Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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1. Introduction
The construction of industrial modernization cannot be separated from the electromechanical integration [1]. Electromechanical products play an extremely important role in the current society. The technical level of electromechanical products determines the development level of the national economy and also directly reflects the country’s manufacturing capacity, scientific and technological strength, economic strength, international competitiveness, and other comprehensive national strengths [2]. Poor quality of electromechanical products will not only bring economic losses to enterprises but also seriously affect the international market competitiveness of products and, more seriously, the overall image and economic strength of the country. Therefore, accelerating the development of electromechanical products and improving the quality of products are of vital significance for the national economic construction, national defense security, and social stability.
There are many quality indexes of electromechanical products. Among them, the four indexes (precision, precision life, performance stability, and reliability) are the most concerned by users, which together constitute the key quality characteristics of electromechanical products and decisively affect the market competitiveness of electromechanical products. Therefore, in order to occupy a place in the competitive high-end market, we must first improve the level of these key quality characteristics [3]. Therefore, carrying out a research into the electromechanical product quality control technology, especially, the quality of its key features, is extremely important. However, it is very difficult to control the quality of complex electromechanical products, not only for its complex structure but also for its working process, a dynamic process composed of many motions. The commonly used structural decomposition methods are mostly the static research based on the quality characteristics of parts in the design process. At the same time, there is a lot of uncertainty and coupling in the process of motion, which makes the dynamic quality characteristics greatly different from the static ones. In view of these problems, this paper puts forward the research idea of quality characteristic control technology of electromechanical product based on MU [4].
2. Literature review
There are many quality characteristics of electromechanical products, but its key characteristics mainly include precision, precision life, performance stability, and reliability, which comprehensively reflect the availability of the product. At present, the analysis of precision is quite common for the stages of design, manufacture, and operation of electromechanical products, but the research of precision life, performance stability, and reliability is few.
The precision of electromechanical products includes geometric accuracy, motion accuracy, transmission accuracy, position accuracy, etc., among which geometric accuracy is the basis and guarantee of other precisions. There are many studies on the precision of electromechanical products. Enterprises pay more attention to the precision of electromechanical products, especially for machine tools. Sata et al. put forward a method to improve machining accuracy of machining center through computer control compensation earlier [5]. Ceglarek et al. used the state space model to model the stream of variation (SOV) of the multi-stage manufacturing system, described the errors of process parameters and product quality characteristics with the vector tolerance, and applied it in automobile body-in-white assembly and automobile engine cylinder head machining [6]. Zeyuan et al. carried out simulation and experimental research on geometric error detection, identification, and compensation of translational axis of NC machine tools by two geometric error-modeling methods [7]. Li et al. summarized the characteristics of the existing error compensation methods and discussed the future development direction of improving the spatial positioning accuracy of five-axis CNC machine tools [8]. Yongwei et al. proposed a method for predicting the motion accuracy of CNC machine tools based on the time-series deep learning network [9].
The research on precision life mainly focuses on the working stage of electromechanical products, and the research object mainly focuses on the precision life of servo feed system (such as screw pair, etc.). Min et al. carried out a simulation test of the precision retaining ability degradation of the ball screw pair to achieve a fast prediction of the life of the ball screw pair [10]. Liping et al. conducted a gray prediction of the rotary precision life of the spindle of CNC lathe [11]. Linlin studied the precision retaining ability of servo feed system of CNC machine tool and its test method [12]. Hu et al. proposed a vibration aging process parameter selection method based on modal analysis and harmonious response analysis to improve the precision life of large basic parts of CNC machine tools in view of the nonstandard phenomenon of vibration aging process in domestic manufacturing enterprises [13]. Shijun et al. analyzed the influence of axial load on friction torque of ball screw pair through experiments and pointed out that the pre-tightening force plays a leading role in the fluctuation of friction torque of ball screw pair [14].
The research on performance stability mainly focuses on the research of performance index and parameter design. Saitou et al. summarized the development history of structural design and optimization of mechanical products and optimized the parameters to achieve the purpose of improving the stability of its performance [15]. Ta et al. studied the influence of friction on sliding stability of multibody systems [16]. Caro et al. improved the robust design method based on the sensitive area and proposed the comprehensive robust design method of mechanism deviation [17]. The size of the mechanism was calculated according to the performance robustness index, and the optimal deviation of the mechanism was calculated using the comprehensive deviation method. Gao et al. analyzed the robustness and robustness estimation of the product quality features [18]. They established a mathematical model based on minimum sensitivity region estimation (MSRE) for the robust optimization of the product quality features. Hui et al. constructed a dual-inertia feed system model based on the sensitivity of the closed-loop transfer function and analyzed the influence of the change of feed system stiffness on the stability of motion accuracy [19].
The research on reliability mainly focuses on reliability design, assembly reliability control, and fault diagnosis technology. Based on the shortcomings of traditional stress-strength interference model, Zhang et al. modeled the reliability from a dynamic perspective [20]. Pinghua et al. studied the assembly reliability control technology of electromechanical products based on fault modeling of motion unit to improve the reliability of assembly process [21]. Genbao et al. proposed the assembly process and technology driven by reliability and used the dynamic Bayesian method to model and control the assembly process [22]. Assaf and Dugan proposed a diagnostic method for large systems using monitors and sensors for reliability analysis by optimizing the diagnostic decision tree (DDTs) [23]. Yu et al. proposed a fault maintenance strategy for field equipment of assembly system [24]. And according to the differences in reliability design of different types of equipment, a comprehensive maintenance mode was introduced to ensure the reliability of automatic docking assembly of large aircraft parts. Qinghu et al. carried out a systematic analysis and prediction modeling research on the main failure mechanism and failure evolution rule of key components of the power transmission system [25].
Quality characteristic control is a comprehensive technical control activity based on decoupling or prediction of quality characteristic, including analysis of quality characteristic at each granularity and adjustment of influencing factors. Moreover, according to quality fluctuation, quality characteristic is under control. Haifeng studied the coupling mapping relation of quality characteristics of complex electromechanical products [26]. Then, combined with axiomatic design principle, they proposed the idea of decoupling design of quality characteristics of complex electromechanical products and presented the decoupling control model in the process of quality characteristic mapping of complex electromechanical products. Xianghua proposed a multiscale collaborative and intelligent quality control method for key parts of air separation equipment including the air compressor, the turbine expander, and the whole machine and conducted an in-depth research on coupling mapping and collaborative control of quality characteristics of large-scale air separation equipment from the whole machine to each part at all scales [27]. Nada studied the quality prediction in the design process of manufacturing system, constructed a basic framework on the basis of the manufacturing system configuration parameters to evaluate the quality of the products, and constructed a comprehensive evaluation model of system configuration based on quality by analytic hierarchy process (AHP), which can transform the structural parameters of manufacturing system into configuration capability indexes and then predict and control them [28]. Xianlin conducted an in-depth research on the quality characteristic prevention and control key technologies such as the quality characteristic evolution mechanism, coupling and decoupling control, timing data prediction, immune diagnosis, and control in the manufacturing process of electromechanical products [29]. In order to solve the coupling problem in quality characteristic predictive control, Zhentao established a quality characteristic predictive decoupling model based on predictive control theory [30]. Yang et al. established and solved the predictive control model based on particle swarm optimization algorithm and support vector machine on the basis of analyzing the characteristics of multiprocess and multistage product quality prediction control and realized the global optimization of multistage product quality prediction and related process parameters [31].
3. Findings
Through the analysis of the above research status, it can be seen that the following main problems exist in the quality characteristic control of electromechanical products at present.
Traditional quality control is mainly based on the quality control of parts, precisely, the precision, However, the combination with the function of parts is far from the expectance. Particularly, general electromechanical products exist for the realization of a certain motion function, and the overall functional failure of the product is caused by the inability to realize the basic motion function. Therefore, quality control should be placed in the basic level of motion, as long as the basic motion function is normal and the overall function of the product is guaranteed. Starting from the quality control of MUs, the quality control of the whole machine is realized through the quality control of the unit level. This control mode is more simple and effective, which does not need to simplify the model and is conducive to the accomplishment of refined quality control.
In terms of the key quality characteristic control technology, there are many researches on the pure precision but few achievements on the precision life, performance stability, reliability, and their coupling control. In fact, the quality gap of electromechanical products at present mainly lies in the aspects of precision life, performance stability, and reliability, among which reliability is the biggest bottleneck of electromechanical products. How to improve the precision life, performance stability, and reliability of electromechanical products as soon as possible is an urgent problem to be solved in the manufacturing industry.
4. Formation mechanisms of MU’s four key quality characteristics
Meta-action unit (MU) is a kind of basic action unit of electromechanical products decomposed in accordance with failure model analysis (FMA) structural decomposition method. It has the specific function of independently completing the specified movement or operation. Compared with other decomposition units, MU is more suitable for quality and reliability analysis. However, the complex quality characteristics are reflected not only in the MU’s motion accuracy but also in its precision life, performance stability, reliability, and other aspects. So it is necessary to conduct an in-depth study on the formation mechanism of each quality characteristic.
The quality characteristics of electromechanical products are very numerous, generally including 12 aspects, i.e., functional compliance, performance stability, application reliability, precision retaining ability, technology primacy, cost economy, esthetic appearance, maintenance convenience, and service timeliness, character uniqueness, security assurance, perception satisfaction, etc., which lead to the resultant diversity of MU’s quality characteristics. During the research process, the key quality characteristics need to be extracted from the perspective of the user’s requirements and the fault distribution. At the same time, as the smallest motion unit decomposed from electromechanical products, most of MUs’ quality characteristics are dynamic; therefore, it is necessary to conduct an in-depth study on their formation mechanism and extract the characteristic indicators of each key quality characteristic from the perspective of motion. Moreover, to expand the amount of the sample data, the corresponding models are established and applied to the similarity determination of MUs.
4.1 Formation mechanism of the MU motion accuracy
Accuracy is the degree to which actual geometric parameters conform to ideal ones, and their deviation values are often defined as errors. The accuracy of electromechanical products generally includes geometric accuracy, motion accuracy, transmission accuracy, and positioning accuracy [32]. For MUs, the accuracy refers to the motion accuracy of the output part that implements the meta-action. Moreover, the movement accuracy of the unit actuator is also affected by the manufacture and assembly errors of other parts in the unit (support, transmission, and fasteners). For example, as the actuator of the rack moves the MU, the translation accuracy of the rack is the only measure indicator. In the actual situation, there are many kinds of error sources that affect the motion accuracy of the MU actuators. Actually, the formation process of the accuracy is also the transmission process of the errors.
4.1.1 The MU motion accuracy
For translation and rotation MU, the motion accuracy of the actuator also includes translation and rotation accuracy. The corresponding translation accuracy indicators of the actuator mainly include positioning accuracy, repeat positioning accuracy, reverse error, moving straightness (trajectory), moving parallelism (trajectory), and resolution (minimum translation increment). The rotation accuracy indicators include positioning accuracy, repeat positioning accuracy, indexing accuracy, reverse error, rotation face fluctuation (radial and end faces), and rotation fluctuation (axial).
