of a Schottky diode.
In this model,
with
The leading manufacturers of the commonly used diodes are Avago, Skyworks, and Macon. Table 3 gives the characteristics of some of the Schottky diodes most considered in the design of RF/DC converters.
In the previous subsection devoted to the receiving antenna, it was mentioned that patch antennas, being compact, lightweight, and low-cost, are the most suitable for the real applications of WSs for which congestion is one of the design constraints. However, compared to other antennas, patch antennas are narrowband and offer lower gains. Thus, the rectifying diode’s conversion efficiency has become a critical design criterion for rectenna circuits [37].
When considering the transformation of the RF signal into a DC signal, the energy harvested by the antenna undergoes the four-stage losses shown in Figure 8. These losses are a significant factor in the choice of the rectifying diode.
Efficiency link of RF/DC power conversion.
The matching efficiency
where
with
Considering Eqs. (13)–(15), an approximate expression of the unmatched reflection coefficient was established in [35] as follows:
From this expression, the conclusion is drawn that at high frequencies, when
with
where
Using Eqs. (12), (17), (18) and (20), a comparison of the RF/DC conversion efficiencies of the four Avago diodes, whose characteristics are reported in Table 3, was proposed in [33]; the obtained results are concise in Table 4. These results show that, for usable power levels [42], the HSMS 2850 diode is more suitable for circuit design.
Diodes | HSMS 2810 Avago | HSMS 2820 Avago | HSMS 2850 Avago | HSMS 2860 Avago | SMS 1546 Skyworks | SMS 7621 Skyworks | SMS 7630 Skyworks | MA4E 1317 Macon | MA4E 2054 Macon |
---|---|---|---|---|---|---|---|---|---|
10 | 6 | 25 | 5 | 4 | 12 | 20 | 4 | 11 | |
0.65 | 0.65 | 0.35 | 0.65 | 0.51 | 0.51 | 0.34 | 0.7 | 0.4 | |
1.1 | 0.7 | 0.18 | 0.18 | 0.38 | 0.1 | 0.14 | 0.2 | 0.13 |
Small-signal characteristics for commonly used Schottky diodes.
Input RF power ( | Optimal load resistance ( | Maximum reached conversion efficiency ( | Best rectifying diode | |
---|---|---|---|---|
1 | 3.52 | 0.8 | 35.5 | HSMS 2850 |
1.8 | 7.12 | 1.22 | 37.3 | HSMS 2850 |
2.5 | 14.63 | 1.13 | 37.8 | HSMS 2860 |
3.5 | 26.21 | 38.3 | 1.22 | HSMS 2860 |
Best Avago rectifier diode according to the incident power level.
Once the diode is selected, it is necessary to consider the topology of the rectifier circuit. Some rectifier topologies recently used in the rectenna design are shown in Figure 9. The most considered are the topologies Single Series Diode (SSD), Single Parallel Diode (SPD), Full Bridge (FB), and Voltage Doubler (VD) [43]. The SSD and SPD topologies are single-wave rectifiers, while the FB and VD topologies are full-wave rectifiers.
Most used rectifier topologies (a) SSD, (b) SPD, (c) FB, (d) VD, and (e) multistage VD.
The characteristics of these basic topologies are proposed in Table 5. A comparison of the three topologies SSD, SPD, and FB, was proposed in [43] using a Rectenna Figure of Merit (RFoM) defined as follow:
Topologies | Description | Advantages | Drawback | Applications |
---|---|---|---|---|
SSD | Easy to implement because it uses a single diode. | Suitable for very low power applications. | Low output DC voltage | A-RF-EH |
SPD | Similar to SSD topology with the same performance. Instead, rectifies the negative alternation. | Suitable for very low power applications. | Low output DC voltage | A-RF-EH |
FB | Uses the Graëtz bridge as in low-frequency power electronics. | Good conversion efficiency at high power. | Insensitive to small tensions. | WPT |
VD | Simple Structure for rectifying the two alternations. | Higher output DC voltage. | Conversion efficiency lower than that of SSD and SPD topologies. | A-RF-EH WPT |
Multi-stage VD | Complex structure using several diodes to amplify the signal | Higher output DC voltage. | Low conversion efficiency due to losses in the diodes. | A-RF-EH WPT |
Comparison of main rectifier topologies.
where
It is also possible to amplify the rectified output voltage several times using several stages of voltage doubling (Figure 9e) [44]. When
where
Although multi-stage VD can achieve significant voltage levels, the fact remains that they contribute to increasing the overall size of the rectenna. Also, the increase in components in the circuits contributes to an increase in losses. This is illustrated in Figure 10, in which up to 10 stages of voltage doublers were analyzed by simulation with Advanced Design System (ADS) software.
Figure 10(a) represents the evolution of the open-circuit voltage
Figure 10(b) shows the conversion efficiency, and it appears that 3 stages of voltage doublers provide the best performance. Beyond that, the efficiency obtained decreases; for example, for 10 stages, maximum efficiency of less than 20% is reached at 10 dBm of incident power.
In Figure 10(c), the circuit’s overall performance is analyzed according to the RFoM defined by Eq. (21). The result shows that the best compromise is reached with 4 stages.
Mutistage VD analysis. (a) Open circuit voltage, (b) efficiency, (c) RFoM.
In addition to ensuring maximum power transfer between the antenna and the rectifier circuit, it also blocks the diode’s harmonics. There are two main types of matching filters for rectennas: the transformer coupling and the LC network. LC networks are more popular and better suited for designing rectennas because of their ease of integration. The LC networks are made of reactive elements (coil and capacitor) which are non-dissipative [24]. The primary LC network is the low pass filter whose cutoff frequency is defined in [38] as:
The parameter for qualitatively characterizing the adaptation is the reflection coefficient of the set consisting of the RF/DC converter and the input filter. A value of
Very few analytical studies on the design of matching filters for rectenna circuits have been introduced in recent years. This is due to the power of the ADS software [33], which incorporates many tools to design and optimize the matching filter elements. In summing up the works [33, 46], the steps for creating a matching filter from the ADS software are shown in Figure 11.
Design step of an optimized matching filter in ADS software.
It is shown (Cf. Figure 11) that from the reflection coefficient of the rectifier circuit, the ADS matching utility tool is used to generate the matching filter in a lumped component. These localized elements are the initial parameters that will then be optimized to achieve specific objectives. Three objectives are generally targeted simultaneously: the minimization of the reflection coefficient in the frequency band of interest, the maximization of the conversion efficiency, and the maximization of the DC output voltage for the expected input RF power level. The ADS software integrates several optimization techniques, the principal ones being: Hybrid, Newton, Quasi-Newton, Gradient, and Random technique. The gradient method search is the most widely used and allows for adjusting a set of variables according to an error function and its gradient. The error function usually used is the least-squares error function. Once the matching filter elements are optimized, the next step in the filter design is the transformation of the lumped component into a microstrip line. Then, the electromagnetic momentum simulator, always integrated into the ADS software, is used to predict the circuit’s performance at high frequencies. This tool is used to create a physical layout to simulate the characteristics of the substrate.
Following the design steps, which are shown in Figure 11, it was proposed in [33], a Rectenna-based Schottky diode HSMS 2850, with a band-pass filter for an optimal RF harvesting at
Optimized rectifier performance at 2.45 GHz [
Most rectennas deployed in a real environment have low and variable DC output voltage due to slight fluctuations in RF input power. The voltage levels achieved cannot, therefore, directly feed the storage element. The DC / DC converter’s function is then to adapt the output voltage of the rectifier to the charging voltage of the storage element. Several DC/DC converters are commercially available. For the case of rectennas design, the most suitable circuits are those with a low start-up voltage, a minimum operating power, and a high conversion efficiency over a wide range. The most appropriate circuits are then the TS3310 of TouchStone and bq25504 of Texas Instrument. A comparison of these two DC/DC converters has been proposed in [47], and it has emerged that the bq25504 converter offers better performance. However, it is less suitable for high dynamic variations of rectenna input power.
Because the harvested RF energy is extremely low, it is difficult to use it to power the WS directly, hence the need for a storage element to accumulate this energy for later use. There are three main components currently used to store harvested energy: the battery, the capacitor, and the supercapacitor. Regardless of the type of the used component, the main features are capacity, voltage, energy density, power density, self-discharge, discharge depth, state of charge, and temperature effects. A comparison of the characteristics of these three components has been proposed in [48]. This study emerges that the supercapacitors can provide high power over a short time; however, the stored energy is ten times lower than that stored in a battery. This justifies the current trend of hybrid storage devices that combine both batteries and supercapacitors [49]. However, in the case of a rectenna, this solution would contribute to increasing the circuit sizes. Thus, for most rectennas involved in WSs, the energy density parameter is the most considered parameter, and it is the battery that offers the best energy density [48]. Depending on the output voltage levels of the DC/DC converter and the desired energy
where
The general design method of WS powered by rectenna is to enslave the WS operation to the available amount of energy. Therefore, one of the major design issues is the battery recharging time, known in the literature as the duty cycle strategy. The battery recharging time knowledge helps define the duty cycle of sensors powered by the harvested energy. Depending on the battery features used, the recharging time is defined in [50] by:
where
For the WS’s perpetual operation, the recharging time must be equal to the time delay spent, by sensor nodes, in the sleep mode, and the energy used during the active mode must avoid draining the battery.
