",isbn:"978-1-83768-248-5",printIsbn:"978-1-83768-247-8",pdfIsbn:"978-1-83768-249-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"8bc7ffd7544fff1901301c787e64fada",bookSignature:"Prof. Magdy Elnashar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11998.jpg",keywords:"Preparation, Characterisation, Applications, Immobilised Cells, Biomaterials, Biofibers, Resins, Polysaccharides, Biocomposites in Health Sciences, Biocomposites in the Chemical Industry, Nanobiocomposites, Nano-Composites",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 27th 2022",dateEndSecondStepPublish:"July 29th 2022",dateEndThirdStepPublish:"September 27th 2022",dateEndFourthStepPublish:"December 16th 2022",dateEndFifthStepPublish:"February 14th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"15 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Prof. Magdy Elnashar received his M.Sc. 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1. Introduction
Gate-all-around (GAA) is a widely-using structure such as logic field-effect transistor (FET) due to its excellent short channel characteristics [1, 2, 3, 4, 5, 6] or its high surface-to-volume ratio [7, 8], 3-D NAND flash memory for bit-cost scalability [9, 10], photodiode due to its waveguide effect [11, 12], and gas sensor due to its high physical fill factor or surface-to-volume ratio [13, 14]. Especially for logic applications, GAAFETs have been introduced by attaining good gate electronics and increasing current drivability under the same active area.
Currently, fin-shaped FETs (FinFETs) have been scaled down to 10-nm node [15] and further to 5-nm node [16] by forming ultra-sharp fin for high current drivability while maintaining gate-to-channel controllability. GAAFETs are possibly showing great potential to substitute FinFETs in the following technology node, and the performance comparisons between FinFETs and GAAFETs have been investigated [3, 4, 5, 6, 17]. But more detailed analysis between FinFETs and GAAFETs is needed to set the device guideline by considering fine TCAD calibration and middle-of-line levels.
Therefore, in this work, DC/AC performances of 3-nm-node GAAFETs were investigated using fully-calibrated TCAD platform. By changing the GAA geometries, we found optimal GAA structure to minimize the RC delay for three different applications such as low power (LP), standard performance (SP), and high performance (HP) applications.
2. Device structure and simulation methods
All the simulation works were performed using Sentaurus TCAD [18]. Drift diffusion transport equations were calculated self-consistently with Poisson and electron/hole continuity equations. Density-gradient model was adopted for the quantum confinement of carriers within the channel. Slotboom bandgap narrowing model was used to consider the doping-dependent energy bandgap. Mobility models include Lombardi for the mobility degradation at the channel/oxide interface, inversion and accumulation layer model for impurity, phonon, and surface roughness scatterings, and low-field ballistic model for quasi-ballistic effects in ultra-short gate length (Lg). Shockley-Read-Hall, Auger, and Hurkx band-to-band tunneling recombination models were adopted. Deformation potential model was used to consider the stress-induced energy bandgap, effective mass, and effective density-of-states. All these physical models were used equivalently in [19, 20].
Figure 1 shows the schematic diagrams of FinFETs and three-stacked GAAFETs. FinFETs have highly-doped punch-through-stopper (PTS) at 2 × 1018 and 4 × 1018 cm−3 for NFETs and PFETs, respectively, in order to prevent the sub-fin leakage currents at off state [21, 22]. GAAFETs, on the other hand, have buried oxide (BOX) layer beneath the source/drain (S/D) regions without PTS so that the bottom leakage currents are completely blocked [1, 23]. Bulk FinFETs can adopt the BOX layer according to [24], but the conventional device structure was considered in this work. S/D doping concentrations of the n-type and p-type devices are 2 × 1020 and 4 × 1020 cm−3, respectively. Interfacial layer (IL), HfO2, and low-k spacer regions have the dielectric constants of 3.9, 22.0, and 5.0, respectively. Contact resistivity at S/D and silicide interface is fixed to 10−9 Ω·cm2 [25]. Equivalent oxide thickness (EOT) is 1.0 nm, which consists of 0.7-nm-thick IL and 1.7-nm-thick HfO2.
Figure 1.
Schematic diagrams of FinFETs and GAAFETs. 2-D cross-sections of nanosheet and nanowire channels were also specified to the right.