The motion accuracy of the unit actuator is also affected by the geometric and assembly accuracy of the various parts in the unit. The geometric accuracy of the part includes the geometric position and shape accuracy, expressed as static errors such as size, position, and shape. According to ISO230-1:1996 and GB/T1800.1-2009 “Limit and fit Part 1: The basis of tolerance, deviation and fit,” the size and position error of the MU actuator refers to the defects in the design, manufacture, and assembly process, which cause the MU actuator (output member) deviating from the ideal size and position. It mainly includes distance dimension error, diameter dimension error, angular dimension error, parallelism, coaxiality, verticality, position, symmetry, and round and full runout. The geometric shape error of the MU actuator refers to the deviation between the actual and ideal shape after machining, which is set as a machining range of the normal distribution and mainly includes straightness, flatness, roundness, cylindricity, line profile, and surface profile.
4.1.2 Error model of the MU motion accuracy
4.1.2.1 Topology structure description of electromechanical product
The electromechanical product is a kind of multibody system, a complete abstract and effective description of general complicated mechanical system, which is the optimal mode of analyzing and researching complicated mechanical system [33, 34, 35]. The multibody kinematic theory is used to establish the synthetic space motion error model of the MU actuator in this chapter.
An MU is regarded as a body, and there is only a single degree-of-freedom relative motion with the constraint type of translation or rotation. The motion of electromechanical products, open or closed loop, aims to accomplish a corresponding function. Because closed-loop motion can be converted as open with specific constraint, the topological structure for general electromechanical products is expressed as follows (Figure 1). The inertial reference system and MU are regarded as different bodies. Then the bodies are numbered in turn along the direction away, according to the natural growth sequence, from one branch to another (each branch accomplishing its corresponding function).
Figure 1.
The topological structure diagram of electromechanical products based on MUs.
The low-order body array is used to describe the topological structure of the multibody system [36], which can be obtained by the following calculation formula (1). The optional body Bk is any typical body in the system, and the sequence number of its n-th lower-order body is defined as
Lnk=jE1
where L is the low-order body operator and body Bk is the n-th high-order body of body Bj (Table 1). Lnk=LLn−1k, L0k=k, and Lnk=0. Moreover, when body Bj is the adjacent lower-order body of body Bk, Lk=j.
k
1
2
3
4
5
6
7
⋯
m
m+1
⋯
n
L0k
1
2
3
4
5
6
7
⋯
m
m+1
⋯
n
L1k
0
1
1
3
1
5
6
⋯
m−1
6
⋯
n−1
L2k
0
0
0
1
0
1
5
⋯
m−2
5
⋯
n−2
L3k
0
0
0
0
0
0
1
⋯
m−3
1
⋯
n−3
Lmk
0
0
0
0
0
0
0
0
0
0
0
0
Lnk
0
0
0
0
0
0
0
0
0
0
0
0
Table 1.
The low-order bodies of electromechanical products based on MU.
4.1.2.2 Motion error characteristic matrix of adjacent MUs
According to the MU motion of the electromechanical product in the actual working process, the description method of multibody system error is used to study the motion error of the MU actuator (Figure 2). Pk, PkI, and PkE are the actual position vector, the ideal position vector, and the position error vector of the origin Qk of the body Bk motion reference coordinate system in its low-order body Bj, respectively; and Dk, DkI, and DkE are the actual displacement vector, the ideal displacement vector, and the displacement error vector of the origin Ok of body Bk reference coordinate system.
Figure 2.
The relative motion sketch of adjacent MUs.
From the kinematic point of view, the errors of MU actuators are divided into two categories. One is the errors that happened in the motion reference coordinate of MU (body Bk), described with the position (static) error vector PkE and the position error feature (transformation) matrix TjkPE. Another is the motion process error (translation or rotation) of the MU (body Bk) relative to the actual motion reference coordinate, described with the translation (motion) error vector DkE (linear or angular displacement) and the motion error feature (transformation) matrix TjkDE.
4.1.2.2.1 Motion error characteristic matrix from translation MU to translation MU
When the MU (body Bk) translates xjkD, yjkD, and zjkD along the X-axis, Y-axis, and Z-axis relative to its adjacent MU (lower-order body Bj), its actuator’s translation matrices and translation error feature matrices can be expressed, respectively, as follows:
4.1.2.2.2 Motion error characteristic matrix from rotation MU to rotation MU
When the MU (body Bk) rotates αjkD, βjkD, and γjkD (the Euler angle or the Karl single angle of the coordinate system k relative to the coordinate system j) along the X-axis, Y-axis, and Z-axis relative to its adjacent MU (lower-order body Bj), its actuator’s rotation matrices and rotation error feature matrices can be expressed, respectively, as follows:
Motion error characteristic matrix of synthetic motion at the MU (body Bk) translates first and then rotates relative to its adjacent MU (low-order body Bj). The specific motion order is translating xjkD, yjkD, and zjkD along the X-axis, Y-axis, and Z-axis and rotating αjkD along the X-axis, βjkD along the Y-axis, and γjkD along the Z-axis, and the ideal motion feature matrix of the unit actuator can be expressed as
TjkD=TjkDxTjkDyTjkDzTjkDαTjkDβTjkDγE2
The motion error feature matrix of the unit actuator is expressed as
According to the geometric description method above, the actual feature (transformation) matrix of the MU actuator in the multibody system is obtained as
Tjk=TjkPTjkPETjkDTjkDEE6
where Tjk, TjkP, TjkPE, TjkD, and TjkDE are the actual position, the ideal position, the static error, the ideal motion, and the motion error feature matrix of the MU actuator, respectively.
4.1.2.4 Integrated space motion errors of MUs
Set the homogeneous coordinates of the MU (body Bj) on any point W in sub-coordinate system Oj−XjYjZj as
Wj=wxjwyjwzj1TE7
Thus, the ideal homogeneous coordinates of the point W in subcoordinate system Oj−XjYjZj is obtained as
Equation (10) is the comprehensive spatial motion error model of MU actuators for general electromechanical products.
4.2 Formation mechanism of the MU precision life
In the process of electromechanical product working, MUs are influenced by various changing indicators, e.g., increase in ambient temperature, wear on part surface, and change of load or force. These indicators cause the variation of the MU accuracy [37, 38].
The general formation process of MU accuracy faults is shown in Figure 3. After MU moving for a period of time, its accuracy variation reaches the designed accuracy variation limit (Ymax or Ymin). Its specific variation law f(t) is as follows.
Figure 3.
The general forming process of the MU accuracy failure.
4.2.1 Initial stage
MU is working at an expectation accuracy a0 (producing accuracy). For a specific MU, because of the influence of its initial state and working condition, a0 has a certain dispersion, following the distribution law fa.
4.2.2 Stable stage
After MU working stably for a period of time Ta, the influence reaches enough degree to cause the variation of MU accuracy. Because of the considerable degree of randomness of the uncertainty influencing factors, the stable operating time Ta obeys a random distribution fTa.
4.2.3 Degradation stage
MU accuracy reaches Ylim at a speed v, and faults happen, where v is a random factor obeying distribution law F(v), so operating time without faults T is also a random factor obeying f(t).
During the moving process, MU is affected by various factors, e.g., friction, environment, and changing working conditions. Its accuracy variation rate v can be regarded as a normal distribution:
fv=1σv2πe−v−v¯22σv2E11
where f(v) is the probability density function of accuracy variation, v¯ represents the average rate of accuracy variation process, and σv is the mean square error of rate accuracy variation.
For a specific MU, if its initial accuracy is a known value a0, obeying a random distribution and its stable accuracy stage is finished, that is, Ta = 0, and then its accuracy variation is obtained as (e.g., linear correlation)
Y=a0+vtE12
As is shown in Figure 3, before accuracy reaching the limitation, Y=Ylim, its operating time without failure is the precision life (accuracy retention time), which is the function of speed v:
T=Y0limvE13
Finally, its accuracy reliability is expressed as
R=Pt≤T=P(v≤Y0limt‖Y0limt‖=Φ(Y0limt·σv‖v¯σvv¯σvE14
4.3 Formation mechanism of the MU performance stability
The performance stability of MUs refers to the ability of maintaining the performance of the MU or the variation range of output quality characteristic (dynamic characteristic) minimal with the uncertainty factor while satisfying various constraints. It means that the output value Y fluctuates around the input-output linear relationship and the smaller the fluctuation, the better the stability.
4.3.1 Performance stability evaluation of MUs
4.3.1.1 The dynamic tracking errors and steady errors
The performances of MUs are real-time and dynamic, and its output quality characteristic fluctuates with input parameters. That is, there is a corresponding objective value for each input signal. At the same time, the fluctuation needs to be as small as possible. This characteristic is defined as a dynamic characteristic.
The dynamic state in the process of MU motion is divided into transient and stead. Dynamic tracking errors mean the deviation between the actual value y(t) and expected value yIdeal of the MU quality characteristic indicator of each transient state. Steady errors refer to the fluctuation amplitude Δ of the deviation between the actual value y(t > T) and expected value yIdeal of the MU quality characteristic under steady state after moving for a time T. To guarantee enough high-performance stability, there are two premises for them:
The indicator of signal/noise ratio (SNR) [39] (Eq. 16) proposed by Prof. Taguchi is applied to evaluate the MU dynamic performance stability in this section. Generally, the greater the SNR value, the better the stability, which indicates that the noise is low even if the signal is strong:
η=PSPNE16
where PS and PN represent the power of signal and noise, respectively.
For the dynamic characteristic, the objective value m is not a constant but a variable determined according to the variation of signal M output characteristic y = α + Mβ + ε (Figure 4), where ε reflects the degree of interference. Moreover, the variation of output characteristic is set as a normal distribution N(0,δ2).
Figure 4.
The sketch of dynamic characteristic.
As is shown in Figure 4, the SNR of dynamic characteristic y is defined as
η=β2σ2E17
where β2 is the measure of signal sensitivity, and δ2 is the fluctuation rate (deviation) of amplitude, which is used to evaluate the stability of MU.
For a specific MU quality indicator q1, the greater the signal/noise ratio η1, the more stable the quality indicator. Its weight (the contribution rate of this quality indicator to quality instability) is expressed as
λl=1ηl∗∑j=1m1ηjE18
4.3.2 Clearance formation mechanism of MUs
The clearance is one of the main factors causing noise of MU and influencing the performance stability of complete machine [40]. However, in real engineering application, the MU clearance is inevitable. It happens in the inner composition parts of MUs, e.g., the clearance between the large piston and the inner wall of the mandrel in the piston moving unit and the meshing clearance between the rack translating unit and the gear rotating unit (Figure 5).
Figure 5.
Clearances between MUs resulting from (a) assembly, (b) design, and (c) wear.
The clearance between MUs includes the static clearance of geometric size and the dynamic clearance under a moving state. As is shown in Figure 5, they are mainly caused by three aspects:
A regular clearance reserved due to the motion fit design of the MU.