All the above shows that the performance of rectenna circuits depends on several parameters that have been defined in this chapter. The most considered performance criterion is the conversion efficiency of the Rectenna. A comparison of efficiency for circuits designed between 2006 and 2014 was reported in [24]. In Table 6, the performances of recent designs are presented. Particular attention is paid to the rectifying diode used, as well as the rectifier topology.
Input power (dBm) | Efficiency (%) | Used rectifying diode | Rectifier topology | Frequency band | Ref |
---|---|---|---|---|---|
−10 | 59 | HSMS 2850 | SSD | 850–950 MHz | [51] |
13.5 | 80.4 | HSMS 8202 | SPD | 850–950 MHz | [52] |
−7 | 84 | 1 N6263 | FB | 850–950 MHz | [53] |
10 | 40* | HSMS 2850 | VD | 850–950 MHz | [54] |
−1 | 42 | SMS 7630 | VD | 850–950 MHz | [55] |
−2.5 | 65 | SMS 7630 | VD | 850–950 MHz | [56] |
−20 | 20 | HSMS 2820 | SSD | 2.4–2.45 GHz | [57] |
10 | 66.8 | HSMS 2860 | SSD | 2.4–2.45 GHz | [58] |
0 | 54 | HSMS 2852 | SSD | 2.4–2.45 GHz | [59] |
8 | 72.8 | SMS 7630 | SSD | 2.4–2.45 GHz | [60] |
83 | 20 | MA4E1317 | SPD | 2.4–2.45 GHz | [61] |
76 | 26 | HSMS 282P | FB | 2.4–2.45 GHz | [62] |
−13 | 9** | SMS 7630 | VD | 2.4–2.45 GHz | [55] |
−10 | 45 | HSMS 2852 | VD | 2.4–2.45 GHz | [63] |
17 | 82 | MA40150–119 | SPD | 5.8 GHz | [36] |
17.7 | 79.5 | MA4E1317 | SPD | 5.8 GHz | [52] |
27 | 76 | MA4E1317 | SPD | 5.8 GHz | [64] |
0 | 18 | MA4E1317 | SPD | 5.8 GHz | [61] |
20 | 76 | MA4E1317 | SPD | 5.8 GHz | [65] |
0 | 54 | — | SPD | 5.8 GHz | [66] |
Some recent rectenna circuit performances.
is an efficiency achieved without the use of a matching filter.
is the overall conversion efficiency considering the RF signal path losses.
This chapter reports recent advances in the design of radiofrequency energy harvester circuits. To do this, we started by justifying the use of the RF source as a primary energy source for feeding the sensor nodes dedicated to the IoT networks. The need for completely energy-autonomous WSs in mobile computing systems has also been highlighted. We then gave an overview of the efforts carried out in the design of rectenna circuits. Current limitations due mainly to health concerns and circuit size were also mentioned. More specifically, a classification of harvesting techniques was defined, the different models of energy propagation were reviewed. The performance of the receiving patch antennas recently designed for IoT applications has been noted. The performance comparison of recently used rectifying diodes and the areas of use of the main rectifier topologies were also proposed.
“The authors declare no conflict of interest.”
A new intellectual movement in the field of cognitive science1 has been developed, above all, in the last two decades of the current century, starting from debates that took place, mainly, in the philosophy of science at the end of the twentieth century. This movement has been described more broadly by many authors as a “new mechanistic philosophy” [4, 5, 6, 7]. Strongly influenced by recent advances in computer science, neuroscience, and artificial intelligence, the theoretical framework developed by some of the movement’s most prominent authors offers a new physicalist (or materialist) and mechanistic view of human cognition2 [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].
The theory formulated from the application of the neo-mechanistic philosophy to cognitive science and, specifically, to human cognition, can be called the
One of the most central elements present in the framework of MTHC is a “model of human cognitive computation” [9, 10, 11, 13, 15], which is also part of the attempt made by several influential authors to provide some type of unification or integration for the field of cognitive science [9, 10, 23, 24, 25]. However, some complex cognitive capacities and some particular aspects of human cognition still present a challenge for explanations constructed by using this theoretical structure [22].
My central goal in this chapter, therefore, is to present an argument to show that human cognition cannot yet be completely understood and explained in terms of mechanistic computation and that this view indeed presents many substantial limitations.
To develop my argument, I present, firstly, some of the central elements of this neo-mechanistic framework and its application to cognitive science. Secondly, I present the mechanistic model of human cognitive computation, as it is currently framed, and, based on the specialized literature, I show in what dimensions it helps our understanding of some aspects of human cognitive capacities, such as visual perception and memory consolidation. Thirdly, I show that to understand and explain some human cognitive capacities, such as self-consciousness and conscious informal reasoning and decision making, the neo-mechanistic framework shows substantial limitations. I conclude the chapter by suggesting that the notion of human artificial cognitive computation can be useful for several projects, but to fully understand natural human cognition we will most certainly have to consider theories that go beyond the current neo-mechanistic model of human cognitive computation.
The contemporary movement of neo-mechanistic philosophy has been historically associated with ideas already present in the period of Ancient Philosophy. Philosophers, such as Democritus, Leucippus, Aristotle, Epicurus, and Lucretius [9, 14, 26], for example, have been mentioned in the specialized literature as precursors. Although there is no unity of thought regarding this philosophical tradition, these thinkers would arguably have launched, in Western philosophical thought, the first notions linked to mechanistic reflections. In other words, these philosophers would have proposed the general idea that many phenomena in nature must be explained through their basic components, their forms of movement, their properties, and their interactions since these phenomena are also composed of these basic elements.
In Modern Philosophy, the history of what might be called “mechanistic philosophy” is quite complex, given the many debates over definitions of the term and the variety of positions that can be considered within a more general view of what the term means in this period. In any case, many authors consider that the movement of mechanistic philosophy in the seventeenth century is a reaction to Aristotelian natural philosophy and various natural philosophies of the Renaissance period [27]. The French philosopher René Descartes (1596–1650), for example, is considered one of the main figures who laid the foundations of modern mechanistic philosophy, especially with regard to explanations of biological natural phenomena [9, 27, 28, 29, 30, 31]. Des Chene [30] argues that Descartes united a mechanistic ontology, on the one hand, with a method of mechanistic explanation, on the other, applying these ideas to numerous biological phenomena, including the behavior of non-human animals and the human body.
Shortly thereafter, this reasoning would also be applied quite influentially to human beings and their mental capacities. One of the most prominent advocates of this view was the French philosopher and physician Julien Offray de La Mettrie (1709–1751), who published
At the beginning of the twentieth century, the debate about the best explanation for the complex phenomenon of “life” was still quite strong [32]. The controversy was over whether or not this phenomenon could be explained in mechanistic terms. In this context, a very influential work was that of the German-born American physiologist and biologist Jacques Loeb (1859–1924), published in 1912,
In the second half of the twentieth century, philosophers of science sought to analyze, in a more precise way, this mechanistic explanatory strategy. One of the most influential analyzes is present in the work of the American philosopher Ernest Nagel (1901–1985),
It was also during this period that some philosophers of science working in the field of biology began the task of elaborating, in an even more robust and systematic way, notions related to mechanistic explanations in science – mainly in biology. Along these lines, some pioneering works were the following: Herbert Simon,
Within this line of philosophical thinking, the work of William Bechtel and Robert Richardson,
It is also important to point out that in the development of the neo-mechanist movement, at the end of the twentieth century, we can distinguish, more generally, two main trends [5]. One of them focuses more on metaphysical and ontological directions. Authors who work in this line seek, above all, to answer what mechanisms are as real things in the world. The other strand followed in the direction of a greater elaboration of the philosophy of science with epistemological and methodological discussions about scientific explanations, mainly in the area of biology. They seek to explain how something works and not make claims about the ultimate reality of things. These two strands of the new mechanism have been elaborated in an enormous specialized literature that covers several scientific and philosophical areas, dominating a great part of the central debates. Despite being two dimensions that can be separated in the debate, ontological and epistemological discussions are deeply related in many works, both directly and indirectly.
The neo-mechanistic philosophy began to be applied with greater emphasis to cognitive science since the decade of 1990 – with this application becoming stronger in the first decade of the twenty-first century – and it has been better elaborated since then until the present days in central works of very influential authors [9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 35, 36, 37, 38, 39, 40, 41, 42, 43]. According to this view, human cognition, specifically, as well as biological cognition, in general, can be understood and explained through complex models of multilevel neurocognitive mechanisms. At these levels, there are causal processes related to cognitive information processing, cognitive representation, cognitive computing, as well as processes related to chemical and physical reactions that can be used to explain a given cognitive phenomenon. These are, in fact, autonomous processes of causation, which take place at all these different levels and are relevant to the explanation of the phenomenon of interest [44]. According to this theory of human cognition, namely, MTHC, all these causal levels and processes, although autonomous, can be related in a pluralistic mechanistic explanation, where the relevant scientific theories are integrated. As a result, MTHC includes not only a theory of human cognition but also a theory of the human neurocognitive relationship; that is, the theoretical framework suggests a possible solution to the problem of how we are to understand and explain the connection between human neural and cognitive phenomena, thus attempting to relate neuroscience and cognitive science.