Table 1 shows the geometrical parameters and values of 3-nm-node FinFETs and GAAFETs. Contacted poly pitch (CPP) and fin pitch (FP) are 42 and 21 nm, following 3-nm technology node [5]. There are two types of GAAFETs: nanowire FETs (NWFETs) having the same width and thickness as WNW, and nanosheet FETs (NSFETs) having thin NS thickness (TNS) of 5 nm but wide NS width (WNS) as 10, 20, 30, 40, and 50 nm. The number of NW or NS channels (Nch) is varied as 1, 2, 3, 4, and 5.
Geometrical parameters
Values
CPP
Contacted poly pitch
42 nm
FP
Fin pitch
21 nm
NP
Nanowire/sheet pitch
WNW or WNS + 16 nm
Lg
Gate length
12 nm
Lsp
Spacer length
5 nm
Wfin
Fin width
5 nm
Hfin
Fin height
46 nm
WNW
Nanowire width
5, 6, 7, 8, 9, 10 nm
WNS
Nanosheet width
10, 20, 30, 40, 50 nm
TNS
Nanosheet thickness
5 nm
TSP
Nanowire/sheet spacing
10 nm
Nch
The number of channels
1, 2, 3, 4, 5
Table 1.
Geometrical parameters and values of FinFETs and GAAFETs.
Figure 2 shows the schematic process flows of GAAFETs. The detailed gate-las process flows are described in [1]. After depositing Si0.7Ge0.3/Si multi-layer and etching like fin structure, poly-Si gate and low-k regions are formed. Inner-spacer is formed by etching sidewalls of Si0.7Ge0.3 regions selectively and depositing low-k regions. Followed by depositing BOX layer, selective epitaxial growth of S/D regions is performed. After removing poly-Si gate, channel release process is performed by etching Si0.7Ge0.3 regions selectively. Replacement metal gate, silicidation, and metal contact formations are done afterwards.
Figure 2.
Process flows of GAAFETs. Key process schemes of GAAFETs are Si0.7Ge0.3/Si multi-layer stacking, inner-spacer formation, and channel release by etching Si0.7Ge0.3 regions selectively.
All the TCAD results were calibrated to Intel 10-nm node FinFETs [15]. Detailed calibration flows are as follows. Geometrical parameters such as Lg, fin width (Wfin), fin height (Hfin), CPP, and FP were referred from [15]. Subthreshold characteristics such as subthreshold swing (SS) and drain-induced barrier lowering (DIBL) were fitted by changing annealing temperature and time for proper S/D doping profiles. Saturation velocity was tuned to fit the drain current (Ids) in the saturation region, whereas minimum low-field mobility and ballistic coefficient were varied to fit the Ids in the linear region. Some parameters related to surface roughness scatterings were also modified to fit the Ids in the strong inversion region accordingly. These calibration flows were equivalent as in [26]. After calibration, FinFETs were scaled down to the 3-nm node for comparison with GAAFETs.
3. Results and discussion
3.1 DC performances of NWFETs and NSFETs
Figure 3 shows the Ids of all the GAAFETs having different WNW or WNS at the fixed Nch of 3 at the drain voltages (Vds) of 0.70 V. It is not shown in this figure, but the Ids increases generally as the WNW or WNS increases irrespective of Nch. As the WNW increases, the Ids shifts leftward and the gate-induced drain leakage (GIDL) increases by losing the gate-to-channel controllability [27]. P-type NWFETs have larger GIDL than n-type NWFETs due to larger S/D doping penetrations into the channel for p-type devices. On the other hand, NSFETs have small GIDL and Ids shifts as thin TNS of 5 nm forms 1-D structural confinement and maintains good short channel characteristics. To the following, there are three applications at different off-state currents (Ioff): LP at the Ioff of 100 pA/μm, SP at the Ioff of 10 nA/μm, and HP at the Ioff of 100 nA/μm [28]. These values were normalized to NP.
Figure 3.
Ids of n-type (top) and p-type (bottom) NWFETs and NSFETs having different WNW or WNS at the fixed Nch of 3 at the drain voltages (Vds) of 0.70 V. it is not shown in this figure, but the GAAFETs have the same Ids trends irrespective of Nch (Ids increases as the WNW or WNS increases).