The uncertainty factors in the MU design and manufacturing process. The error will be caused to form clearance.
The uncertainty factors in the process of operating, e.g., vibration, friction, wear, etc., cause the irregular clearance.
4.4 Formation mechanism of the MU reliability
The MU reliability refers to the ability to accomplish specific machine motion precisely, timely, and coordinately under a specified time and condition and maintain all quality characteristics within the allowable range. Its quantification indicator, the probability, is known as reliability.
4.4.1 Reliability indicators of MUs
4.4.1.1 The MU reliability
The MU reliability refers to the probability of output quality characteristic parameters being in the allowable range at a specified time (period). Suppose that output characteristic parameter, e.g., accuracy, precision life, performance stability, etc., is random variable Y(t). According to the design requirement, output quality characteristic parameter is controlled within the range of [Ymin, Ymax]. The MU reliability is defined as the probability P that happens:
R=PYmaxminE19
Its corresponding failure probability F is expressed as
F=1−R=1−PYmaxminE20
Taking the motion accuracy (motion error) of the MU as an example, its motion accuracy reliability is the probability of its motion output error within the maximum allowable error range:
R=PemaxminE21
For a specific MU, its error obeys a normal distribution, and its reliability is derived as
4.4.1.2 The mission reliability of complete machine system
Because multiple different missions of the upper motion units are accomplished by the input and output of multiple MUs, from the perspective of the mission reliability, the mission reliability of the complete machine system of electromechanical products is an organic combination of the MU motion reliability (Figure 6), which is expressed as follows:
Figure 6.
The mission reliability model of (a) MU and (b) complete machine system.
RW=∑i=1nαiRAiE23
4.4.2 Failure mechanism of MUs
The MU failure analysis mainly includes three aspects: the failure stress, the failure mechanism, and the failure mode. To highlight the key points, we mainly analyze them from the perspective of the motion function.
4.4.2.1 The failure stress
The failure stress is the physical condition causing the MU failure, mainly including the working and environment stress. The working stress refers to the necessary stress to motivate MU to achieve a basic motion function, e.g., pressure, thrust, bending torque, etc.; the environment stress refers to MU’s external working environment conditions and its interaction between the internal parts, e.g., temperature, humidity, dust, noise, vibration, friction, impact force, etc.
Taking the end-toothed disk indexing turntable function unit as an example, we analyzed the failure stress of each decomposition MU (Table 2).
The failure stress analysis of end-toothed disk indexing turntable function unit.
4.4.2.2 The failure mechanism
The MU quality uncertainty includes gradual and emergent uncertainty; furthermore, corresponding failures are gradual and emergent. When they happen, their certain performance parameter reaches or exceeds the threshold, caused by the MU uncertainty. If an MU performance parameter changes at the beginning of working process without a stable stage, the failure that happened is defined as a gradual failure. If the MU performance parameter suddenly reaches or exceeds the threshold in an instant with an infinite speed, the failure that happened is defined as an emergent failure. The gradual failure is also called as wear failure, mainly including:
A. Functional abnormality due to the MU wear deformation, e.g., worm gear MU rotation difficulty, loose, vibration, large clearance, etc.; B. function abnormality due to the deformation or fracture caused by fatigue, e.g., gear tooth fracture of gear rotation MU; C. deformation and fracture due to corrosion; D. deformation due to yielding; E. deformation, cracking, and fracture due to deterioration of the polymer; F. obstacles caused by wear-induced debris; G. abnormal rotation and heat caused by deterioration of oil; H. abnormal movement due to the incorporation of rust, peeling paint, and peeling plating.
The formation mechanisms of the typical MU failures are shown in Table 3.
Failure reason
Failure mechanism
Cause analysis
Wear
Wear generally happens in the relative motion part of MUs. The friction factor increases gradually in the initial wear stage, accompanied by heat, which eventually causes melting
The influence factors include mainly the materials in contact with each other, the surface contact pressure, the wear rate, and the lubricating oil
Fatigue
The MU undergoes a wavelike stress during the movement, and its effect accumulates continuously and develops into a damage after a certain number of iterations
Internal force caused by vibration, rotation, intermittent motion, etc. External force due to roughness of the external contact surface
Vibration
Different degree vibrations happen during the MU motion. Some of them cause failure of the MU and are defined as “failure” vibration
Vibrations determined by the MU structural characteristics, such as meshing vibration of gears, etc.
Yielding
Deformation exceeds the elasticity limit of the material to produce unrecoverable plastic deformation
Under long time stress or stress much higher than the elasticity limit, plastic deformation is produced
Impact
When the MU structure material property is brittle, with an excessive impact load, the visible deformation, e.g., extension or bending, is caused under one shot, especially at the stress concentration
This kind of damage suddenly is caused with a large loss, which is mainly determined by the material basic properties. The reasons include the choice of materials, the stress concentration structure, the imperfect function of the buffer mechanism, etc.
Table 3.
Formation mechanisms of typical MU failures.
5. Key quality characteristic control methods based on the MU
On the basis of studying the key quality characteristics of MUs and the coupling relationship of the complete machine quality characteristics, it is necessary to unitize and aggregate the MUs’ quality characteristic according to the “decomposition-analysis-synthesis” analysis method of the large system control theory [41]. The whole machine quality prediction method is studied from bottom to top, the quality characteristic state space model is constructed, and the predictive control algorithm is derived to achieve the comprehensive prediction and control of the complete machine quality characteristics.
5.1 Multiple generalized operator model of complex large systems
The whole working process of electromechanical products is achieved and accomplished via various orderly units. Therefore, the whole control process of the complete machine quality characteristic system can be decomposed into several quality control sub-processes of units and regarded as a linear system. To better understand the decomposition and aggregation relationship between the quality characteristics of the upper and lower units, corresponding to the quality characteristic coupling relationship model based on the FMA tree, the multiple generalized operator model of the complete machine large system [42] is established combining generalized operator model of complete machine system and the generalized operator relationship model of units (Figure 7).
Figure 7.
The multilayer generalized operator model of complete machine.
The power input of the complete machine system is decomposed into the units’ output, which is transmitted through the same layer and then aggregated to the complete system.
5.1.1 Generalized operator model of units
The first-layer generalized operator model of the complete machine system can be expressed as
YW≐KW·UWE24
where UW and YW are the total input and output of the complete machine system, respectively, and KW· is the macro generalized operator of coarse granularity.
The second-layer generalized operator model of the function units can be expressed as
YFi≐KFi·UFi,YFj≐KFj·UFj,i,j=1,2,⋯,nE25
where UFi and YFi are the input and output of function unit Fi, respectively, and KFi· is its generalized operator.
The third-layer generalized operator model of the motion units can be expressed as
YMi≐KMi·UMi,YMj≐KMj·UMj,i,j=1,2,⋯,nE26
where UMi and YMi are the input and output of motion unit Mi respectively, and KMi· is its generalized operator.
The fourth generalized operator model of the MUs can be expressed as
YAi≐KAi·UAi,YAj≐KAj·UAj,i,j=1,2,⋯,nE27
where UAi and YAi are the input and output of MU Ai and KAi· is its generalized operator.
5.1.2 Relationship model of generalized operators
5.1.2.1 Relationship model of vertical generalized operators
According to the function and mission of the unit, the total input of the complete machine system is distributed down to the corresponding unit of the next layer to form its input. Therefore, the input between units from different layers is the effective allocation of the complete machine system input, and the downward vertical relationship model can be expressed as
where R↓· is the downward vertical relationship operator.
The output (quality characteristics) of the unit is aggregated to the complete machine system to form its total output (quality characteristics). Therefore, YW is the order integration of the unit output, and the upward vertical relationship model can be expressed as
where rAi,Aj· is the horizontal relationship operator of MU Ai and Aj.
5.2 Multilayer state space model of quality characteristics
According to the quality characteristic mapping model based on the FMA tree, the large system of the complete electromechanical product is decomposed into several units. To predict and control the quality characteristics of different hierarchical units, the corresponding granularity required is different. The structural features of the multilayer state space model system, decomposition-analysis-synthesis, can combine the microscopic quality characteristics of each unit and the macroscopic large system to control the quality characteristics more accurately. Therefore, referring to the multigeneralized operator model of the large-scale system, the multilayer state space model of the quality characteristic of the complete machine is established step by step as follows [43, 44].
5.2.1 Decomposition
According to the qualitative model, the quantitative equations of multilayer state space model of the complete machine quality characteristics are set as follows:
where yt is the output variable, y=y1y2⋯ymT; xt is the state variable, x=x1x2⋯xnT; and ut is the input variable, u=u1u2⋯urT. A is the object (system) matrix describing the characteristics of the controlled object:
A=aijn×n=a11a12⋯a1na21a22⋯a2n⋮⋮⋱⋮an1an2⋯annE31
B is the control matrix describing the characteristics of the control mechanism:
B=bijn×r=b11b12⋯b1rb21b22⋯b2r⋮⋮⋱⋮bn1bn2⋯bnrE32
C is the observing matrix describing the characteristics of the observing device:
C=cijm×n=c11c12⋯c1nc21c22⋯c2n⋮⋮⋱⋮cm1cm2⋯cmnE33
D is the observing matrix describing the characteristics of the observing device:
D=dijm×r=d11d12⋯d1rd21d22⋯d2r⋮⋮⋱⋮dm1dm2⋯dmrE34
5.2.2 Synthesis
Because the complete electromechanical products are composed of a series of units, according to the research on coupling and decoupling technology based on MUs, the spatial state variables of the complete product can be expressed as the weighted sum of that of some function units. Furthermore, the spatial state variables of function units can be expressed as the weighted sum of that of some motion units. Finally, the spatial state variables of motion units can be expressed as the weighted sum of that of some MUs. The specific model can be expressed as
With the increase of structure complexity of electromechanical products, the quality control of electromechanical products by parts is too complicated, and its quality control becomes significantly difficult. The control method based on MUs provides a good idea properly analyzing the formation mechanism of the key quality characteristics (precision, precision life, performance stability, and reliability) of electromechanical products is studied based on MUs. On the one hand, due to the appropriate granularity, it is more convenient for quality control; on the other hand, because the meta-action decomposition method is a dynamic decomposition process of electromechanical products, the quality characteristics of electromechanical products can be controlled by the four key quality characteristics (the precision, the precision life, the stability, the reliability) of the quality of the control MUs. It makes up for the deficiency of traditional control methods.
7. Conclusions and prospects
The research shows that it is adaptive and feasible to study the formation mechanism of key quality characteristics and quality control through the proposed analysis method based on the meta-action theory. MU has specific functions and can independently complete the specified motion or operation, which is more suitable for quality and reliability analysis. In this paper, the forming mechanism of MU’s four key quality characteristics (the accuracy, the precision life, the performance stability, the reliability) is deeply studied, and its parameter expression model is obtained. The model of quality characteristic state space of unit is established; based on that, the basic principle of predictive control and the forming mechanism of the quality characteristics of MUs are analyzed and clarified. Starting from the quality control of MUs, the quality control of the whole machine is accomplished through the quality control of the unit level. This control mode is more simple, effective, and conducive to the accomplishment of refined quality control.