The main objective of a mechanistic scientific explanation in scientific areas, such as biology, cognitive neuroscience, and cognitive science, is to identify the parts of a mechanism, its operations, its organization, and thus show how these elements constitute the system’s relationship with the phenomenon that must be explained [9, 10, 45]. Particularly, in cognitive science, the central idea present in the theory is that human neurocognitive processes are a type of information processing performed by neural systems (mechanisms). These processes and the components that carry them out can be decomposed into subparts, and these subparts are decomposed again, as far as necessary for the understanding of the investigated phenomenon. After that, these components and activities have to be located in the brain as spatiotemporal parts of a complex multilevel neurobiological mechanism. As a result, there may be multiple levels of mechanistic composition in a human neurocognitive mechanism.
Another important feature of MTHC is that it was developed within a broad physicalist context that is present in a vast amount of work in contemporary cognitive science, philosophy of cognitive science, and philosophy of mind. In this physicalist context, the theory tries to combine central ideas present in traditional cognitive science with the main ideas present in certain fields of neuroscience that investigate human cognition. In this sense, some authors argue that this mechanistic physicalist framework can provide a consistent way to build a unified science of cognition and integrate cognitive science and neuroscience [23, 24, 25, 40].
Indeed, integrating and unifying, from a physicalist background, traditional cognitive science and traditional neuroscience to understand and investigate human cognition is an old dream held by many authors. Patricia Churchland, in 1986, calls for the unification of cognitive research and neural research in her book
It is possible to argue that MTHC was articulated with the objective of providing this integration and unification in a more precise theoretical way and within a clear physicalist background. The influential version of MTHC by William Bechtel is a clear example. He considers the human phenomenon “mind-brain” as “a set of mechanisms for controlling behavior” [9], and he explains that cognitive phenomena (e.g., perception, attention, memory, problem solving, and language) can be characterized as “information-processing mechanisms” [9]. Bechtel [9] states that scientific disciplines that aim to explain cognitive activities recognize that “in some way, these activities depend upon our brain.” Or, to put it in another way: “Psychological phenomena are realized in brains comprised of neurons” [45]. This means that cognitive phenomena are physical and need to be explained in some physical (neural) way.
Craver and Tabery [47] describe the physicalist commitment quite clearly—“many mechanists opt for some form of explanatory anti-reductionism, emphasizing the importance of multilevel and upward-looking explanations, without rejecting the central ideas that motivate a broad physicalist world-picture.” Therefore, in this approach, there is no space for any form of dualism, pluralism, or non-physicalism of any kind in relation to the ontology of human cognition. There is, indeed, a clear commitment to a form of ontological monism, namely, physicalism, that underlies the neo-mechanistic theory of human cognition.
Neo-mechanistic ideas about human cognitive phenomena are becoming increasingly dominant in fields related to theoretical cognitive science and cognitive neuroscience [48]. Consequently, the neo-mechanistic framework is often presented as one of the main theories, or the main theory, to explain human cognition in the twenty-first century.
Formulations of the idea that human cognition can be considered in computational terms can already arguably be found in the works of Thomas Hobbes (1588–1679) and Gottfried Leibniz (1646–1716). However, it is in the first half of the twentieth century that new developments in this tradition made the thesis gain great strength [49]. Alan Turing (1912–1954), with his work on computation, made a solid mathematical contribution to advances in the attempt to build machines capable of thinking like humans. And with the development of the computer and the emergence of studies in computer science and artificial intelligence, there was an even greater push for the acceptance of these ideas in the period. Indeed, these were crucial factors in the development of cognitive psychology in the 1950s and cognitive science (in the specific sense) in the 1970s. In discussing the foundations of cognitive science, Gardner [3] states that “there is the faith that central to any understanding of the human mind is the electronic computer.” Furthermore, according to him: “Involvement with computers, and belief in their relevance as a model of human thought, is pervasive in cognitive science” [3].
The first formulations of the philosophical foundations and the most central bases of the “computational theory of cognition” were presented, above all, in central works by Hilary Putnam (1926–2016) and Jerry Fodor (1935–2017). It is mainly based on works like these that the “classical model of cognitive computation” was formulated [49]. According to this proposal, the human mind is a computational system similar in important respects to a “Turing machine,” which works through “Turing-style computations.” In this view, cognitive processes, such as problem solving, decision making, and formal reasoning, are performed through computations similar to those of a Turing machine.
Another line of work, however, developed an alternative notion of cognitive computation. Inspired by research in the field of neurophysiology, some authors in the 1980s proposed that cognitive computation was something very different from Turing-style computation [50]. The correct format of cognitive computation for them was that of neural networks, in which, very briefly, data nodes are connected in a particular way so that when the network is activated through an input, it can provide an output. This framework became known as connectionism, and it has been developed in numerous works since then. Many cognitive models of different phenomena were built based on this view, such as object recognition, speech perception, and sentence comprehension.
The notion of “cognitive mechanistic computation” is part of this tradition, and it is especially related to the model of neural networks. Craver [10], for example, writes about the “computational properties of brain regions” and “computational properties of neural systems,” without giving much detail about what exactly this means. In any case, it is clear that the supposed computation is much more related to concrete properties of neural systems than to abstract functional properties of psychological capacities considered in terms of Turing computation or something similar. Milkowski [11], in turn, presents a proposal that holds that neurocognitive processing occurs over states that contain information, but he does not elaborate much on the content and the semantic dimension of cognitive information or of putative cognitive computations.
Bechtel [9, 19] considers mental mechanisms as information-processing mechanisms that operate through neural representations and neural computations about vehicles and content. In his view, the “control theory of dynamical systems” shows how content is placed in this context. And Thagard [14, 15] thinks that mental mechanisms operate through computations that take place on representations at the cognitive level and computations that take place at the neural and molecular levels. In Thagard’s work, there is also recourse to the “theory of dynamical systems” (as in Bechtel’s); however, just in his version of the mechanistic theory, there is a definite number of mechanistic levels and extensive discussion about the “semantic pointers theory” of Chris Eliasmith.
Finally, there is the work of Piccinini [12, 13, 51], which is one of the most theoretically sophisticated and detailed among neo-mechanists regarding such issues. The author defends a mechanistic neurocomputational theory of human cognition. In his view, the human nervous system is a functional mechanism that produces computations through the activation of neurons, while the processing occurs in vehicles according to rules. Cognitive capacities are explained then by multilevel neurocognitive mechanisms that perform neural computations over neural representations. Besides, he thinks that neural computation (i.e., computations defined on the functionally relevant elements of neural activity) is not purely digital, as classically understood, nor purely analog, as alternatively understood; in his view, neural computation is
One does not need to enter so deep into these individual theories to see that they differ significantly. Craver mentions computations but does not offer an elaborated account. Thagard is the only one mentioning semantic pointers as central to the account. Milkowski and Piccinini attempt to avoid the problems with content, by means of focusing on formal properties. And Bechtel uses control theory to deal with the issue of content. As a result, it is not possible to derive from those accounts a single theory, as each author develops his/her own point of view with its significant particularities. There is, therefore, no theoretical substantial unity among these proponents.
However, one can try to find common aspects to evaluate at least the most basic and important tenets. To do that, an analysis of two cases where this mechanistic view on human cognitive computation can be applied will be helpful.
One of the best examples found in the specialized literature of a concrete application of this view to particular cognitive phenomena is related to memory, which, indeed, has been traditionally an object of study in the field of psychology [9, 10]. Functional analyses of the human memory capacity reveal the existence of many sub-capacities, such as short-term memory, long-term memory, phonological memory, visuospatial memory, semantic memory, episodic memory, and memory consolidation. In mechanistic terms, one of the best-understood phenomena in this memory system is memory consolidation. Roughly put, this is the phenomenon of transforming short-term memories (which are liable and easy to disrupt) into long-term memories, which are robust and enduring, when consolidation takes place and permits the organism to remember important events for a longer period of time and modify its behavior accordingly [52]. To explain this phenomenon, all the relevant regions in the brain responsible for the functions that compose the neuro-cognitive mechanism of memory consolidation, including all relevant mechanistic levels of decomposition, must be identified, that is, all the particular component parts and component operations of the whole mechanism must be determined, as shown on Figure 1. Finally, the causal processes and causal interactions within the mechanism functions need also to be understood, that is, the general organization of the mechanism.
An example of a simple model of a neuro-cognitive biological mechanism (M1). In this model, M1 is composed, at the level L1, by its component parts C1, C2, and C3, which perform the functions (or activities) f1, f2, and f3. The component parts can be decomposed into smaller components, as it happens with C3, which is composed, at level L2, of the sub-components SC1, SC2, SC3, and SC4. The component SC3 can be further decomposed, at level L3, into its subcomponents ssc1, ssc2, and ssc3.