Figure 4 shows SS and DIBL of all the devices. Threshold voltages (Vth) and SS are extracted at the constant current of Weff/Lg × 108 A, where Weff is the effective width equal to 2 × Hfin + Wfin for FinFETs, 4 × WNW × Nch for NWFETs, and (2 × WNS + 2 × TNS) × Nch for NSFETs. DIBL is calculated as the difference of the Vth at two different Vds of 0.05 and 0.70 V for n-type (−0.05 and − 0.70 V for p-type) devices [29]. NWFETs degrade the short channel characteristics much than FinFETs as the WNW is 9 and 10 nm. NSFETs, on the other hand, have smaller SS and DIBL than FinFETs even as the WNS increases up to 50 nm because the gate-to-channel controllability is maintained by GAA structure and thin TNS of 5 nm. But when the NWFETs have ultra-small WNS of 5 or 6 nm, 2-D structural confinement decreases the SS and DIBL greatly, which would be preferable for LP applications. It is not shown in this figure, but the SS and DIBL are independent of Nch.
Figure 4.
SS (left) and DIBL (right) of FinFETs, NWFETs, and NSFETs having fixed Nch of 3. It is not shown in this figure, but the GAAFETs have the same SS and DIBL irrespective of Nch.
Figure 5 summarizes the effective currents (Ieff) of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Ieff was calculated using two Ids at different Vds and gate voltages (Vgs) as
Figure 5.
Ieff of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Ieff of n-type and p-type FinFETs are also specified as yellow symbols. Blue regions indicate that the GAAFETs have superior Ieff than the FinFETs.
Ieff=IH−IL/lnIHILE1
where IH = Ids (Vgs = VDD, Vds = VDD/2) and IL = Ids (Vgs = VDD/2, Vds = VDD) [30], and VDD is the operation voltage fixed to 0.7 V. All the Ieff were normalized to the NP, and the Ioff were fixed to 10 nA/μm for SP applications. GAAFETs need to have at least the Nch of 3 to outperform the FinFETs. As the WNW is 9 nm, both n-type and p-type NWFETs suffer from short channel effects (SCEs) and thus have smaller Ieff than the devices having smaller WNW in spite of larger Weff. NSFETs, on the other hand, have larger Ieff as the WNS is larger as the SCEs are reduced by thin TNS of 5 nm. But even though small same SS and DIBL are maintained for all the Nch, the increasing rate of Ieff as a function of Nch decreases as Nch increases.
Figure 6 shows the S/D parasitic resistance (Rsd) of the GAAFETs having the WNW or 7 nm and the WNS of 30 nm as a function of Nch. Other WNW and WNS have the same Rsd trends and thus are not shown in this work. Rsd was possibly extracted using Y-function method due to the linearity of Y-function at high Vgs [31]. As the Nch increases, Rsd of the GAAFETs decrease but at decreasing rate. Furthermore, Rsd becomes saturated as the Nch is 3 or 4. This phenomena can be explained by 2-D schematic diagrams shown in the right of Figure 6. Since the S/D contacts reside at the top of the S/D epi, current paths start from the top toward the channels at the bottom. As the Nch increases, longer current paths are needed to flow the bottom-side channels, facing more Rsd components at the S/D epi. Thus, increasing the Nch beyond 3 or 4 does not help DC performance improvements greatly.
Figure 6.
Rsd of n-type and p-type GAAFETs having the WNW of 7 nm and the WNS of 30 nm as a function of Nch (left) and the 2-D schematic diagram of half of the GAAFETs showing the current paths and Rsd components (right).
3.2 AC performances of NWFETs and NSFETs
Figure 7 summarizes the gate capacitances (Cgg) of all the GAAFETs. The Cgg is extracted at the Vgs and the Vds of VDD. Generally, Cgg increases as the WNW (or WNS) or Nch increases due to the increased Weff. PFETs have larger Cgg than NFETs due to larger S/D doping concentrations and penetrations into the channels. Different from the Ieff trends, the GAAFETs have Nch smaller than 3 to outperform the FinFETs, thus there are performance trade-offs between Ieff and Cgg as a function of Nch. Furthermore, the increasing rate of Cgg as a function of Nch is constant while the increasing rate of Ieff as a function of Nch decreases, which would degrade the RC delay (= IeffVDD/Cgg) as the Nch increases.
Figure 7.
Cgg of n-type (top) and p-type (bottom) GAAFETs having different WNW (or WNS) and Nch. Cgg of n-type and p-type FinFETs are also specified as yellow symbols. Blue regions indicate that the GAAFETs have smaller Cgg than the FinFETs.