Due to a variety of electromechanical products, different functions and their implementation processes are complex. On the one hand, the data acquisition of MU quality index is very difficult. On the other hand, the factors influencing the quality uncertainty are various. Moreover, the meta-action theory also has certain limitations up to now, and some deficiencies exist here. Thus, we plan to further improve the following research work in the future:
In the follow-up study, the quality characteristic database of meta-action units should be continuously increased through various channels such as collecting process data or conducting meta-action unit tests so as to better verify the theoretical methods.
This paper only studies the four key quality characteristics (the reliability, the precision, the precision life, the performance stability). In order to make the later quality prediction and control more accurate, it can be considered to increase the research of quality characteristics such as usability.
Acknowledgments
This work is financially supported by the National Natural Science Foundation of China (No. 51705048; 51835001) and the National Major Scientific and Technological Special Project for “High-grade CNC and Basic Manufacturing Equipment” of China (2018ZX04032-001; 2016ZX04004-005).
\n',keywords:"key quality characteristics, electromechanical product, meta-action unit",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69400.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69400.xml",downloadPdfUrl:"/chapter/pdf-download/69400",previewPdfUrl:"/chapter/pdf-preview/69400",totalDownloads:631,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:42,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"January 31st 2019",dateReviewed:"September 2nd 2019",datePrePublished:null,datePublished:"January 15th 2020",dateFinished:"October 7th 2019",readingETA:"0",abstract:"Electromechanical products have many quality characteristics, representing their quality. In addition, there are long-existed quality problems of electromechanical products, such as poor accuracy, short precision life, large fluctuations in performance, frequently failing, and so on. Based on meta-action unit (MU) for electromechanical products, this book chapter proposes a key quality characteristic control method, which provides theoretical and technical support for essentially guaranteeing the complete machine’s quality. The formation mechanisms of MU’s four key quality characteristics (precision, precision life, performance stability, and reliability) are studied. Moreover, we introduce an overview of key quality characteristic control methods based on MU. The complex large system research method of “decomposition-analysis-synthesis” is adopted to study these key science problems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69400",risUrl:"/chapter/ris/69400",book:{id:"8879",slug:"emerging-trends-in-mechatronics"},signatures:"Yan Ran, Xinlong Li, Shengyong Zhang and Genbao Zhang",authors:[{id:"273420",title:"Dr.",name:"Yan",middleName:null,surname:"Ran",fullName:"Yan Ran",slug:"yan-ran",email:"ranyan@cqu.edu.cn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"286849",title:"Prof.",name:"Genbao",middleName:null,surname:"Zhang",fullName:"Genbao Zhang",slug:"genbao-zhang",email:"gen.bao.zhang@263.net",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Chongqing University",institutionURL:null,country:{name:"China"}}},{id:"307457",title:"Mr.",name:"Shengyong",middleName:null,surname:"Zhang",fullName:"Shengyong Zhang",slug:"shengyong-zhang",email:"rainbowzhang269@163.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"308760",title:"Mr.",name:"Xinlong",middleName:null,surname:"Li",fullName:"Xinlong Li",slug:"xinlong-li",email:"xinlongl@163.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Chongqing University",institutionURL:null,country:{name:"China"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Literature review",level:"1"},{id:"sec_3",title:"3. Findings",level:"1"},{id:"sec_4",title:"4. Formation mechanisms of MU’s four key quality characteristics",level:"1"},{id:"sec_4_2",title:"4.1 Formation mechanism of the MU motion accuracy",level:"2"},{id:"sec_4_3",title:"4.1.1 The MU motion accuracy",level:"3"},{id:"sec_5_3",title:"Table 1.",level:"3"},{id:"sec_5_4",title:"Table 1.",level:"4"},{id:"sec_6_4",title:"4.1.2.2 Motion error characteristic matrix of adjacent MUs",level:"4"},{id:"sec_6_5",title:"4.1.2.2.1 Motion error characteristic matrix from translation MU to translation MU",level:"5"},{id:"sec_7_5",title:"4.1.2.2.2 Motion error characteristic matrix from rotation MU to rotation MU",level:"5"},{id:"sec_9_4",title:"4.1.2.3 Actual characteristic matrix of MUs",level:"4"},{id:"sec_10_4",title:"4.1.2.4 Integrated space motion errors of MUs",level:"4"},{id:"sec_13_2",title:"4.2 Formation mechanism of the MU precision life",level:"2"},{id:"sec_13_3",title:"4.2.1 Initial stage",level:"3"},{id:"sec_14_3",title:"4.2.2 Stable stage",level:"3"},{id:"sec_15_3",title:"4.2.3 Degradation stage",level:"3"},{id:"sec_17_2",title:"4.3 Formation mechanism of the MU performance stability",level:"2"},{id:"sec_17_3",title:"4.3.1 Performance stability evaluation of MUs",level:"3"},{id:"sec_17_4",title:"4.3.1.1 The dynamic tracking errors and steady errors",level:"4"},{id:"sec_18_4",title:"4.3.1.2 The signal/noise ratio",level:"4"},{id:"sec_20_3",title:"4.3.2 Clearance formation mechanism of MUs",level:"3"},{id:"sec_22_2",title:"4.4 Formation mechanism of the MU reliability",level:"2"},{id:"sec_22_3",title:"4.4.1 Reliability indicators of MUs",level:"3"},{id:"sec_22_4",title:"4.4.1.1 The MU reliability",level:"4"},{id:"sec_23_4",title:"4.4.1.2 The mission reliability of complete machine system",level:"4"},{id:"sec_25_3",title:"Table 2.",level:"3"},{id:"sec_25_4",title:"Table 2.",level:"4"},{id:"sec_26_4",title:"Table 3.",level:"4"},{id:"sec_30",title:"5. Key quality characteristic control methods based on the MU",level:"1"},{id:"sec_30_2",title:"5.1 Multiple generalized operator model of complex large systems",level:"2"},{id:"sec_30_3",title:"5.1.1 Generalized operator model of units",level:"3"},{id:"sec_31_3",title:"5.1.2 Relationship model of generalized operators",level:"3"},{id:"sec_31_4",title:"5.1.2.1 Relationship model of vertical generalized operators",level:"4"},{id:"sec_32_4",title:"5.1.2.2 Relationship model of horizontal generalized operators",level:"4"},{id:"sec_35_2",title:"5.2 Multilayer state space model of quality characteristics",level:"2"},{id:"sec_35_3",title:"5.2.1 Decomposition",level:"3"},{id:"sec_36_3",title:"5.2.2 Synthesis",level:"3"},{id:"sec_39",title:"6. Discussion",level:"1"},{id:"sec_40",title:"7. Conclusions and prospects",level:"1"},{id:"sec_41",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Yan Z, Liuyong S, Liu Shihao HS. 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DOI: 10.3969/j.issn.1002-137X.2014.03.054'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Yan Ran",address:"ranyan@cqu.edu.cn",affiliation:'
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1. Introduction
In 2014 recreational, adult-use of cannabis (interchangeably referred to as marijuana) was established in the state of Colorado. At this time the Colorado Department of Public Health and Environment (CDPHE) was given statutory responsibility in Colorado Revised Statute (C.R.S.) 25-1.5-110, to; “monitor changes … in the emerging science and medical information relevant to the health effects associated with marijuana use.” and “appoint a panel of health care professionals with expertise in, but not limited to, neuroscience, epidemiology, toxicology, cannabis physiology, and cannabis quality control to further direct policy.” Based on this charge, CDPHE appointed a 14-member committee titled the Retail Marijuana Public Health Advisory Committee (RMPHAC) to review scientific literature on the health effects of marijuana.
Under the same statute mentioned previously, the RMPHAC is directed to “…establish criteria for studies to be reviewed, reviewing studies and other data, and making recommendations, as appropriate, for policies intended to protect consumers of marijuana or marijuana products and the general public.” To implement this charge, the RMPHAC meets four or five times a year to review the scientific literature currently available on health effects of marijuana use, evaluate findings without bias, openly discuss the science and apply expert opinion, come to consensus on the science, translate the science into public health messages, make policy-related recommendations, recommend surveillance activities, and identify and address gaps in the science important to public health. All this information is compiled and detailed in a report every two years for the Colorado State Board of Health, the Colorado Department of Revenue, and the Colorado General Assembly, titled “Monitoring Health Concerns Related to Marijuana in Colorado” [1].
Since 2014, and prior to this publication, the RMPHAC has come together on a quarterly basis, held discussions concerning hundreds of articles, and developed over one hundred evidence statements within eleven health topics. As more scientific evidence regarding cannabis health effects are published, this committee continues to build upon existing evidence statements or will construct new statements when appropriate. This chapter will detail the review methods used by the RMPHAC to develop evidence statements about the health effects associated with marijuana use, describe the findings from all eleven health topics, and report the public health statements, recommendations, and research gaps used to inform public health policy in the State of Colorado.
2. Systematic review development and process
The first step in the process of investigating the health effects from marijuana use was to develop and implement an unbiased, transparent, and complete process for evaluating scientific literature and data on marijuana use and health outcomes. To ensure this, the RMPHAC and CDPHE technical staff developed a twelve step review process guided by the established preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework [2]. These twelve steps are followed for each review and are as follows:
Conduct a broad search of current peer-reviewed publications quarterly. Relevant articles cited in reviews or other primary studies are also included.
Review relevant full-text articles identified in the search.
Rate the findings: each finding in the articles is rated as a high-, medium-, or low-quality finding based on strengths and limitations of the methods. Evaluation of the strengths and limitations was based on criteria in the grading of recommendations assessment, development and evaluation (GRADE) system, a well-accepted method for evaluating the quality of scientific evidence [3].
Group related findings: each finding is categorized based on population, exposure, and outcome (health effect), to answer specific questions.
Weigh the evidence: draft evidence statements that summarize the quantity and quality of evidence answering a specific question.
Translate the evidence: draft public health statement that translate the evidence statement into language at an 11th grade reading level.
Synthesize the evidence: draft public health recommendations (e.g., for education or monitoring) based on important information identified through the review process.
Identify research gaps: draft statements to articulate the research gaps identified during the review process.
Present to committee: findings, evidence statements, public health statements, public health recommendations, and research gaps are publicly presented to the RMPHAC for review and revision during open public meetings.
Public comment: during the open public meetings, interested stakeholders and members of the public are invited to provide comments relevant to the topics presented.
Reach consensus: committee members come to consensus on findings, evidence statement, public health statement, public health recommendations, and research gaps.
Adopt summary statements: committee votes to officially accept findings, evidence statements, public health statements, public health recommendations, and research gaps.