The explanation starts at the highest level of the whole mechanism. At this level, it is necessary to correctly identify all the large neural network that is responsible for memory consolidation. Secondly, it must be established whether this large neural system is indeed all that is relevant for the explanation of the phenomenon. The mechanistic explanation at this level also needs to clarify how the neural network process information about new memory episodes through
Once this has been clarified, the explanation turns to the second level of description in which the large neural system is decomposed into particular sub-neural systems localized in more specific regions. Here the goal is to understand the information processing and computational operations (e.g., spiking patterns in populations of neurons) of these smaller neural networks and how they contribute to the performance of the whole mechanism composed of such neural nets.
Moreover, a further stage of decomposition must be reached that concerns the processes underlying memory at an intercellular level. The explanation at this particular level aims at describing the components of a particular neural network and at understanding how a small number of neurons operate (e.g., how they depolarize and fire in the process of propagation of action potentials, or how they are responsible for synaptic processes, neurotransmitters being released, and so on). Here it is possible to measure spiking rates of neurons, or spiking frequency and record neural activity in general.
Finally, the explanation can go even to another lower mechanistic level—the intracellular and molecular level. At this level, the description is in terms of the activity of relevant proteins, molecules, and ions. As one can see, this kind of explanation “exhibits a progression from the behavioral-level characterization of memory consolidation to the identification of important components in the process at progressively lower levels.” [52]. All levels are equally important to achieve the complete multilevel mechanistic explanation of the particular phenomenon in the end.
Another example is related to human visual perception [9, 13, 40], which is roughly understood as the capacity to acquire and process visual information from objects and events in the environment. In the biological mechanism related to human visual perception, the occipital lobe is central, since many studies on humans show deficits in visual processing due to damage in the occipital lobe. The mechanism also includes a projection of the optic tract going from the eye, passing by the lateral geniculate nucleus (LGN), which is an area of the thalamus, and achieving the occipital lobe. Besides, it includes the eyes, optic nerves, and other brain areas responsible for visual perception. All these areas can be decomposed in working components and their operations, and each decomposition is considered to be a lower level in the entire constitution of the mechanism. The occipital lobe, for instance, can be itself decomposed in areas responsible for particular visual functions, such as the striate cortex, also known as Brodmann area 17, or V1 (primary visual cortex, or visual area 1).
The same procedure can be done for all the other areas in the brain that are also part of the mechanism responsible for visual perception; for instance, V2, V3, V4, and V5/MT. It is necessary to identify also the cells (including visual receptor cells in the retina of the eye, such as cones and rods), networks of cells, or larger neural systems in these areas that are responsible for
As one can observe by looking at these two examples, the notion of “computation” in the mechanistic framework stands for some causal interactions within the nervous system and this is how different brain regions “compute” different information. Each brain region “stands for” some kind of particular information—related to perception, sensation, memory, language, reasoning, emotion, etc. The substantial problems with such an account of human cognition will be analyzed in what follows.
A great deal of criticism has arisen in the specialized literature concerning the notion of human cognitive computation. It is nearly impossible to review all of the works, but I will make some considerations of some of the most influential critics.
Fodor [53, 54, 55], for instance, claims that many mental representations (e.g., beliefs) and mental processes (e.g., abductive reasoning) are sensitive to global properties (i.e., properties that beliefs, for instance, have so that they are determined by a set of other beliefs which they are members of). For example, a belief about a tennis racket being broken may complicate the plan of playing tennis on the weekend, but not the plan of playing soccer. This means that a mental representation, such as an intention to play tennis, will depend on the context at the moment—whether there is a racket available for the game or not. Fodor argues, though, that classical symbolic computing models are only sensitive to local properties, and neural network models cannot handle this feature of human cognition.
Dreyfus [56], in turn, claims that much human knowledge cannot be captured by symbolic manipulation and formal rules, since this knowledge is constructed through direct contact and practicing in the world. Nagel [57] brings attention to the problem of phenomenal consciousness—roughly, the issue of what it feels like to experience something subjectively. Following this line of thinking, we can also say that a computer cannot know (if it can know anything) what it feels like to taste the flavor of chocolate. It has no idea of what it is like to eat chocolate, something that is quite basic for any child that does it. More than that, computers do not feel pain or pleasure, which is quite basic for human beings. Furthermore, Searle [58] brings attention to the difficulties related to intentionality, understanding, and meaning, with his famous “Chinese room argument.” And, additionally, Putnam [59] develops the idea that mental states cannot be identified with computational states, consequently arguing vigorously against computational reductionism3.
The case of Bruner’s critics is also very interesting. One of the names most frequently mentioned in influential works of historical reconstruction of the events and studies that contributed to the beginning and development of the cognitive movement in psychology is the American psychologist Jerome Bruner (1915–2016) [1, 2, 3, 60, 61]. He is recognized for having founded, together with George Miller (1920–2012), the Center for Cognitive Studies at Harvard University, in 1960. In addition, Bruner published, together with colleagues, in 1956,
One of the most interesting points in Bruner’s work, however, is his strong criticism of the very cognitive movement he helped to develop. He has presented this criticism in key works, such as
In
However, in Bruner’s view, this is not the way forward. In
One of the major problems pointed out by Bruner in the computationalist approach is that the production of meaning is often extremely complex, sensitive to the context, and involves the difficulty of clear and precise understanding [64]. This is not the same as establishing computational procedures for the processing of input and output information to the system, whether this is computational processing in digital format or the form of neural networks. For Bruner, meaning making is not merely information processing; it is something more profound and more complex. Culture, in his view, has a fundamental role in human life and it is only through it and in it that certain processes and mental structures are formed and used.
The human being, in Bruner’s view, was able to develop a way of life in which reality is represented by a symbolism shared by members of a cultural community, and human life is organized and built from this symbolism that is conserved, elaborated, and transmitted through successive generations [64]. Although meaning is in the mind and is produced by it, it also has its origins in culture and has its importance within the culture in which it was generated. And for the production of meanings, the human mind creates and makes use of symbolic cultural systems. Thus, in this view, thinking and learning are always situated in a cultural context [64]. Computer systems, however, are not capable of producing meanings. They only deal with a certain set of formalized and operationalized meanings, but they do not make interpretations of human and cultural phenomena.
Furthermore, there is no very clear reason to suppose that processes and relationships between all mental phenomena are literally computational in nature, nor that all mental representations have this same character. The application of the concept of computation to these phenomena investigated in the tradition of psychological research is based only on a working hypothesis present in a certain particular theoretical system. Nevertheless, there is as yet no concrete proof that all human cognition works according to a type of computational processing x, y, or z. In fact, finding out what kind of computational processing is related to the human mind has become an extremely debated issue internally by adherents of any computational model of human cognition [49]. It is no accident that comprehensive theoretical systems were developed precisely with the intention of questioning the computational model of cognition.
Now, to illustrate more concretely some of the difficulties mentioned with the notion of human cognitive computation, let us consider some cases involving conscious complex informal reasoning and conscious complex decision making where explanations for human behavior might be required [22].
Consider, firstly, a case where a person is dissatisfied with her marriage and is thinking about getting a divorce. To make such a decision, she has been consciously reflecting for months on the current state of the marriage, her beliefs about the relationship, her emotions about her partner, her desires and expectations in life, the beliefs of her family and closest friends about the issue and what are the reasons to take action in this regard. After thinking carefully for a very long time, being aware that she really does not feel comfortable and happy at all, she decides to go for a divorce.
Consider also a second example. A person needs to decide which candidate she will vote for as president of her country. To make this decision, she needs to use her conscious informal reasoning ability. Thus, she reflects on the arguments put forward by politicians running for the election, the arguments put forward by commentators, scientists, and political analysts, as well as journalists writing on the subject, and the arguments of friends and family she finds relevant and credible. After three months of thinking, she has not decided yet but is rather still in doubt concerning her vote in the major candidates A and B. When someone asks her which candidate she is going to vote, she says: “I still don’t know.” Then, some surprising news arises in a serious newspaper with charges of corruption against candidate A, and she is a frequent reader of this newspaper, so she becomes immediately aware of this. Upon reflection on the matter and related issues, she takes the new information seriously and she finally decides that voting for candidate B is the best option. The major reason is that there is no charge whatsoever of corruption against him. When she is asked now which candidate she is going to vote for, she answers immediately: “candidate B.” After she made up her mind, she finally goes to the appropriate place on the proper day and time to cast her vote.
A third example is the case of a college student who suffers from difficulties related to his excessive anxiety. Through a general psychological assessment, it can be seen that the factors related to student anxiety are financial difficulties, difficulties in family life where physical and psychological violence occurs, difficulties in finding leisure time to relax and have fun (since they need to work and study at the same time) and difficulties with excessive concerns about the uncertain future, as he believes that it will not be easy to find a job when he graduates. All of these factors seem to contribute to generate in the student’s mind distorted and dysfunctional negative thoughts about himself and his life, and it seems very plausible that these distorted thoughts are strongly associated with his excessive anxiety. This interpretation is, indeed, supported by numerous works in the specialized literature in clinical psychology. Thus, we observe that the most relevant causal factors to explain this psychological phenomenon are not merely computational, but psychological, social, and environmental.