Figure 8 shows the Cgg and parasitic capacitances (Cpara) of the GAAFETs varying Nch and WNW (or WNS). Cpara is extracted at off-state for SP applications. For all the cases, PFETs have larger Cpara than NFETs due to larger S/D doping and penetrations into the channels [20]. At the fixed Nch of 3, larger WNW or WNS, except for p-type NWFETs, decreases the Cpara/Cgg because the proportion of the channels out of the metal gate increases. For the same reason, larger Nch decreases the Cpara/Cgg. Large Cpara/Cgg at the WNW of 9 nm for NFETs is because large SS forms on state before reaching strong inversion region.
Figure 8.
Cgg and Cpara of NWFETs (left) and NSFETs (right) having different WNW (or WNS) at the fixed Nch of 3 and having different Nch at the fixed WNW of 7 nm (or WNS of 30 nm). Percentages represent the Cpara/Cgg.
Figure 9 shows the S/D doping profiles of NFETs (top) and PFETs (bottom) having different WNW at the fixed Nch of 3. In general, NFETs have larger doping concentrations in the middle of channels than PFETs because the Ge intermixing within multi-stacked Si/Si0.7Ge0.3 layers increases the Ge concentration at the channels and assists more phosphorus dopants diffusing into the channels while it segregates boron dopants [32, 33, 34]. Both NFETs and PFETs increase the doping concentrations in the middle of channels as the WNW increases because the dopant segregations near the low-k spacer regions decrease [35]. But PFETs increase the doping concentrations in the middle of channels much due to smaller Ge intermixing for larger WNW. This great increase of the doping concentrations in the middle of channels increases the Cpara/Cgg for p-type NWFETs (as shown in Figure 8).
Figure 9.
S/D doping profiles of NFETs (top) and PFETs (bottom) having different WNW at the fixed Nch of 3. Doping concentrations in the middle of top-side channels are also specified.
Figure 10 finalizes the RC delay of all the GAAFETs for LP, SP, and HP applications. N-type FinFETs have smaller RC delay than p-type FinFETs for all the applications due to better short channel characteristics, greater Ieff (as shown in Figure 5) and smaller Cgg (as shown in Figure 8). For LP applications, n-type GAAFETs having small WNW equal to 5 or 6 nm can outperform n-type FinFETs by decreasing SS and DIBL critically. But as the Nch is 1 (or 5), the Ieff decreases greatly (or the Cgg increases greatly), thus degrading the RC delay. On the other hand, p-type GAAFETs have more WNW or WNS options to outperform p-type FinFETs because boron dopants of the GAAFETs are segregated by Si/Si0.7Ge0.3 intermixing and have more abrupt S/D doping profile than p-type FinFETs. For LP applications, both n- and p-type GAAFETs have the minimum RC delay at the WNW of 5 nm and the Nch of 4. For both SP and HP applications, both n- and p-type GAAFETs have the minimum RC delay at the WNS of 50 nm and the Nch of 3. As the WNS increases beyond 50 nm, RC delay decrease but a little (as shown in Appendix). All these RC delay are achieved by enhancing the Ieff rather than the Cgg. To outperform the FinFETs, therefore, GAAFETs should be NWFETs, showing outstanding short channel characteristics, for LP applications and NSFETs, showing superior DC performance, for SP and HP applications.
Figure 10.
RC delay of all the GAAFETs for (a) LP, (b) SP, and (c) HP applications. RC delay of FinFETs for three different applications are also specified. The devices having the RC delay smaller than FinFETs are marked as yellow.
4. Conclusion
3-nm-node GAAFETs have been analyzed by changing WNW (or WNS) and Nch using fully-calibrated TCAD. Compared to FinFETs, GAAFETs have smaller and SS and DIBL as the WNW is smaller than 9 nm but irrespective of the WNS. Both Ieff and Cgg of the GAAFETs increase as the Nch increases, but the increasing rate of Ieff decreases due to the increase of Rsd at the longer S/D epi. The increasing rate of Cgg, on the other hand, is almost constant. Because of these phenomena, Minimum RC delay are formed at the middle Nch of 3 or 4. The NWFETs having the WNW of 5 or 6 nm achieve smaller RC delay than the FinFETs by achieving better gate electronics for LP applications, whereas the NSFETs having the WNS of 40 or 50 nm increase the Ieff greatly and thus decrease the RC delay for SP and HP applications. Overall, GAAFETs are possible candidates to substitute FinFETs in the 3-nm technology node for all the applications by adopting different WNW or WNS.