All review methods were approved by the RMPHAC, including the terms used to conduct the ongoing broad search of peer-reviewed publications for relevant literature. Medline is the priority research database used to obtain articles for review. Embase, the biomedical database, and gray literature were secondarily reviewed when references in included articles were not included in Medline searches. Studies of marijuana use in humans were the primary focus of the review, with animal studies included for only specific topics with limited human research. All identified peer-reviewed literature on a given topic was reviewed, regardless of positive or negative findings or quality of the methods utilized. For the ongoing broad Medline search, medical subject heading (MeSH) terms were used and is as follows; “Cannabis”[Mesh] OR Marijuana “Smoking”[Mesh] OR “Marijuana Abuse”[Mesh] OR cannabis OR marijuana OR marihuana OR hash oil OR hashish. In 2014, when this review was established, specific searches were conducted using the appropriate MeSH terms for each topic area.
Once relevant literature is obtained, each finding is rated high, medium, or low quality based on the strengths and limitations of the methods which is determined by criteria in the GRADE system. The GRADE system is a well-established method for systematic literature review and has been used by the Cochrane Collaboration, British Medical Journal, American College of Physicians, World Health Organization, and many others [3]. Findings rated high quality are defined as “We are very confident that the true effect lies close to that of the estimate of the effect outlined in the study.” These are well-designed and well-controlled studies with few limitations. Due to the fact that most studies included in our review are observational epidemiology studies, receiving a high quality rating does not necessarily imply causation. It simply implies that an observed association persists between an exposure and effect in an appropriately-sized study population after adjusting for appropriate confounders. Medium quality findings are defined as “we are moderately confident in the effect estimate outlined in the study. The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.” For observational epidemiology studies this implies the finding of an observed association may be limited by a small study population or insufficient adjustment for important confounders. Low quality findings are defined as “our confidence in the effect estimate outlined in the study is limited. The true effect may be substantially different from the estimate of the effect.” For observational epidemiology studies this implies the finding of an observed association with an interpretation that is significantly restricted by study limitations.
Findings from relevant literature are usually grouped based on outcome or the health effect in question. However, in some situations findings are further subdivided based on factors such as: age range of the exposed population, special subject circumstances such as pregnancy or breastfeeding, level or method of marijuana use, time period since last use of marijuana, amount of marijuana used, and THC concentration. Standardized definitions of level of marijuana use (daily, weekly, etc.) and age groups (child, adolescent, young adult, etc.) were established to help facilitate grouping of findings. Once findings are grouped appropriately, the evidence is drafted into evidence statements that summarize the quality and quantity of scientific evidence supporting an association between marijuana use and a health outcome.
3. Systematic review findings
In order to make our review findings easily interpretable we used a standardized rating system to classify evidence statements. These statements are also constructed to accurately portray the quality and quantity of all findings used to support the particular health outcome. Evidence statements all use standardized language from one of the following six classifications:
Substantial evidence—indicates robust scientific findings that support an association between marijuana use and the outcome.
Moderate evidence—indicates scientific findings that support an association, but these findings have some limitations.
Limited evidence—indicates modest scientific findings that support an association, but these findings have significant limitations.
Mixed evidence—indicates both supporting and opposing scientific findings for an association, with neither direction dominating.
Insufficient evidence—indicates the outcome has not been sufficiently studied to conclude whether or not there is an association between marijuana use and the outcome.
Body of research failing to show an association—indicates the topic has been researched without evidence of an association; is further classified as a limited, moderate, or substantial body of research.
In the following sections evidence statements will be discussed according to health topic and statements with enough findings to receive a substantial or moderate rating are displayed in tables. All statements, regardless of evidence level, are drafted by CDPHE technical staff, revised based on committee review and feedback from technical advisors and public stakeholders. Statements in their final form are approved by a vote of the committee.
3.1 Marijuana use among adolescents and young adults
The RMPHAC has reviewed the relationships between adolescent and young adult marijuana use on various areas of concern; including cognitive abilities, academic performance, mental health, and future substance use, displayed in Table 1. Specifically regarding cognitive and academic abilities, weekly marijuana use by adolescents is associated with deficits for at least twenty-eight days after last use. Weekly use among adolescents is also associated with failure to graduate from high school or complete a college degree. Information on how marijuana use affects short-term and long-term IQ is currently insufficient and limited, respectively. As with many of our statements that reflect long-term marijuana use, the paucity of long-term studies is a research gap that will hopefully improve due to the changing legal landscape of cannabis throughout the United States.
Substantial evidence
Moderate evidence
Benefits of quitting
Treatment for cannabis use disorder can reduce use and dependence [4, 5, 6, 7, 8, 9, 10]
Quitting or decreasing marijuana use lowers the risk of adverse mental health outcomes [11, 12, 13, 14]
Cognitive and academic effects
Weekly, or more frequent, use is associated with a lower rate of graduating high school [15, 16, 17, 18, 19, 20, 21, 22, 23]
Weekly, or more frequent, use is associated with a lower rate of attaining a college degree (among those who start a degree program) [19, 24, 25, 26, 27, 28, 29]
Weekly, or more frequent, use is associated with ongoing cognitive and academic impairment for at least 28 days after last use [30, 31, 32, 33, 34, 35]
Mental health
Daily or near daily use is associated with future psychotic disorders like schizophrenia [36, 37, 38, 39, 40, 41, 42, 43]
Using marijuana with higher THC concentration (>10% THC) is associated with continued use [38, 106, 107, 108]
Use of marijuana with higher THC concentration (>10% THC) is associated with future mental health symptoms and disorders [38, 78, 107]
Table 1.
Marijuana use among adolescents and young adults.
Adolescents and young adults who use marijuana are more likely to experience psychotic symptoms in adulthood (such as hallucinations, paranoia, and delusional beliefs), future psychotic disorders (such as schizophrenia), and suicidal thoughts or attempting suicide, when compared to adolescents and young adults who do not use marijuana. Additionally, those using marijuana with higher tetrahydrocannabinol (THC) concentration (>10% THC) are more likely than non-users to continue using and to develop future mental health symptoms and disorders. How marijuana use during adolescence affects symptoms or a diagnosis of anxiety in adulthood currently stands at a mixed evidence level, with fourteen articles contributing to this rating. Only one of which received a high quality rating and also reported mixed findings relevant to this evidence statement on anxiety [64]. Results from their main analysis did show an association with adolescent cannabis use and adulthood anxiety, however, results from a monozygotic-only co-twin control analysis reported no association [64].
Evidence shows that adolescents who use marijuana can develop cannabis use disorder, along with marijuana use being associated with developing use disorder for tobacco, alcohol, and other drugs. On a more positive note, evidence shows that adolescents who receive treatment for cannabis use disorder can decrease their use and dependence. Additionally, those who quit using marijuana have lower risks of adverse cognitive and mental health outcomes than those who continue to use.
3.2 Marijuana use and cancer
To assess how marijuana use may or may not be associated with cancer, the RMPHAC reviewed health effects of the chemicals released in marijuana smoke and vapor and evaluated how different rates of marijuana use relate to cancer. Strong evidence shows marijuana smoke contains many of the same cancer-causing chemicals found in tobacco smoke [109]. There is also substantial evidence that daily or near-daily marijuana smoking is associated with pre-malignant lesions in the airway. However, there is conflicting research for whether or not marijuana smoking is associated with lung cancer. As shown by the moderate evidence statement in Table 2, the body of research reviewed has failed to show an association between smoking less than the equivalent of one joint per day for 10 years and lung cancer.
Substantial evidence
Moderate evidence
Cancer and precancerous lesions
Daily or near daily use is associated with pre-cancerous lesions in airway [110, 111, 112]
Smoking less than the equivalent of one joint per day for 10 years is not associated with lung cancer [113, 114, 115, 116, 117, 118]
Chemicals in MJ smoke or vapor
Marijuana smoke contains many of the same cancer causing chemicals as tobacco smoke [109, 119, 120, 121, 122]
Genitourinary Cancer
Use among adult males is associated with increased risk of nonseminoma testicular cancer [123, 124, 125, 126, 127]
Table 2.
Marijuana use and cancer.
Apart from the respiratory system, most of our statements are not in Table 2 due to the limited evidence available concerning cancers of the bladder, prostate, head and neck. These limited statements all suggest these forms of cancer might not have any association with marijuana use. However, there is evidence that marijuana use among adult males may be associated with nonseminoma testicular cancer. High quality research on non-respiratory tract cancers related to marijuana use remains a research gap identified by the RMPHAC.
3.3 Marijuana use and cardiovascular effects
Related to cardiovascular health effects, how marijuana use associates with myocardial infarction, stroke, and death from cardiovascular causes were reviewed. Evidence shows that marijuana use or consumption in those under the age of fifty-five years are at an increased risk of ischemic stoke, as shown in Table 3. However, currently there is only limited scientific evidence to support our statements on myocardial infarction and death related to a cardiovascular event.
An important metric to understand is how THC blood levels compare from various marijuana methods of use and the numerous concentrations of THC in available products on the retail marijuana market. For example, there is substantial evidence that smoking more than 10 mg THC (or 10–20% of a 1 g marijuana joint) produces a blood THC level near or above 5 ng/mL within 10 min. As we see the THC concentration of marijuana products increase, we can expect this association to remain strong. One important finding in Table 4 is that it can take up to four hours after consuming an edible marijuana product to reach the peak THC blood concentration and feel the full effects. Another method of use, vaporized THC, shows moderate evidence of producing a similar blood THC level to smoking the same amount.
Substantial evidence
Moderate evidence
THC blood levels resulting from different exposures
It takes up to four hours after ingesting marijuana (edible products) to reach peak blood THC levels [146, 147, 148, 149, 150, 151]
Ingesting (edible products) more than 15 mg THC may produce a blood THC level above 5 ng/mL [148, 152, 153, 154]
Smoking more than 10 mg THC produces a blood THC level near or above 5 ng/mL within 10 min [152, 155, 156, 157, 158, 159]
Inhaling vaporized THC produces a blood THC level similar to smoking the same dose [149, 159, 160]
Secondhand exposure
Typical secondhand marijuana smoke exposure is unlikely to cause a positive drug screen by urine or blood [161, 162, 163, 164, 165, 166, 167, 168, 169]
Table 4.
Marijuana dose and drug interactions.
Within this topic the RMPHAC reviewed effects of secondhand marijuana smoke, drug-drug interactions involving marijuana, and relationships between marijuana and opioid use. There is credible evidence of clinically important drug-drug interactions between marijuana and multiple medications, including some anti-seizure medications and a common blood-thinner, warfarin. Data about potential interactions are lacking for many drugs at this time and are likely to evolve substantially in the coming years. Other than our statement about secondhand marijuana smoke exposure being unlikely to cause a positive drug screen, our statements in this topic area are all based on limited evidence. Health effects resulting from secondhand marijuana smoke exposure is an area lacking in research. There is also conflicting evidence for whether or not marijuana use is associated with a decrease in opioid use among chronic pain patients or individuals with a history of problem drug use.
3.5 Marijuana use and driving
As with any psychoactive substance it is imperative to know how marijuana affects a person’s ability to drive and the crash risks associated with use. To fully comprehend how marijuana causes driving impairment we must also understand the pharmacokinetics of THC in the human body to know how long these affects will persist after last use. Table 5 displays all driving related statements that have evidence to provide a substantial or moderate rated statement. Current research shows substantial evidence that recent marijuana use by a driver increases the risk of a motor vehicle crash. In addition, using alcohol and marijuana together increases impairment and the risk of a motor vehicle crash more than using either substance alone.