Psychological scientific explanations, in these cases, need considerations that go beyond the investigation of computations being performed in nervous systems or even in any abstract functional system. What explains the psychological phenomenon of belief formation and decision making in the first example and the excessive anxiety in the third example is the meaning formation and interaction of beliefs, desires, and intentions to act (according to logical rules, practical rules, and interpretation of reality), which are strongly affected by emotions, physical environment, and social factors.
In the second example, evidently, an informative explanation would have to mention an important causal factor—the event of the corruption charges against candidate A, appearing in a serious newspaper. Moreover, the explanation would have to mention that the person becomes aware of this event, accepts it as reliable, accepts the charges as true and accurate, and now this content is present in one or some of her beliefs. In possession of this content, she can rationally justify herself when engaging in discussions about the topic with family, friends, and other people, providing reasons for her related beliefs and her related behaviors. Thus, the influence of the event on her is external and affects the internal logic and content of her systems of beliefs, emotions, desires, and intentions. This explanation involves then particular properties of human cognitive systems, present for instance in belief and intention systems. These properties are clearly different than those involved in merely describing supposed automatic computational activities in her neural networks or describing what is happening in terms of physical and chemical neural processes. The explanation for this phenomenon of belief formation, therefore, would also have to account for how this new information could change a particular belief given her system of beliefs about the topic.
In the examples above, there are cognitive processes that often necessitate consciousness and complex informal reasoning about belief systems that are often linked to particular perceptions, sensations, emotions, desires, intentions, attitudes, as well as related to each other and the external environment. Some of these beliefs have great value, such as some moral beliefs, which makes this whole dynamic even more complex. In these cases, blind computation might even occur at some level, but what is most relevant are environmental, social, cultural, historical, and psychological factors (such as beliefs, emotions, desires, and intentions) that acquire meaning in a given cognitive system.
The relevant explanation of the actions in such cases is made through considerations—(1) about the creation and alteration of the content of perceptions, beliefs, sensations, emotions, maxims, wills, desires, intentions, etc.; (2) about their internal relationships; and (3) about their external relationships with the physical, social, historical, and cultural context. Rigorous empirical scientific research can aid in discovering strong and systematic (stable) regularities in human behavior explained in such terms without the need for the notion of computation. Statistical tools and analysis, through the mathematical application, can bring greater objectivity, avoiding both an extremely subjective and confusing vocabulary, as well as unproductive speculation and mere common sense.
Moreover, self-consciousness here is crucial, since we humans have the ability to
Therefore, human beings have the ability to form original belief systems and relate them according to logical and interpretative rules, building arguments to support their point of view, which often influences their behavior. Human beings are also able to think about different types of relevant information for months or years to make an important and complex decision. To make a difficult decision, a human being can take into account information related to plans for the very distant future, in which many scenarios are considered. A human may wonder what happened in the very distant past, or what might have happened, even if he or she knew what really happened. And complex informal reasoning and complex decision making are things that humans do naturally and often in their daily lives.
Thus, in cognitive science, it is necessary to deal with extremely complex phenomena, given that human beings show great differences when compared to other animals in nature. Human beings have a cumulative, complex, dynamic, and elaborate culture that is passed on through generations. Humans are also involved in understanding and writing their own history. They have natural languages with enormous, complex, and refined expressive power and sophisticated grammar. Human beings practice and appreciate art, such as literature, painting, cinema, and music. They engage in purely formal or very abstract thoughts when they do mathematics, logic, and engage in certain religious thoughts. They create legal laws for their societies and think about morality, building moral systems. They build artificial intelligence machines that are able to learn with a certain level of autonomy and are able to explore other planets. Furthermore, humans are involved in politics, science, and philosophy.
Computers, by contrast, so far, do not form beliefs on their own, they do not have the capacity to evaluate and improve them by themselves, and they do not interact in the social environment neither using natural language with a huge degree of sophistication as humans do nor engaging in social and cultural practices. If we look at the problem from a very concrete and objective point of view, we observe that even the most advanced computer systems, the most advanced robots, and the most advanced artificial neural and cognitive architectures today are still very far from behaving like human beings in relation to language and actions that involve consciousness and informal rationality. Humans are capable of playing chess, cooking pizza, making coffee, having a conversation about politics, creating a new song on a guitar, and playing tennis on the same day. No computational artificial system is currently capable of this generality in cognition. So, as a matter of current fact, computational artificial cognition cannot be used to fully explain the major capacities of human cognition and intelligence.
It is no surprise, then, that mechanistic accounts of psychological capacities usually suggest only
Difficulties with the notion of cognitive computation are recognized by influential neo-mechanists themselves. Milkowski [21], for instance, concludes his work by admitting that we “still don’t know how to model consciousness mechanistically.” Additionally, there are several alternative models of cognitive computation in cognitive science nowadays—syntactic computation; algorithmic computation; causal computation; and semantic computation [65]. None of the models has gained significant prominence over the others concerning the understanding and explanation of human cognition. Finally, there is strong criticism even of the attempt by neo-mechanists to propose that good computational explanations in cognitive science must be also mechanistic explanations [66, 67].
Therefore, if we think about the issue from the point of view of current facts, we need to recognize that the neo-mechanistic proposal for human cognition is still far from being able to be considered the best or most plausible understanding and explanation of human cognition. It is just one view among many.
The mechanistic framework has been offering significant contributions to the field of cognitive science, on the one hand. One of its best contributions is the promotion of debates on the issue of human cognitive computation. In this sense, there is a search for a better understanding of what this notion actually means. All this effort is very worthwhile and welcome. More generally, the theoretical debate about fundamental questions in cognitive science promoted by new mechanists is also very important, as well as their effort to clarify what a “biological mechanism” and a “cognitive mechanism” are and what a “mechanistic explanation” in cognitive science is. Furthermore, another contribution of the new mechanistic philosophy is to encourage historical research and current debate, in cognitive science and beyond, about the relationship among “mechanism,” “materialism,” “reductionism” and “computationalism”, so that these concepts are not confused and that the positions adopted by the authors, as well as the different dimensions of the debate, are appreciated in a fair and correct way. Finally, the new mechanistic philosophy applied to cognitive science is also contributing to the important debate concerning the unity, integration, and plurality in the field.
On the other hand, however, many of the current promises of the new mechanism for cognitive science are quite difficult to fulfill. Firstly, neo-mechanistic philosophy is a philosophy of science built primarily from examples from the biological sciences and neuroscience that is serving as the basis for building a philosophy of the science of mind. We live in a period in which neuroscience and artificial intelligence research have gained great prestige and recognition. A great deal of economic investment has been made in these areas and this is very attractive. In part, this also influences “the new wave of mechanism,” and the necessity of some authors to expand the framework. However, numerous particularities related to psychology and human cognition are being neglected in this theoretical structure, as I tried to show.
Secondly, there is considerable disagreement among leading neo-mechanists over the most plausible formulation of MTHC regarding fundamental issues, such as the idea of human cognitive computation. Thus, there is a considerable difficulty related to the internal articulation and unification of the theory. Furthermore, many alternative major theories, and the research programs based on them, strongly threaten the neo-mechanistic framework in current cognitive science, since they are also seeking predominance in the field, or just for having more space and recognition.
Given this, we can conclude that the mechanistic model of human cognitive computation cannot provide substantial theoretical or explanatory unification or integration to the field of cognitive science today, since there is no unification between the proponents themselves. Moreover, their different proposals are often unclear on many important aspects concerning traditional problems of intentionality, consciousness, and self-consciousness. The accounts are sometimes internally not well-articulated; and, externally, there is serious criticism of them, with countless debates and controversies on several fundamental questions. In addition, there are several alternative models competing for predominance on this particular issue. And it is yet by no means clear whether the explanatory power of any of them is greater than the explanatory power of the others.
This analysis shows, therefore, that the neo-mechanistic proposal concerning human cognitive computation has serious weaknesses. But the problem is not to use the idea of cognitive computing to advance models of biological and artificial cognitive architectures, since many human cognitive abilities can already be simulated. Indeed, it is very interesting to see that our science has advanced to the point where a computer can win against the best chess and go game players in the world. In fact, advancements within computational artificial systems and robotics could well be applied to improve our educational and health systems. For example, inspired by scientific developments in the field of cognitive science, artificial cognitive systems could possibly be developed to help children with the learning process of mathematics, natural language, or history at schools, or even at the university level. Artificial systems could possibly be developed to help people with excessive anxiety symptoms, as well. This could be extremely worthwhile. Moreover, better and more advanced artificial cognitive systems and robotic systems can contribute to improving theories of human cognition, as much as better and more correct theories of human cognition can help in faster advancements of cognitive artificial systems and robotic systems. But there is good reason to keep these efforts separated and to consider human cognition as a very complex and particular phenomenon in nature.