Conflict of interest
The authors declare no conflict of interests.
Appendices and nomenclature
Figure A1 shows the DC/AC performances of the NSFETs as the WNS increases from 40 to 100 nm. Minimum RC delay are formed at the WNS of 50 nm and the Nch of 3 as shown in Figure 10, but much smaller RC delay can be attained as the WNS increases to 100 nm by increasing the Ieff rather than the Cgg even though larger WNS extends the device area. For the most, RC delay decrease by 5.4% for PFETs as the WNS increases from 40 to 100 nm.
Figure A1.
Ieff, Cgg, and RC delay of the NSFETs having the WNS of 40, 50, 60, 70, 80, 90, and 100 nm at the fixed Nch of 3 for SP and HP applications.
\n',keywords:"gate-all-around, nanowire, nanosheet, field-effect transistors, fin, RC delay, parasitic resistance, parasitic capacitance",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73506.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73506.xml",downloadPdfUrl:"/chapter/pdf-download/73506",previewPdfUrl:"/chapter/pdf-preview/73506",totalDownloads:757,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:5,introChapter:null,impactScore:0,impactScorePercentile:37,impactScoreQuartile:2,hasAltmetrics:1,dateSubmitted:"June 16th 2020",dateReviewed:"September 16th 2020",datePrePublished:"October 30th 2020",datePublished:"July 14th 2021",dateFinished:"October 8th 2020",readingETA:"0",abstract:"DC/AC performances of 3-nm-node gate-all-around (GAA) FETs having different widths and the number of channels (Nch) from 1 to 5 were investigated thoroughly using fully-calibrated TCAD. There are two types of GAAFETs: nanowire (NW) FETs having the same width (WNW) and thickness of the channels, and nanosheet (NS) FETs having wide width (WNS) but the fixed thickness of the channels as 5 nm. Compared to FinFETs, GAAFETs can maintain good short channel characteristics as the WNW is smaller than 9 nm but irrespective of the WNS. DC performances of the GAAFETs improve as the Nch increases but at decreasing rate because of the parasitic resistances at the source/drain epi. On the other hand, gate capacitances of the GAAFETs increase constantly as the Nch increases. Therefore, the GAAFETs have minimum RC delay at the Nch near 3. For low power applications, NWFETs outperform FinFETs and NSFETs due to their excellent short channel characteristics by 2-D structural confinement. For standard and high performance applications, NSFETs outperform FinFETs and NWFETs by showing superior DC performances arising from larger effective widths per footprint. Overall, GAAFETs are great candidates to substitute FinFETs in the 3-nm technology node for all the applications.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73506",risUrl:"/chapter/ris/73506",book:{id:"10071",slug:"nanowires-recent-progress"},signatures:"Jun-Sik Yoon, Jinsu Jeong, Seunghwan Lee, Junjong Lee and Rock-Hyun Baek",authors:[{id:"231361",title:"Dr.",name:"Jun-Sik",middleName:null,surname:"Yoon",fullName:"Jun-Sik Yoon",slug:"jun-sik-yoon",email:"junsikyoon@postech.ac.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}},{id:"329367",title:"Mr.",name:"Jinsu",middleName:null,surname:"Jeong",fullName:"Jinsu Jeong",slug:"jinsu-jeong",email:"js.jeong@postech.ac.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}},{id:"329368",title:"Mr.",name:"Seunghwan",middleName:null,surname:"Lee",fullName:"Seunghwan Lee",slug:"seunghwan-lee",email:"sh5233@postech.ac.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}},{id:"329369",title:"Mr.",name:"Junjong",middleName:null,surname:"Lee",fullName:"Junjong Lee",slug:"junjong-lee",email:"lee1539@postech.ac.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}},{id:"329370",title:"Prof.",name:"Rock-Hyun",middleName:null,surname:"Baek",fullName:"Rock-Hyun Baek",slug:"rock-hyun-baek",email:"rh.baek@postech.ac.kr",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Pohang University of Science and Technology",institutionURL:null,country:{name:"Korea, South"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Device structure and simulation methods",level:"1"},{id:"sec_3",title:"3. Results and discussion",level:"1"},{id:"sec_3_2",title:"3.1 DC performances of NWFETs and NSFETs",level:"2"},{id:"sec_4_2",title:"3.2 AC performances of NWFETs and NSFETs",level:"2"},{id:"sec_6",title:"4. Conclusion",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"},{id:"sec_7",title:"Appendices and nomenclature",level:"1"}],chapterReferences:[{id:"B1",body:'Loubet N, Hook T, Montanini P, Yeung C.-W, Kanakasabapathy S, Guillorn M, Yamashita T, Zhang J, Miao X, Wang J, Young A, Chao R, Kang M, Liu Z, Fan S, Hamieh B, Sieg S, Mignot Y, Xu W, Seo S.-C, Yoo J, Mochizuki S, Sankarapandian M, Kwon O, Carr A, Greene A, Park Y, Frougier J, Galatage R, Bao R, Shearer J, Conti R, Song H, Lee D, Kong D, Xu Y, Arceo A, Bi Z, Xu P, Muthinti R, Li J, Wong R, Brown D, Oldiges P, Wu T, Gupta D, Lian S, Divakaruni R, Gow T, Labelle C, Lee S, Paruchuri V, Bu H, Khare M. 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Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea
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1. Introduction
The present chapter shows one of the most relevant issues regarding the area of road safety since according to the WHO, road accidents are among the ten leading causes of death in the world [1]. “In Mexico, it is estimated that between 70% and 90% of traffic accidents are attributed to the driver, with human errors and driver offenses in traffic regulations as the two main contributing factors” [2].
Shell [3] exposes “Improving road education involves an analysis of human behavior, where both classroom instruction on safety issues, laws and regulations, vehicle operation, and those factors affecting driving are combined.” It is for these reasons that “the vast majority of road education exams have focused on accidents.” [4] The factors in these studies include age, income, and driver’s attitude.
In relation to the implementation of any road safety system, Ker et al. [5] and Mackay and Tiwari [6] acknowledge that human errors should be minimized in order to significantly improve road safety. In the circumstances of drivers, traffic safety policies recently implemented have been focused on improving their traffic behavior [7], particularly to endorse a better attitude when using roads [8, 9, 10]. Nonetheless, Mirzaei et al. [7] reported that while many drivers show a positive safety attitude in regards to traffic, there are specific circumstances that may induce a poor traffic performance from some of these road users. Therefore, the authors inform us about the need to illuminate such situations, containing any potential cultural aspects. For the diverse groups of road users, Factor et al. [11, 12] proposed a theoretical model to analyze the influence that some social and cultural characteristics of these groups have on traffic safety, reporting that road safety differs in cultural and social features, including lifestyles and attitudes.
This study analyzed together the socioeconomic and road knowledge characteristics of these users to determine the probabilities of being involved in a road accident. This issue arises from the research that has been done [3], which informs us that those who have knowledge of road education are less likely to be involved in accidents or to carry out traffic violations. Whereas Factor et al. [12], using a logistic regression, found a relationship between socioeconomic status and presence in traffic accidents, as to say there is a direct correlation between higher level of education and greater socioeconomic status, which lowers the probability of being involved in a road accident.
It is worth mentioning that the present research aims to develop a methodology to create, step by step, a model that determines which socioeconomic variables and road education are significant to determine the probability of being involved in a road accident, which was applied to a case study in the city of Santiago de Querétaro.
This is why it is important to analyze the behavior of drivers on public roads since one of the main factors of road accidents is the lack of education and knowledge that these users may have about road safety. Not only does lack of knowledge influence road safety but also social factors, such as differing cultures, social behavior, the age of a driver, and the socioeconomic status of the drivers. This is an explanation as to the importance of doing the study because knowing these aspects that were previously mentioned, are all aspects that can attribute to a driver’s performance when operating a vehicle.
2. Background
When examining the number of road mishaps as a meaning of a given country’s economic level, Xu et al. [13] concluded that “road users’ income is a determining factor for road safety.” Concurring to its 2013 Global Status Report on road safety, such a conclusion is also reached by the World Health Organization, as low- and middle-income countries show higher traffic death rates when associated with high-income economies. Additional authors also report this cause–effect relationship [12, 14, 15, 16]. Overall, these authors claim that a low per capita income is a decisive factor for traffic crashes.