Substantial evidence
Moderate evidence
Combined marijuana and alcohol use
Combined use of marijuana and alcohol increases crash risk more than either substance alone [170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181]
Impairment and crash risk
Recent marijuana use/consumption by a driver increases the risk of a motor vehicle crash [170, 171, 172, 174, 182, 183, 184, 185, 186, 187, 188]
Higher THC blood level increases the risk of a motor vehicle crash [173, 178, 180, 189]
Blood THC levels of impaired drivers are higher now than they were in the past [201]
Orally ingesting more than 10 mg THC can lead driving impairment [146, 147, 153, 155]
Increased risk of driving impairment at blood THC as low as 2–5 ng/mL [155, 185, 190, 202, 203, 204, 205, 206]
Time to wait before driving
Waiting at least 6 after smoking less than 18 mg allows driving impairment to resolve or nearly resolve [155, 190, 207]
Waiting at least 6 h after smoking about 35 mg allows driving impairment to resolve or nearly resolve [157, 192, 196]
Waiting at least 8 h after orally ingesting less than 18 mg allows driving impairment to resolve or nearly resolve [147, 153, 155, 208]
Table 5.
Marijuana use and driving.
The RMPHAC also set out to determine how various patterns of marijuana use affect driving. People that consume marijuana less-than-weekly are likely to experience impaired driving after using marijuana containing ten milligrams or more of THC. This statement holds true for smoking or consuming edible marijuana products. Research on driving impairment for those that consume more frequently than weekly is currently lacking in scientific literature. Due to this our evidence statement on crash risk for different levels of use (less-than-weekly compared to more frequent use) has received an insufficient rating at this time.
Articles measuring THC blood levels were also assessed to evaluate for any correlation to driving impairment, crash risk, and to develop statements informing consumers the amount of time to wait prior to driving. There is substantial evidence, including a randomized clinical trial [202], which has displayed meaningful driving impairment with a whole blood THC of 2–5 ng/mL. Additionally, moderate evidence points to a positive relationship between THC blood level and motor vehicle crash risk. In order for marijuana consumers to allow impairment to resolve, less-than-weekly consumers should wait at least six hours after smoking or eight hours after eating or drinking marijuana products. When consuming larger amounts of THC or for people that consume more frequently, evidence is currently insufficient to determine the safe amount of time for impairment to wear off. Evidence is also showing that blood THC levels of marijuana-impaired drivers are higher now than in the past, likely resulting from the increasing THC concentration of available marijuana products.
3.6 Marijuana use and gastrointestinal or reproductive effects
The RMPHAC reviewed how marijuana use may affect gastrointestinal disease, particularly cyclic vomiting, and infertility or abnormal reproductive function. Displayed in Table 6, evidence shows that long-time, daily or near daily marijuana use is associated with cyclic vomiting, also called cannabinoid hyperemesis syndrome (CHS). A majority of evidence supporting this statement is from case reports or case series of identified CHS patients, however, many review articles detail diagnostic criteria, treatment options, and the physiology behind marijuana use and CHS presentation [220]. Regarding reproductive function, there is limited research showing marijuana use is associated with male infertility or abnormal function, however, the research is conflicting for women.
Substantial evidence
Moderate evidence
Cyclic vomiting
Cyclic vomiting can occur with long-time, daily or near daily marijuana use/consumption (cannabinoid hyperemesis syndrome) [209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219]
Table 6.
Marijuana use and gastrointestinal and reproductive effects.
3.7 Marijuana use and injury
The RMPHAC reviewed workplace, recreational and other non-driving injuries, burns from hash-oil extraction or failed electronic smoking devices, and physical dating violence. Evidence shows mixed results for marijuana use affecting the risk of workplace injury, recreational injury, and other types of non-driving-related injury. There have been many reports of severe burns resulting from home-extraction of butane hash oil leading to explosions, and cases of electronic smoking devices exploding, leading to trauma and burns.
Concerning dating violence, Table 7 shows our only statement reaching moderate or substantial levels of evidence is that young adult women who use marijuana are unlikely to perpetrate physical dating violence against their dating partners. Otherwise, evidence does show that young adults who use marijuana are unlikely to commit or be victims of physical dating violence, however evidence is limited at this time. Evidence for adolescent boys that use marijuana has mixed findings for physical dating violence perpetration and limited evidence for victimization, with evidence for adolescent girls being the opposite (Table 7).
Substantial evidence
Moderate evidence
Physical dating violence
Young adult women who use marijuana are unlikely to perpetrate physical dating violence [221, 222, 223, 224, 225, 226]
Table 7.
Marijuana use and injury.
3.8 Marijuana use and neurological, cognitive, and mental health effects
Similar to statements in our adolescent and young adult section, it is imperative to understand how marijuana could impact neurological, cognitive, and mental health in adult marijuana consumers. This section also explores how marijuana consumption relates to marijuana abuse and addiction among adult consumers. While our review on cognitive effects includes decision making, executive function, memory impairment, and lasting cognitive effects, strong evidence has been found only for memory impairment, as shown in Table 8. We have found substantial evidence that daily or near daily adult marijuana consumers are more likely than non-users to have memory impairments for at least seven days after last use. Evidence is mixed for whether or not these memory impairments or other cognitive effects last for at least twenty-eight days after last use, among the same population of adult consumers.
Substantial evidence
Moderate evidence
Cognitive effects
Daily or near daily use is associated with impaired memory for at least 7 days [30, 227, 228, 229, 230, 231, 232, 233, 234, 235]
Mental health effects
Use is associated with acute psychotic symptoms during intoxication, which are worse with higher doses [236, 237, 238, 239, 240, 241, 242, 243]
Daily or near daily use is associated with future psychotic disorders like schizophrenia [38, 42, 107, 244, 245, 246]
Use of marijuana with THC concentration > 10% is associated with future psychotic disorders like schizophrenia [38, 107, 247]
Substance use, abuse and addiction
Those who use marijuana can develop cannabis use disorder (addiction) [82, 85, 86, 93, 248, 249, 250, 251, 252]
Treatment for cannabis use disorder can reduce use and dependence [4, 6, 8, 9, 253, 254, 255, 256, 257]
Marijuana use and neurological, cognitive, mental health effects.
As with all psychoactive substances, mental health effects in adult marijuana consumers must be examined. An important acute effect of THC with substantial evidence is psychotic symptoms, such as hallucinations, paranoia, and delusional beliefs during intoxication, and these symptoms are worse with higher doses. Additionally, daily or near daily marijuana use is associated with developing a psychotic disorder such as schizophrenia. As detailed in our report focusing on the increasing concentration of THC in products available, there is increased public health concern as these products may lead to higher potential for adverse health effects in consumers [1]. This concern is substantiated by available research enabling us to provide a moderate rated statement showing association between higher concentration THC products and future psychotic disorders in adult marijuana consumers.
Finally, evidence shows marijuana consumers can experience withdrawal symptoms when abstaining and become addicted to marijuana or develop cannabis use disorder. However, as with adolescents, treatment for cannabis use disorder can reduce use and dependence in adult consumers. Many associations within this section lack high quality evidence or research currently exhibits mixed findings, such as marijuana use being associated with anxiety, depression, or bipolar disorder (Table 8).
3.9 Marijuana use during pregnancy and/or breastfeeding
Table 9 details our evidence concerning marijuana use during pregnancy and breastfeeding. Biological evidence shows THC passes through the placenta to the fetus and is present in the breast milk of women who use marijuana. Scientific evidence shows the fetus absorbs and metabolizes THC passed through the placenta and THC metabolites are found in the meconium or first stool passed by the newborn after birth. Additionally, infants who drink breast milk containing THC absorb and metabolize the THC. These statements show how important it is to understand how marijuana use during pregnancy and/or breastfeeding can affect the offspring or impact delivery of the offspring.
Substantial evidence
Moderate evidence
Effects on exposed offspring
Prenatal marijuana exposure is associated with reduced cognitive function, academic ability, and IQ scores in childhood [271, 272, 273, 274, 275, 276, 277, 278, 279, 280]
Prenatal marijuana exposure is associated with attention problems in childhood [273, 281, 282, 283, 284, 285]
Birth defects
Prenatal marijuana use is not associated with birth defects [286, 287, 288, 289, 290, 291, 292]
Preterm delivery or abnormal birth weight
Maternal use during pregnancy is associated with infants being born small for gestational age (birth weight less than 10th percentile for gestational age) [286, 287, 289, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305]
Biological evidence concerning marijuana use during pregnancy and breastfeeding
THC is passed through the placenta of women who use marijuana, the fetus absorbs and metabolizes the THC, and THC metabolites are found in the meconium [306, 307, 308, 309, 310].
THC is present in the breast milk of women who use marijuana. Infants who drink breast milk containing THC absorb and metabolize the THC [311, 312, 313, 314, 315, 316].
Table 9.
Marijuana use during pregnancy and/or breastfeeding.
Specifically regarding exposed offspring, the RMPHAC reviews potential effects starting at birth and later in childhood or adolescence. Marijuana use during pregnancy has shown to not be associated with birth defects in general, but limited evidence of an association with an increased risk of heart defects, stillbirth, and decreased growth in offspring. Stronger evidence was found for effects that are seen in offspring years after birth if a child’s mother used marijuana while pregnant. These include impaired cognitive function and academic ability, lower IQ scores, and attention problems in childhood.
3.10 Marijuana use and respiratory effects
While consumers have a variety of marijuana products to choose from, smoking marijuana flower remains the most common method of use and thus respiratory effects must be evaluated [317]. The RMPHAC reviews respiratory diseases such as chronic obstructive pulmonary disorder (COPD), chronic bronchitis and asthma, respiratory infections, lung function relative to smoked marijuana. The committee has also reviewed potential health effects of vaporized marijuana as those products have emerged on the legal market. Displayed in Table 10, strong evidence shows an association between daily or near-daily marijuana use and chronic bronchitis, including chronic cough, sputum production, and wheezing. Weaker evidence shows daily or near-daily marijuana use may be associated with bullous lung disease leading to pneumothorax in individuals younger than forty years of age. Additionally, limited evidence does show frequent smokers who switch from marijuana smoking to marijuana vaporizing may have fewer respiratory symptoms and improved pulmonary function. Finally, a notable effect of acute marijuana smoking is a short-term improvement in lung airflow, though evidence contributing to this statement is dated (Table 10).
Substantial evidence
Moderate evidence
Smoked marijuana
Use is associated with chronic bronchitis with cough, wheezing and mucus [318, 319, 320, 321, 322, 323, 324, 325, 326, 327]
Acute use is associated with short-term lung airflow improvement [328, 329, 330]
Table 10.
Marijuana use and respiratory effects.