The problem arises only with the untenable suggestion that we already have, or that we are very close to getting, the complete and definitive understanding and explanation of all the major capacities of human cognition in computational terms. This, yes, is a mistake.
The author declares no conflict of interest.
Authors are listed below with their open access chapters linked via author name:
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\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
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\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
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\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
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\n\nFei Wei 2016-18
\n\nIoannis Xenarios 2017, 2018
\n\nQi Xie 2016-18
\n\nXin-She Yang 2017, 2018
\n\nYulong Yin 2015, 2017, 2018
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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:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{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. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. 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His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. 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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Over the past few decades, no major new types of antibiotics have been produced and almost all known antibiotics are increasingly losing their activity against pathogenic microorganisms. The levels of multi-drug resistant bacteria have also increased. It is known that worldwide, more than 60% of all antibiotics that are produced find their use in animal production for both therapeutic and non-therapeutic purposes. The use of antimicrobial agents in animal husbandry has been linked to the development and spread of resistant bacteria. Poultry products are among the highest consumed products worldwide but a lot of essential antibiotics are employed during poultry production in several countries; threatening the safety of such products (through antimicrobial residues) and the increased possibility of development and spread of microbial resistance in poultry settings. This chapter documents some of the studies on antibiotic usage in poultry farming; with specific focus on some selected bacterial species, their economic importance to poultry farming and reports of resistances of isolated species from poultry settings (farms and poultry products) to essential antibiotics.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Christian Agyare, Vivian Etsiapa Boamah, Crystal Ngofi Zumbi and\nFrank Boateng Osei",authors:[{id:"182058",title:"Dr.",name:"Christian",middleName:null,surname:"Agyare",slug:"christian-agyare",fullName:"Christian Agyare"},{id:"261271",title:"MSc.",name:"Crystal Ngofi",middleName:null,surname:"Zumbi",slug:"crystal-ngofi-zumbi",fullName:"Crystal Ngofi Zumbi"},{id:"261272",title:"MSc.",name:"Frank Boateng",middleName:null,surname:"Osei",slug:"frank-boateng-osei",fullName:"Frank Boateng Osei"},{id:"261273",title:"Dr.",name:"Vivian Etsiapa",middleName:null,surname:"Boamah",slug:"vivian-etsiapa-boamah",fullName:"Vivian Etsiapa Boamah"}]},{id:"49246",doi:"10.5772/61300",title:"Chitosan as a Biomaterial — Structure, Properties, and Electrospun Nanofibers",slug:"chitosan-as-a-biomaterial-structure-properties-and-electrospun-nanofibers",totalDownloads:4727,totalCrossrefCites:27,totalDimensionsCites:63,abstract:"Chitosan is a polysaccharide derived from chitin; chitin is the second most abundant polysaccharide in the world, after cellulose. Chitosan is biocompatible, biodegradable and non-toxic, so that it can be usedin medicalapplications such as antimicrobial and wound healing biomaterials. It also used as chelating agent due to its ability to bind with cholesterol, fats, proteins and metal ions.",book:{id:"4648",slug:"concepts-compounds-and-the-alternatives-of-antibacterials",title:"Concepts, Compounds and the Alternatives of Antibacterials",fullTitle:"Concepts, Compounds and the Alternatives of Antibacterials"},signatures:"H. M. Ibrahim and E.M.R. El- Zairy",authors:[{id:"90645",title:"Dr.",name:"Hassan",middleName:null,surname:"Ibrahim",slug:"hassan-ibrahim",fullName:"Hassan Ibrahim"},{id:"175694",title:"Dr.",name:"Enas",middleName:null,surname:"El- Zairy",slug:"enas-el-zairy",fullName:"Enas El- Zairy"}]},{id:"70919",doi:"10.5772/intechopen.90891",title:"Antimicrobial Effect of Titanium Dioxide Nanoparticles",slug:"antimicrobial-effect-of-titanium-dioxide-nanoparticles",totalDownloads:1817,totalCrossrefCites:21,totalDimensionsCites:47,abstract:"The widespread use of antibiotics has led to the emergence of multidrug-resistant bacterial strains, and therefore a current concern for food safety and human health. The interest for new antimicrobial substances has been focused toward metal oxide nanoparticles. Specifically, titanium dioxide (TiO2) has been considered as an attractive antimicrobial compound due to its photocatalytic nature and because it is a chemically stable, non-toxic, inexpensive, and Generally Recognized as Safe (GRAS) substance. Several studies have revealed this metal oxide demonstrates excellent antifungal and antibacterial properties against a broad range of both Gram-positive and Gram-negative bacteria. These properties were significantly improved by titanium dioxide nanoparticles (TiO2 NPs) synthesis. In this chapter, latest developments on routes of synthesis of TiO2 NPs and antimicrobial activity of these nanostructures are presented. Furthermore, TiO2 NPs favor the inactivation of microorganisms due to their strong oxidizing power by free radical generation, such as hydroxyl and superoxide anion radicals, showing reductions growth against several microorganisms, such as Escherichia coli and Staphylococcus aureus. Understanding the main mechanisms of antimicrobial action of these nanoparticles was the second main purpose of this chapter.",book:{id:"9521",slug:"antimicrobial-resistance-a-one-health-perspective",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A One Health Perspective"},signatures:"Carol López de Dicastillo, Matias Guerrero Correa, Fernanda B. Martínez, Camilo Streitt and Maria José Galotto",authors:[{id:"244902",title:"Dr.",name:"Carol",middleName:null,surname:"Lopez De Dicastillo",slug:"carol-lopez-de-dicastillo",fullName:"Carol Lopez De Dicastillo"},{id:"315494",title:"Mr.",name:"Matias",middleName:null,surname:"Guerrero Correa",slug:"matias-guerrero-correa",fullName:"Matias Guerrero Correa"},{id:"315495",title:"Ms.",name:"Fernanda",middleName:null,surname:"B. Martínez",slug:"fernanda-b.-martinez",fullName:"Fernanda B. Martínez"},{id:"315496",title:"Mr.",name:"Camilo",middleName:null,surname:"Zuñiga",slug:"camilo-zuniga",fullName:"Camilo Zuñiga"},{id:"315497",title:"Dr.",name:"Maria José",middleName:null,surname:"Galotto",slug:"maria-jose-galotto",fullName:"Maria José Galotto"}]},{id:"65613",doi:"10.5772/intechopen.84411",title:"The Methods for Detection of Biofilm and Screening Antibiofilm Activity of Agents",slug:"the-methods-for-detection-of-biofilm-and-screening-antibiofilm-activity-of-agents",totalDownloads:9283,totalCrossrefCites:15,totalDimensionsCites:26,abstract:"Biofilm producer microorganisms cause nosocomial and recurrent infections. Biofilm that is a sticky exopolysaccharide is the main virulence factor causing biofilm-related infections. Biofilm formation begins with attachment of bacteria to biotic surface such as host cell or abiotic surface such as prosthetic devices. After attachment, aggregation of bacteria is started by cell-cell adhesion. Aggregation continues with the maturation of biofilm. Dispersion is started by certain conditions such as phenol-soluble modulins (PSMs). By this way, sessile bacteria turn back into planktonic form. Bacteria embedded in biofilm (sessile form) are more resistant to antimicrobials than planktonic bacteria. So it is hard to treat biofilm-embedded bacteria than planktonic forms. For this reason, it is important to detect biofilm. There are a few biofilm detection and biofilm production methods on prosthetics, methods for screening antibacterial effect of agents against biofilm-embedded microorganism and antibiofilm effect of agents against biofilm production and mature biofilm. The aim of this chapter is to overview direct and indirect methods such as microscopy, fluorescent in situ hybridization, and Congo red agar, tube method, microtiter plate assay, checkerboard assay, plate counting, polymerase chain reaction, mass spectrometry, MALDI-TOF, and biological assays used by antibiofilm researches.",book:{id:"8427",slug:"antimicrobials-antibiotic-resistance-antibiofilm-strategies-and-activity-methods",title:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods",fullTitle:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods"},signatures:"Sahra Kırmusaoğlu",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"}]},{id:"63397",doi:"10.5772/intechopen.80624",title:"Antibiotic Resistance in Lactic Acid Bacteria",slug:"antibiotic-resistance-in-lactic-acid-bacteria",totalDownloads:2486,totalCrossrefCites:12,totalDimensionsCites:21,abstract:"Most starter cultures belong to the lactic acid bacteria group (LAB) and recognized as safe by the US Food and Drug Administration (FDA) and the European Food Safety Authority (EFSA). However, LAB may act as intrinsic or extrinsic reservoirs for antibiotic resistance (AR) genes. This fact may not constitute a safety concern itself, as the resistance gene transfer is vertical. Nevertheless, external genetic elements may induce changes that favor the horizontal transfer transmission of resistance from pathogens as well as from the human intestinal microbiota, which represents a severe safety issue. Some genus of AR LAB includes Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, and Streptococcus isolated from fermented meat and milk products. Currently, the WHO recommends that LAB used in the food industry should be free of resistance. Therefore, the objective of this chapter is to present an overview of the LAB antibiotic resistance and some methods to determine the same.