These accidents affect different social areas, and for this reason the subject of road education is a responsibility that belongs to a whole society, which encompasses pedestrians, cyclists, motorcyclists, drivers of vehicles, passengers, and transportation. Improving road education involves an analysis of human behavior, where both classroom instruction on safety issues, laws and regulations, vehicle operation, and those factors affecting driving, as well as vehicle driving practice are combined with a trained instructor [3]. It is for these reasons that the vast majority of road education exams have focused on accidents [4].
Regarding age, on the other hand, much of the road safety literature focuses on high-risk drivers, often being young, low-income men with low education [17]. It is recognized that older people appear to be more safety-conscious [18].
In terms of income, it should be noted that per capita income has been identified as a determinant of overall injury mortality [19]. Based on research conducted by Zmud and Arce [20, 21], it is ensured that lower-middle income groups may be at increased risk of occupant motor vehicle injuries. Attitude is a very important factor in road education, which also predicts longitudinally an unsafe driver [22].
2.1 Multi-criteria models for the decision-making process
For this process, three decision-making models are discussed that are based on the manipulation of the simple related data that provide the means to develop indicators in a systematic way [23]. These decision-making criteria represent a multi-criteria approach, which must be compared with other processes of several criteria such as the qualification model, the hierarchical analytical process (AHP), and the multiple attribute utility theory. The AHP method is a method that has been applied to deal with problems in different areas, matching the sentences of intangible qualitative criteria with tangible quantitative criteria [24]. The AHP method was initially developed by Saaty [25], with the objective of determining the relative importance of a set of alternatives in a multi-criteria decision problem. There are three main steps in the AHP: design of the hierarchy, a prioritization procedure, and the calculation of the results.
3. Methodology
Recent road safety research focuses on the need to improve the “behavior” of drivers [7]. In this sense, we did not give the task of evaluating 5 (five) road users, such as pedestrian, cyclist, motorcyclist, vehicle driver, and freight truck driver.
The study consisted of an evaluation of the previously mentioned users determined by a sample size as a significant representation; this evaluation was applied through a questionnaire designed for each type of user, which was divided into two parts; the first containing information such as general data, socioeconomic level, age and origin of acquired knowledge and accident, second is designed with information such as regulations or recommendations, traffic signals, current situation in road safety and human factors, infrastructure, courtesy and urbanity and applied situations. It should be noted that because each questionnaire was designed by user type there are variants in some questions.
This research also has an important message for society and aims to contribute knowledge on the subject as well as to help in the reduction of traffic accidents in our country. For the execution of this project, we will be using the five steps of methodology to conduct this investigation, we will also describe each of these steps:
3.1 Step 1: knowledge of the context of the variables to be evaluated and their development
The main objective of this stage consists of bounded problems for which the fundamental parameters can be defined. For this activity, some elements are incorporated in the analysis and are obtained from a review of global, national, and local literature in relation to safety education programs and driving tests. As a result of this analysis, a list of specific questions involving six common variables around which two or three user-related questions are written is based on the comparative analysis of the necessary knowledge. Each question was obtained through a review of the literature, the resulting number of questions for each of the users of the infrastructure is as follows: 24 for drivers of vehicles, 24 for freight conductors, 24 for motorcycle users, 21 for bicycle users, and 21 for pedestrians.
3.2 Step 2: structuring the questionnaire and evaluation
Within this stage, once the questions were established in the context of the selected variables two parallel processes will be carried out: the planning for the execution of the survey and the establishment of the weighting factors for the survey questions. The AHP method will be selected for this process, as it represents a structured and computerized process in which comparisons are made on a peer basis, which provides some evidence regarding the assessments made by experts of the Mexican Institute Transport (IMT) and the Autonomous University of Querétaro (UAQ). To obtain the reason scales of the AHP methodology, we compared the set of peer evaluations for each question. The peer comparison was as follows: 1 = equal, 3 = moderate, 5 = strong, 7 = very strong, and 9 = extreme.
3.3 Step 3: experimental design and sample size for survey operation
In this step, we will determine the size of the sample of users of the road examined which is calculated according to the number of inhabitants of the area [26] and the means of transport chosen by the users, as reported by Obregón and Betanzo [27].
n=N∗Zα2p∗qd2∗N−1+Zα2∗p∗qE1
Where N is the total number of inhabitants in the area (804 663 de Santiago de Queretaro), Zα = 1.96 (for a reliability 95%), p = expected proportion (in this case 5% = 0.05), q = 1 - p (in this case 1- 0,05 = 0,95), and d = precision (can be 1% to 3%; 2% was selected).