3.11 Unintentional marijuana exposure in children
As marijuana becomes more accessible to the public, we must consider unintentional exposures in homes with children and how packaging can affect these. Strong evidence was found, shown in Table 11, that more unintentional exposures of children occur in states with increased legal access to marijuana, and exposures can lead to significant clinical effects requiring medical attention and even hospitalization. However, evidence does show that child-resistant packaging reduces unintentional pediatric marijuana poisonings (Table 11).
Substantial evidence
Moderate evidence
Legal marijuana access increases unintentional marijuana exposures in children [331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341]
Once evidence statements have been drafted and approved by the RMPHAC, the next step (number 6 from our systematic review process) is to translate the evidence into public health statements. These are designed to accurately reflect the evidence statements using language the public can understand. The committee also wanted to ensure these statements conveyed the volume and quality of research related to the outcome and allowed the statement to stand on its own without context. Similar to our evidence statements, these use standardized language to represent the strength of relationship and use the phrase “associated with” to represent epidemiologic associations that do not imply causation. As of the date of this book’s publication CDPHE has seventy-four public health statements corresponding to all our evidence statements rated moderate or substantial.
In a similar manner, public health statements are subsequently drafted into public health recommendations. These are synthesized in order to inform the development of evidence-based prevention and education campaigns performed by CDPHE. Furthermore, recommendations are separated by data quality issues, surveillance, and education. Our recommendations share common themes to those put forth by the National Academies of Sciences, Engineering, and Medicine’s review of health effects associated with cannabis and cannabinoids [345].
Data quality issues are defined as recommendations to improve current data collection deficiencies at the clinical or governmental level that prevent full analysis of public health outcomes related to marijuana use. It is especially important to improve data quality by systematically collecting information on the frequency, amount, THC content, and method of marijuana use in both public health surveillance and medical care settings. Clinicians should routinely screen for marijuana use during hospitalizations, especially among pregnant or adolescent patients.
Public health surveillance recommendations are based on improving capacity to detect an acute public health danger (e.g., real time emergency department surveillance to detect poisonings from contaminated product); the ability to characterize chronic public health dangers to support policy and other intervention decisions; or the ability to generate epidemiologic data to contribute to planning and evaluating population level interventions. Questions regarding marijuana use should be continued on population-based surveys such as the Behavioral Risk Factor Surveillance System, the Healthy Kids Colorado Survey, and Pregnancy Risk Assessment Monitoring System. Additionally, methods should be expanded to collect more detailed information, such as quantity and methods of use, THC content of products used, and adverse effects experienced.
Education recommendations are included to ensure evidence-based information on potential health effects of marijuana use is provided to the appropriate target audiences. Public education is especially important related to the effects of use during pregnancy, adolescent use, driving after use, increasing THC concentration of products, and unsafe storage around children. Education for health care providers should also be emphasized on the need for marijuana use screening, the known health effects of use, and encouraging more open dialog between providers and patients.
5. Research gaps
In addition to public health recommendations, important research gaps related to the population-based health effects of marijuana use were identified during the literature review process. These research gaps are based on common limitations of existing research or issues important to public education or policymaking. Research gaps particularly important to public health and safety include the need for: (1) research on the effects of marijuana use on pregnant women and their offspring, including while breastfeeding; (2) research on marijuana and marijuana products that contain THC concentrations consistent with products currently available in legalized markets; (3) research on health effects among individuals who have used marijuana frequently for a long period of time; (4) research on driving impairment among people who use marijuana more than weekly and may have developed tolerance; (5) research to better characterize the pharmacokinetics/pharmacodynamics, potential drug interactions, health effects, and impairment related to non-smoking methods of marijuana use such as edible products and vaporizing; and (6) research to better describe the risk of adverse health effects due to contamination of the marijuana product by fungi, mold, solvents, additives, heavy metals, and pesticides.
Other research gaps identify areas that need improvement in new research moving forward. Such as studies using better and more standardized indicators of marijuana use, including frequency, THC content, and route of exposure, including populations that use marijuana daily or near daily, and stratifying groups by age and gender. Finally one step to provide strong evidence would be research data on a community based cohort to study both beneficial and adverse health effects of marijuana consumption. Identifying these research gaps provides researchers and funding sources with an important framework to prioritize areas of research related to marijuana use and public health.
6. Conclusion
Since 2014, when CDPHE was designated to monitor the emerging science and medical information relevant to the health effects associated with marijuana use, the RMPHAC and CDPHE technical staff have conducted an ongoing systematic review of scientific literature to establish over one hundred evidence statements with eleven health topics. Our mission is to translate the science into meaningful public health statements and recommendations to inform and educate the general public, healthcare providers, and everyone in-between on the health effects associated with marijuana use.
First, the committee established a strict process to ensure a thorough and unbiased review, set up quarterly meetings to enable open discussions on a continuous basis, and come to consensus on the science and how to present this information to the public. After establishing our process, evidence from scientific research is constantly reviewed and added when appropriate to form a comprehensive review of marijuana health effects across eleven health topic. Strong evidence statements from all health topics were displayed in tables and key findings were detailed in subsections to provide an overview of effects associated with marijuana use across many different populations and health topics. Additional details were described on how these evidence statements are used to inform public health policy in the State of Colorado through public health recommendations and research gaps.
Acknowledgments
Elyse Contreras, DeLayna Goulding, Daniel I. Vigil, Katelyn E. Hall, Michael Van Dyke, Shireen Banerji, Russell Bowler, Ashley Brooks-Russell, Christopher Domen, Heather Krug, David J Kroll, Sharon Langendoerfer, Andrew Monte, Kenneth Novoa, Judith Shlay, Elizabeth Stuyt, George Sam Wang, Bernadette Albanese, Lisa Barker, Laura Borgelt, Alvin C. Bronstein, Todd Carlson, Rowena Crow, Teresa Foo, Ken Gershman, Tista Ghosh, Heath Harmon, Rebecca Helfand, Renee M. Johnson, Bruce Mendelson, Madeline Morris, Kristina T. Phillips, Allison Rosenthal, Kim Siegal, Scott Simpson, Christian Thurstone.
Conflict of interest
“The authors declare no conflict of interest.”
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Our mission is to translate the science into meaningful public health statements and recommendations to inform and educate the general public, healthcare providers, and everyone in-between on the health effects associated with marijuana use. This chapter summarizes evidence from all of our health topics; ranging from respiratory effects of marijuana to cognitive and academic effects of marijuana use on adolescents and young adults.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82942",risUrl:"/chapter/ris/82942",signatures:"Richard Holdman",book:{id:"11714",type:"book",title:"Cannabinoids - Recent Perspectives and Applications in Human Health",subtitle:null,fullTitle:"Cannabinoids - Recent Perspectives and Applications in Human Health",slug:null,publishedDate:null,bookSignature:"Dr. Steven P. James",coverURL:"https://cdn.intechopen.com/books/images_new/11714.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-579-8",printIsbn:"978-1-80356-578-1",pdfIsbn:"978-1-80356-580-4",isAvailableForWebshopOrdering:!0,editors:[{id:"343012",title:"Dr.",name:"Steven P.",middleName:null,surname:"James",slug:"steven-p.-james",fullName:"Steven P. James"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Systematic review development and process",level:"1"},{id:"sec_3",title:"3. Systematic review findings",level:"1"},{id:"sec_3_2",title:"3.1 Marijuana use among adolescents and young adults",level:"2"},{id:"sec_4_2",title:"3.2 Marijuana use and cancer",level:"2"},{id:"sec_5_2",title:"3.3 Marijuana use and cardiovascular effects",level:"2"},{id:"sec_6_2",title:"3.4 Marijuana dose and drug interactions",level:"2"},{id:"sec_7_2",title:"3.5 Marijuana use and driving",level:"2"},{id:"sec_8_2",title:"3.6 Marijuana use and gastrointestinal or reproductive effects",level:"2"},{id:"sec_9_2",title:"3.7 Marijuana use and injury",level:"2"},{id:"sec_10_2",title:"3.8 Marijuana use and neurological, cognitive, and mental health effects",level:"2"},{id:"sec_11_2",title:"3.9 Marijuana use during pregnancy and/or breastfeeding",level:"2"},{id:"sec_12_2",title:"3.10 Marijuana use and respiratory effects",level:"2"},{id:"sec_13_2",title:"3.11 Unintentional marijuana exposure in children",level:"2"},{id:"sec_15",title:"4. Public health statements and recommendations",level:"1"},{id:"sec_16",title:"5. Research gaps",level:"1"},{id:"sec_17",title:"6. Conclusion",level:"1"},{id:"sec_18",title:"Acknowledgments",level:"1"},{id:"sec_21",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Monitoring Health Concerns Related to Maijuana; Reports and Summaries. Colorado Department of Public Health and Environment. Colorado Department of Public Health and Environment. 2022. Available from: https://marijuanahealthinfo.colorado.gov/reports-and-summaries'},{id:"B2",body:'Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Journal of Clinical Epidemiology. 2009;62(10):1006-1012'},{id:"B3",body:'GRADE guidelines—best practices using the GRADE framework.: GRADE working group; 2014. 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Five years with 5 million child-resistant containers. Clinical Toxicology. 1974;7(1):91-95'},{id:"B343",body:'Clarke A, Walton WW. Effect of safety packaging on aspirin ingestion by children. Pediatrics. 1979;63(5):687-693'},{id:"B344",body:'Rodgers GB. The effectiveness of child-resistant packaging for aspirin. Archives of Pediatrics & Adolescent Medicine. 2002;156(9):929-933'},{id:"B345",body:'The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research. Washington, DC: The National Academies Collection: Reports Funded by National Institutes of Health; 2017'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Richard Holdman",address:"richard.holdman@state.co.us",affiliation:'
Colorado Department of Public Health and Environment, Denver, United States
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Consequently, filtennas not only save space and costs but also reduce transmission losses. In this chapter, three sorts of filtennas have been proposed: the first sort is band-pass/band-stop filtennas, which are mainly realized by assembling band-pass/band-stop filters and antennas to achieve the combined functions; the second sort is multi-resonator-cascaded filtennas, which are obtained by altering the coupled-resonators in the last stage of the filters to act as the radiating elements; and the third sort is near-field resonant parasitic, bandwidth-enhanced filtennas, which are accomplished through organically combining radiator and filtering structures. For the second and third sorts, it is worth noting that the design methods witness significant electrical size reduction without degrading the radiation performance of the filtennas in general.",signatures:"Ming-Chun Tang, Yang Wang and Ting Shi",authors:[{id:"267218",title:"Prof.",name:"Ming-Chun",surname:"Tang",fullName:"Ming-Chun Tang",slug:"ming-chun-tang",email:"tangmingchun@cqu.edu.cn"},{id:"276364",title:"MSc.",name:"Yang",surname:"Wang",fullName:"Yang Wang",slug:"yang-wang",email:"yang_wang_cqu@cqu.edu.cn"},{id:"276543",title:"Dr.",name:"Ting",surname:"Shi",fullName:"Ting Shi",slug:"ting-shi",email:"tingshi@cqu.edu.cn"}],book:{id:"8653",title:"Electromagnetic Materials and Devices",slug:"electromagnetic-materials-and-devices",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"147585",title:"Dr.",name:"Zion",surname:"Menachem",slug:"zion-menachem",fullName:"Zion Menachem",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sami Shamoon College of Engineering",institutionURL:null,country:{name:"Israel"}}},{id:"266637",title:"Dr.",name:"Vasa",surname:"Radonic",slug:"vasa-radonic",fullName:"Vasa Radonic",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"267175",title:"Dr.",name:"Goran",surname:"Kitic",slug:"goran-kitic",fullName:"Goran Kitic",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"BioSense Institute",institutionURL:null,country:{name:"Serbia"}}},{id:"267177",title:"Dr.",name:"Norbert",surname:"Cselyuszka",slug:"norbert-cselyuszka",fullName:"Norbert Cselyuszka",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"BioSense Institute",institutionURL:null,country:{name:"Serbia"}}},{id:"267826",title:"Dr.",name:"Licinius",surname:"Alcantara",slug:"licinius-alcantara",fullName:"Licinius Alcantara",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"268866",title:"Prof.",name:"Liying",surname:"Zhang",slug:"liying-zhang",fullName:"Liying Zhang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"268868",title:"Prof.",name:"Shuguang",surname:"Bi",slug:"shuguang-bi",fullName:"Shuguang Bi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Wuhan University",institutionURL:null,country:{name:"China"}}},{id:"268869",title:"Dr.",name:"Ming",surname:"Liu",slug:"ming-liu",fullName:"Ming Liu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Nanyang Technological University",institutionURL:null,country:{name:"Singapore"}}},{id:"276364",title:"MSc.",name:"Yang",surname:"Wang",slug:"yang-wang",fullName:"Yang Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"276920",title:"Dr.",name:"Vesna",surname:"Crnojevic-Bengin",slug:"vesna-crnojevic-bengin",fullName:"Vesna Crnojevic-Bengin",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Novi Sad",institutionURL:null,country:{name:"Serbia"}}}]},generic:{page:{slug:"OA-publishing-fees",title:"Open Access Publishing Fees",intro:"
The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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850 GBP Chapter - Book Series Topic (Annual Volume)
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
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Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Added Value of Publishing with IntechOpen
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Indexing and listing across major repositories, see details ...