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Yenizey M. 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After attachment, aggregation of bacteria is started by cell-cell adhesion. Aggregation continues with the maturation of biofilm. Dispersion is started by certain conditions such as phenol-soluble modulins (PSMs). By this way, sessile bacteria turn back into planktonic form. Bacteria embedded in biofilm (sessile form) are more resistant to antimicrobials than planktonic bacteria. So it is hard to treat biofilm-embedded bacteria than planktonic forms. For this reason, it is important to detect biofilm. There are a few biofilm detection and biofilm production methods on prosthetics, methods for screening antibacterial effect of agents against biofilm-embedded microorganism and antibiofilm effect of agents against biofilm production and mature biofilm. The aim of this chapter is to overview direct and indirect methods such as microscopy, fluorescent in situ hybridization, and Congo red agar, tube method, microtiter plate assay, checkerboard assay, plate counting, polymerase chain reaction, mass spectrometry, MALDI-TOF, and biological assays used by antibiofilm researches.",book:{id:"8427",slug:"antimicrobials-antibiotic-resistance-antibiofilm-strategies-and-activity-methods",title:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods",fullTitle:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods"},signatures:"Sahra Kırmusaoğlu",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"}]},{id:"62553",title:"Antibiotic Use in Poultry Production and Its Effects on Bacterial Resistance",slug:"antibiotic-use-in-poultry-production-and-its-effects-on-bacterial-resistance",totalDownloads:7327,totalCrossrefCites:43,totalDimensionsCites:92,abstract:"A surge in the development and spread of antibiotic resistance has become a major cause for concern. Over the past few decades, no major new types of antibiotics have been produced and almost all known antibiotics are increasingly losing their activity against pathogenic microorganisms. The levels of multi-drug resistant bacteria have also increased. It is known that worldwide, more than 60% of all antibiotics that are produced find their use in animal production for both therapeutic and non-therapeutic purposes. The use of antimicrobial agents in animal husbandry has been linked to the development and spread of resistant bacteria. Poultry products are among the highest consumed products worldwide but a lot of essential antibiotics are employed during poultry production in several countries; threatening the safety of such products (through antimicrobial residues) and the increased possibility of development and spread of microbial resistance in poultry settings. This chapter documents some of the studies on antibiotic usage in poultry farming; with specific focus on some selected bacterial species, their economic importance to poultry farming and reports of resistances of isolated species from poultry settings (farms and poultry products) to essential antibiotics.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Christian Agyare, Vivian Etsiapa Boamah, Crystal Ngofi Zumbi and\nFrank Boateng Osei",authors:[{id:"182058",title:"Dr.",name:"Christian",middleName:null,surname:"Agyare",slug:"christian-agyare",fullName:"Christian Agyare"},{id:"261271",title:"MSc.",name:"Crystal Ngofi",middleName:null,surname:"Zumbi",slug:"crystal-ngofi-zumbi",fullName:"Crystal Ngofi Zumbi"},{id:"261272",title:"MSc.",name:"Frank Boateng",middleName:null,surname:"Osei",slug:"frank-boateng-osei",fullName:"Frank Boateng Osei"},{id:"261273",title:"Dr.",name:"Vivian Etsiapa",middleName:null,surname:"Boamah",slug:"vivian-etsiapa-boamah",fullName:"Vivian Etsiapa Boamah"}]},{id:"65914",title:"Introductory Chapter: The Action Mechanisms of Antibiotics and Antibiotic Resistance",slug:"introductory-chapter-the-action-mechanisms-of-antibiotics-and-antibiotic-resistance",totalDownloads:4428,totalCrossrefCites:6,totalDimensionsCites:10,abstract:null,book:{id:"8427",slug:"antimicrobials-antibiotic-resistance-antibiofilm-strategies-and-activity-methods",title:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods",fullTitle:"Antimicrobials, Antibiotic Resistance, Antibiofilm Strategies and Activity Methods"},signatures:"Sahra Kırmusaoğlu, Nesrin Gareayaghi and Bekir S. Kocazeybek",authors:[{id:"179460",title:"Associate Prof.",name:"Sahra",middleName:null,surname:"Kırmusaoğlu",slug:"sahra-kirmusaoglu",fullName:"Sahra Kırmusaoğlu"},{id:"248288",title:"Prof.",name:"Bekir",middleName:null,surname:"Kocazeybek",slug:"bekir-kocazeybek",fullName:"Bekir Kocazeybek"},{id:"406463",title:"Dr.",name:"Nesrin",middleName:null,surname:"Gareayaghi",slug:"nesrin-gareayaghi",fullName:"Nesrin Gareayaghi"}]},{id:"63397",title:"Antibiotic Resistance in Lactic Acid Bacteria",slug:"antibiotic-resistance-in-lactic-acid-bacteria",totalDownloads:2486,totalCrossrefCites:12,totalDimensionsCites:21,abstract:"Most starter cultures belong to the lactic acid bacteria group (LAB) and recognized as safe by the US Food and Drug Administration (FDA) and the European Food Safety Authority (EFSA). However, LAB may act as intrinsic or extrinsic reservoirs for antibiotic resistance (AR) genes. This fact may not constitute a safety concern itself, as the resistance gene transfer is vertical. Nevertheless, external genetic elements may induce changes that favor the horizontal transfer transmission of resistance from pathogens as well as from the human intestinal microbiota, which represents a severe safety issue. Some genus of AR LAB includes Enterococcus, Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, and Streptococcus isolated from fermented meat and milk products. Currently, the WHO recommends that LAB used in the food industry should be free of resistance. Therefore, the objective of this chapter is to present an overview of the LAB antibiotic resistance and some methods to determine the same.",book:{id:"6978",slug:"antimicrobial-resistance-a-global-threat",title:"Antimicrobial Resistance",fullTitle:"Antimicrobial Resistance - A Global Threat"},signatures:"Yenizey M. Álvarez-Cisneros and Edith Ponce-Alquicira",authors:[{id:"256345",title:"Dr.",name:"Yenizey Merit",middleName:null,surname:"Alvarez Cisneros",slug:"yenizey-merit-alvarez-cisneros",fullName:"Yenizey Merit Alvarez Cisneros"},{id:"256347",title:"Dr.",name:"Edith",middleName:null,surname:"Ponce-Alquicira",slug:"edith-ponce-alquicira",fullName:"Edith Ponce-Alquicira"}]},{id:"49246",title:"Chitosan as a Biomaterial — Structure, Properties, and Electrospun Nanofibers",slug:"chitosan-as-a-biomaterial-structure-properties-and-electrospun-nanofibers",totalDownloads:4726,totalCrossrefCites:27,totalDimensionsCites:63,abstract:"Chitosan is a polysaccharide derived from chitin; chitin is the second most abundant polysaccharide in the world, after cellulose. Chitosan is biocompatible, biodegradable and non-toxic, so that it can be usedin medicalapplications such as antimicrobial and wound healing biomaterials. It also used as chelating agent due to its ability to bind with cholesterol, fats, proteins and metal ions.",book:{id:"4648",slug:"concepts-compounds-and-the-alternatives-of-antibacterials",title:"Concepts, Compounds and the Alternatives of Antibacterials",fullTitle:"Concepts, Compounds and the Alternatives of Antibacterials"},signatures:"H. M. Ibrahim and E.M.R. El- Zairy",authors:[{id:"90645",title:"Dr.",name:"Hassan",middleName:null,surname:"Ibrahim",slug:"hassan-ibrahim",fullName:"Hassan Ibrahim"},{id:"175694",title:"Dr.",name:"Enas",middleName:null,surname:"El- Zairy",slug:"enas-el-zairy",fullName:"Enas El- Zairy"}]}],onlineFirstChaptersFilter:{topicId:"897",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81704",title:"Quorum Sensing Inhibition Based Drugs to Conquer Antimicrobial Resistance",slug:"quorum-sensing-inhibition-based-drugs-to-conquer-antimicrobial-resistance",totalDownloads:22,totalDimensionsCites:0,doi:"10.5772/intechopen.104125",abstract:"Quorum sensing is the cell to cell communication mechanism in microorganism through signalling molecules. Regulation of virulence factor, sporulation, proteolytic enzymes production, biofilm formation, auto-inducers, cell population density are key physiological process mediated through quorum-sensing (QS) signalling. Elevation of innate immune system and antibiotic tolerance of pathogens is highly increased with perspective of quorum-sensing (QS) activity. Development of novel drugs is highly attractive scenario against cell-cell communication of microbes. Design of synthetic drugs and natural compounds against QS signal molecules is vital combat system to attenuate microbial pathogenicity. Quorum sensing inhibitors (QSIs), quorum quenchers (QQs), efflux pump inhibitors (EPIs) act against multi-drug resistance strains (MDR) and other pathogenic microbes through regulation of auto-inducers and signal molecule with perceptive to growth arrest both in-vitro and in-vivo. QQs, QSIs and EPIs compounds has been validated with various animal models for high selection pressure on therapeutics arsenal against microbe’s growth inhibition. Promising QSI are phytochemicals and secondary metabolites includes polyacetylenes, alkaloids, polyphenols, terpenoids, quinones.