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Long-term archiving
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Visibility on the world's strongest OA platform
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Live Performance Metrics to track readership and the impact of your chapter
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Dissemination and Promotion
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Benefits of Publishing with IntechOpen
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Proven world leader in Open Access book publishing with over 10 years experience
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+5,700 OA books published
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Most competitive prices in the market
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Fully compliant with OA funding requirements
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Optimized processes that assure your research is made available to the scientific community without delay
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Personal support during every step of the publication process
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+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
\n\t
850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\n\n
Services included are:
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An online manuscript tracking system to facilitate your work
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
\n\t
Assurance that your manuscript meets the highest publishing standards
\n\t
English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
\n\t
XML Typesetting and pagination - web (PDF, HTML) and print files preparation
\n\t
Discoverability - electronic citation and linking via DOI
\n\t
Permanent and unrestricted online access to your work
\n
\n\n
What isn't covered by the Open Access Publishing Fee?
\n\n
If your manuscript:
\n\n
\n\t
Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
\n\t
If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\n
\n\n
Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
\n\t
Long-term archiving
\n\t
Visibility on the world's strongest OA platform
\n\t
Live Performance Metrics to track readership and the impact of your chapter
\n\t
Dissemination and Promotion
\n
\n\n
Benefits of Publishing with IntechOpen
\n\n
\n\t
Proven world leader in Open Access book publishing with over 10 years experience
\n\t
+5,700 OA books published
\n\t
Most competitive prices in the market
\n\t
Fully compliant with OA funding requirements
\n\t
Optimized processes that assure your research is made available to the scientific community without delay
\n\t
Personal support during every step of the publication process
\n\t
+184,650 citations in Web of Science databases
\n\t
Currently strongest OA platform with over 175 million downloads
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Their work provided the base for the development of semiconductor photocatalysis for the environmental remediation and energy applications. Photoactivity of some semiconductors was found to be low due to larger band gap energy and higher electron-hole pair recombination rate. To avoid these problems, the development of visible light responsive photocatalytic materials by different approaches, such as metal and/or non-metal doping, co-doping, coupling of semiconductors, composites and heterojunctions materials synthesis has been widely investigated and explored in systematic manner. This chapter emphasizes on the different type of tailored photocatalyst materials having the enhanced visible light absorption properties, lower band gap energy and recombination rate of electron-hole pairs and production of reactive radical species. Visible light active semiconductors for the environmental remediation purposes, particularly for water treatment and disinfection are also discussed in detail. Studies on the photocatalytic degradation of emerging organic compounds like cyanotoxins, VOCs, phenols, pharmaceuticals, etc., by employing variety of modified semiconductors, are summarized, and a mechanistic aspects of the photocatalysis has been discussed.",book:{id:"7671",slug:"concepts-of-semiconductor-photocatalysis",title:"Concepts of Semiconductor Photocatalysis",fullTitle:"Concepts of Semiconductor Photocatalysis"},signatures:"Fatima Imtiaz, Jamshaid Rashid and Ming Xu",authors:[{id:"292882",title:"Dr.",name:"Jamshaid",middleName:null,surname:"Rashid",slug:"jamshaid-rashid",fullName:"Jamshaid Rashid"},{id:"302498",title:"Ms.",name:"Fatima",middleName:null,surname:"Imtiaz",slug:"fatima-imtiaz",fullName:"Fatima Imtiaz"},{id:"308434",title:"Prof.",name:"Ming",middleName:null,surname:"Xu",slug:"ming-xu",fullName:"Ming Xu"}]},{id:"17728",title:"Defect Related Luminescence in Silicon Dioxide Network: A Review",slug:"defect-related-luminescence-in-silicon-dioxide-network-a-review",totalDownloads:9508,totalCrossrefCites:46,totalDimensionsCites:99,abstract:null,book:{id:"332",slug:"crystalline-silicon-properties-and-uses",title:"Crystalline Silicon",fullTitle:"Crystalline Silicon - Properties and Uses"},signatures:"Roushdey Salh",authors:[{id:"48391",title:"Dr.",name:"Roushdey",middleName:null,surname:"Salh",slug:"roushdey-salh",fullName:"Roushdey Salh"}]},{id:"58469",title:"The Electrochemical Performance of Deposited Manganese Oxide-Based Film as Electrode Material for Electrochemical Capacitor Application",slug:"the-electrochemical-performance-of-deposited-manganese-oxide-based-film-as-electrode-material-for-el",totalDownloads:1754,totalCrossrefCites:4,totalDimensionsCites:8,abstract:"The transition metal oxide has been recognized as one of the promising electrode materials for electrochemical capacitor application. Due to the participation of charge transfer reactions, the capacitance offered by transition metal oxide can be higher compared to double layer capacitance. The investigation on hydrous ruthenium oxide has revealed the surface redox reactions that contributed to the wide potential window shown on cyclic voltammetry curve. Although the performance of ruthenium oxide is impressive, its toxicity has limited itself from commercial application. Manganese oxide is a pseudocapacitive material behaves similar to ruthenium oxide. It consists of various oxidation states which allow the occurrence of redox reactions. It is also environmental friendly, low cost, and natural abundant. The charge storage of manganese oxide film takes into account of the redox reactions between Mn3+ and Mn4+ and can be accounted to two mechanisms. The first one involves the intercalation/deintercalation of electrolyte ions and/or protons upon reduction/oxidation processes. The second contributor for the charge storage is due to the surface adsorption of electrolyte ions on the electrode surface.",book:{id:"6083",slug:"semiconductors-growth-and-characterization",title:"Semiconductors",fullTitle:"Semiconductors - Growth and Characterization"},signatures:"Chan Pei Yi and Siti Rohana Majid",authors:[{id:"197956",title:"Associate Prof.",name:"S.R.",middleName:null,surname:"Majid",slug:"s.r.-majid",fullName:"S.R. Majid"},{id:"216449",title:"Ms.",name:"Pei Yi",middleName:null,surname:"Chan",slug:"pei-yi-chan",fullName:"Pei Yi Chan"}]},{id:"60792",title:"TCAD Device Modelling and Simulation of Wide Bandgap Power Semiconductors",slug:"tcad-device-modelling-and-simulation-of-wide-bandgap-power-semiconductors",totalDownloads:2155,totalCrossrefCites:15,totalDimensionsCites:16,abstract:"Technology computer-aided Design (TCAD) is essential for devices technology development, including wide bandgap power semiconductors. However, most TCAD tools were originally developed for silicon and their performance and accuracy for wide bandgap semiconductors is contentious. This chapter will deal with TCAD device modelling of wide bandgap power semiconductors. In particular, modelling and simulating 3C- and 4H-Silicon Carbide (SiC), Gallium Nitride (GaN) and Diamond devices are examined. The challenges associated with modelling the material and device physics are analyzed in detail. It also includes convergence issues and accuracy of predicted performance. Modelling and simulating defects, traps and the effect of these traps on the characteristics are also discussed.",book:{id:"6625",slug:"disruptive-wide-bandgap-semiconductors-related-technologies-and-their-applications",title:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications",fullTitle:"Disruptive Wide Bandgap Semiconductors, Related Technologies, and Their Applications"},signatures:"Neophytos Lophitis, Anastasios Arvanitopoulos, Samuel Perkins and\nMarina Antoniou",authors:[{id:"236488",title:"Dr.",name:"Neophytos",middleName:null,surname:"Lophitis",slug:"neophytos-lophitis",fullName:"Neophytos Lophitis"},{id:"247344",title:"Dr.",name:"Marina",middleName:null,surname:"Antoniou",slug:"marina-antoniou",fullName:"Marina Antoniou"},{id:"247347",title:"Mr.",name:"Anastasios",middleName:null,surname:"Arvanitopoulos",slug:"anastasios-arvanitopoulos",fullName:"Anastasios Arvanitopoulos"},{id:"247349",title:"Mr.",name:"Samuel",middleName:null,surname:"Perkins",slug:"samuel-perkins",fullName:"Samuel Perkins"}]}],onlineFirstChaptersFilter:{topicId:"159",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks: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:107,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:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,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:21,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:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"August 3rd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",slug:"robert-koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,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},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"82526",title:"Deep Multiagent Reinforcement Learning Methods Addressing the Scalability Challenge",doi:"10.5772/intechopen.105627",signatures:"Theocharis Kravaris and George A. Vouros",slug:"deep-multiagent-reinforcement-learning-methods-addressing-the-scalability-challenge",totalDownloads:19,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Multi-Agent Technologies and Machine Learning",coverURL:"https://cdn.intechopen.com/books/images_new/11445.jpg",subseries:{id:"27",title:"Multi-Agent Systems"}}},{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:14,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/267218",hash:"",query:{},params:{id:"267218"},fullPath:"/profiles/267218",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()