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Kothandapani Sundar, Ramachandira Prabu and Gopal Jayalakshmi"},{id:"82372",title:"Unlocking the Potential of Ghost Probiotics in Combating Antimicrobial Resistance",slug:"unlocking-the-potential-of-ghost-probiotics-in-combating-antimicrobial-resistance",totalDownloads:20,totalDimensionsCites:0,doi:"10.5772/intechopen.104126",abstract:"Antimicrobial resistance is a global concern that requires immediate attention. Major causes of development of antimicrobial resistance in microbial cells are overuse of antimicrobials along the food chain especially in livestock, in preventing infections as well as misuse of antimicrobials by patients. Probiotics could be a viable alternative to antibiotics in the fight against antimicrobial resistance. Probiotic strains can act as a complement to antimicrobial therapy, improving antimicrobial function and enhancing immunity. However, there are safety concerns regarding the extensive use of live microbial cells especially in immunocompromised individuals; these include microbial translocation, inhibition of other beneficial microorganisms and development of antimicrobial resistance, among other concerns. Inevitably, ghost probiotics have become the favored alternative as they eliminate the safety and shelf-life problems associated with use of probiotics. Ghost probiotics are non-viable microbial cells (intact or broken) or metabolic products from microorganisms, which when administered in adequate amounts have biologic activity in the host and confer health benefits. Ghost probiotics exert biological effects similar to probiotics. However, the major drawback of using ghost probiotics is that the mechanism of action of these is currently unknown, hence more research is required and regulatory instruments are needed to assure the safety of consumers.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Abigarl Ndudzo, Sakhile Ndlovu, Nesisa Nyathi and Angela Sibanda Makuvise"},{id:"82178",title:"Managing Antimicrobial Resistance beyond the Hospital Antimicrobial Stewardship: The Role of One Health",slug:"managing-antimicrobial-resistance-beyond-the-hospital-antimicrobial-stewardship-the-role-of-one-heal",totalDownloads:16,totalDimensionsCites:0,doi:"10.5772/intechopen.104170",abstract:"Infections caused by micro-organisms affect the health of people and animals, causing morbidity and mortality, with Asia and Africa as the epicenters. Some of the infectious diseases are emerging and re-emerging in nature. Examples include viral hepatitis, Lassa fever, Ebola, yellow fever, tuberculosis, covid-19, measles, and malaria, among others. Antimicrobials have been playing an important role in the treatment of infections by these microbes. However, there has been a development of resistance to these antimicrobials as a result of many drivers. This write-up used secondary data to explore the management of antimicrobial resistance (AMR) beyond the hospital antimicrobial resistance steward using the one health concept. The findings showed AMR to be a transboundary, multifaceted ecosystem problem affecting both the developed and developing countries. It is also one of the top ten global public health threats facing mankind. Globally, AMR will cost over US$100 trillion in output loss by 2050, about 700,000 deaths a year, and 4,150,000 deaths in Africa by 2050. About 2.4 million people could die in high-income countries between 2015 and 2050 without a sustained effort to contain AMR. The drivers of AMR are beyond the hospital and hospital AMR stewardship. Therefore, the need for one health concept to manage it.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Istifanus Anekoson Joshua, Mathew Bobai and Clement Sokfa Woje"},{id:"81918",title:"Machine Learning for Antimicrobial Resistance Research and Drug Development",slug:"machine-learning-for-antimicrobial-resistance-research-and-drug-development",totalDownloads:53,totalDimensionsCites:0,doi:"10.5772/intechopen.104841",abstract:"Machine learning is a subfield of artificial intelligence which combines sophisticated algorithms and data to develop predictive models with minimal human interference. This chapter focuses on research that trains machine learning models to study antimicrobial resistance and to discover antimicrobial drugs. An emphasis is placed on applying machine learning models to detect drug resistance among bacterial and fungal pathogens. The role of machine learning in antibacterial and antifungal drug discovery and design is explored. Finally, the challenges and prospects of applying machine learning to advance basic research on and treatment of antimicrobial resistance are discussed. Overall, machine learning promises to advance antimicrobial resistance research and to facilitate the development of antibacterial and antifungal drugs.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Shamanth A. Shankarnarayan, Joshua D. Guthrie and Daniel A. Charlebois"},{id:"81891",title:"Alternatives to Antibiotics in Semen Extenders Used in Artificial Insemination",slug:"alternatives-to-antibiotics-in-semen-extenders-used-in-artificial-insemination",totalDownloads:29,totalDimensionsCites:0,doi:"10.5772/intechopen.104226",abstract:"Antimicrobial resistance is a serious global threat requiring a widespread response. Both veterinarians and medical doctors should restrict antibiotic usage to therapeutic use only, after determining the sensitivity of the causal organism. However, the addition of antibiotics to semen extenders for animal artificial insemination represents a hidden, non-therapeutic use of antimicrobial substances. Artificial insemination for livestock breeding is a huge global enterprise with hundreds of million sperm doses prepared annually. However, reporting of antimicrobial resistance in semen is increasing. This review discusses the consequences of bacteria in semen samples, as well as the effect of antimicrobial substances in semen extenders on bacteria in the environment and even on personnel. Alternatives to antibiotics have been reported in the scientific literature and are reviewed here. The most promising of these, removal of the majority of bacteria by colloid centrifugation, is considered in detail, especially results from an artificial insemination study in pigs. In conclusion, colloid centrifugation is a practical method of physically removing bacteria from semen, which does not induce antibiotic resistance. Sperm quality in stored semen samples may be improved at the same time.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Jane M. Morrell, Pongpreecha Malaluang, Aleksandar Cojkic and Ingrid Hansson"},{id:"81699",title:"Efflux Pumps among Urinary E. coli and K. pneumoniae Local Isolates in Hilla City, Iraq",slug:"efflux-pumps-among-urinary-e-coli-and-k-pneumoniae-local-isolates-in-hilla-city-iraq",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.104408",abstract:"Urinary tract infections (UTI) are the most common bacterial infections affecting humans. Escherichia coli and Klebsiella pneumoniae were common enterobacteria engaged with community-acquired UTIs. Efflux pumps were vital resistance mechanisms for antibiotics, especially among enterobacteria. Overexpression of an efflux system, which results in a decrease in antibiotic accumulation, is an effective mechanism for drug resistance. The ATP-binding cassette (ABC) transporters, small multidrug resistance (SMR), and multidrug and toxic compound extrusion (MATE) families, the major facilitator superfamily (MFS), and the resistance-nodulation- cell division (RND) family are the five superfamilies of efflux systems linked to drug resistance. This chapter highlights the results of studying the prevalence of efflux pump genes among local isolates of E. coli and K. pneumoniae in Hilla City, Iraq. class RND AcrAB-TolC, AcrAD-TolC, and AcrFE-TolC genes detected by conventional PCR of E. coli and K. pneumoniae respectively. The result revealed approximately all studied efflux transporter were found in both E. coli and K. pneumoniae in different percentages. Biofilm formation were observed in 50(100%) of K. pneumoniae and 49(98%) of E. coli isolates were biofilm former and follow: 30(60%), 20(40%) were weak, 12(24%), 22(44%) were moderate and 7(14%) and 8(16%) were Strong biofilm former for E. coli and K. pneumoniae, respectively.",book:{id:"11373",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg"},signatures:"Hussein Al-Dahmoshi, Sahar A. Ali and Noor Al-Khafaji"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:124,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:7,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). 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. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"13633",title:"Prof.",name:"Abdelhamid",middleName:null,surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk",profilePictureURL:"https://mts.intechopen.com/storage/users/13633/images/1567_n.jpg",institutionString:null,institution:{name:"Paris 12 Val de Marne University",institutionURL:null,country:{name:"France"}}},{id:"109268",title:"Dr.",name:"Ali",middleName:null,surname:"Al-Ataby",slug:"ali-al-ataby",fullName:"Ali Al-Ataby",profilePictureURL:"https://mts.intechopen.com/storage/users/109268/images/7410_n.jpg",institutionString:null,institution:{name:"University of Liverpool",institutionURL:null,country:{name:"United Kingdom"}}},{id:"3807",title:"Dr.",name:"Carmelo",middleName:"Jose Albanez",surname:"Bastos-Filho",slug:"carmelo-bastos-filho",fullName:"Carmelo Bastos-Filho",profilePictureURL:"https://mts.intechopen.com/storage/users/3807/images/624_n.jpg",institutionString:null,institution:{name:"Universidade de Pernambuco",institutionURL:null,country:{name:"Brazil"}}},{id:"38850",title:"Dr.",name:"Efren",middleName:null,surname:"Gorrostieta Hurtado",slug:"efren-gorrostieta-hurtado",fullName:"Efren Gorrostieta 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