According to Eq. (1), 207 individuals were needed. This sample size considers individuals using the different means of transportation listed in Table 1, where it can be observed that freight vehicle, motorcycle, and bicycle users were the least frequent road users, with 1%, 1%, and 0.7%, respectively. To increase the reliability of these users, the sample size was increased to 20, for each of these modes. The number of validated questionnaires was 254.
Freight vehicle
Car
Motorcycle
Bicycle
Walk
Rest
Total
Distribution of users by mean of transport (%)
1.00
32.50
1.00
0.70
10.10
54.00
100
Percentage of sample size per type of infrastructure user
1
33
1
1
10
54
100
Estimated sampled
5
148
5
3
46
246
453
Total sampled
20
148
20
20
46
0
254
Table 1.
Sample and user distribution by transport means in Santiago de Querétaro. Own elaboration by the distribution data from Ref. [27].
The specific public areas for applying the survey were selected as a function of the type of transport infrastructure user: (1) public spaces, in which people spend at least 10 minutes completing some paperwork; (2) spaces around public schools, in which students move; and (3) recreational areas, in which users have more time to respond the survey (e.g. malls and public parks).
3.4 Step 4: database processing
In this process, we will compile the database obtained through the questionnaires applied to each user evaluated. Subsequently, this database will be analyzed to know the socioeconomic and road users’ knowledge. In the following graph (see Figure 1), the analysis of the variables of road education performed with the results obtained by the surveys in each one of the evaluated users is shown. This shows that the users that resulted with the lowest road knowledge in general are freight drivers (FD) and vehicle drivers (VD), unlike cyclists (C) who obtained the highest level of knowledge. At the same time, we can observe that the motorcyclists (M) obtained a low rating in regulation and recommendations (R&R); in contrast, the pedestrian (P) proved to have low knowledge in courtesy and urbanity (C&U).
Figure 1.
Road education grade of each user.
The rest of the variables of road education by its initials are classified in the following form: traffic signals (TS), current situation in road safety and human factor (CRS&HF), infrastructure (Infra), and applied situations (AS).
3.5 Step 5: the probabilistic model
In the literature, the use of Logit models has been reported to estimate the probability of accidents [7, 28]. In this sense, the present research project estimated the presence of road accidents using Logit models. These models are estimated using the commercial software NLOGIT version 5, which was used for the same objective by Tay [29]; who mentions that binary regression models are adequate techniques to predict a binary dependent variable as a function of predictor variables.
Due to its ease in its estimation, the logit transformation is one of the most used in studies, this conducive search of a model of choice is more comfortable analytically, and the result was the binary logit model. This is under the assumption that εn is logistically distributed [29]; and the probability of choosing alternative i is given by Eq. (2).
Pni=11+e−μVin−VjnE2
For this model, the dependent variable P(i), is a probability (between 0 and 1) that cannot be observed; only the choices of each individual are observed and these are variables (0 and 1).
4. Results and discussion
This section describes the logit models estimated to determine which socioeconomic and road education variables are significant to determine the probability of being involved in a road accident considering the means of transport used in their mobility. Depending on the mode of transport, the survey asks the user if they have been in a traffic accident in their life and during the last 12 months. Subsequently, each of the models obtained from each analyzed user is described. It should be noted that the first model (Model 1) was analyzed requesting the user if he has been involved in a traffic accident in his life. Unlike the second model (Model 2), which represents if you have been in a traffic accident in the last 12 months.
4.1 Freight driver
Two models were analyzed, in the first model, it can be seen that the significant variable is the income. Unlike Model 2, the most significant variable turned out to be the years with the driver’s license (YDL) that the user has. It is worth mentioning that the variable that resulted most significantly in freight driver to determine the probability of being involved in a road accident is theincome (0.8345) (Table 2).
Model 1
Model 2
Coef
SE Coef
Coef
SE Coef
Intercept
−3.7912*
2.0713
-21.4281 ·
16.6554
(−1.830)
(−1.287)
Income
0.8345*
0.4865
—
—
(1.715)
—
YDL
—
—
−0.7845 ·
0.6018
—
(−1.304)
Table 2.
Logit model, freight driver probability to be involved in a traffic crash.
Note: ***, **, *, · = significance at 1, 5%, 10%, and 15% level.
4.2 Vehicle driver
The first two models were analyzed, showing the following variables that are significant: if the user has a driver’s license (DL) and the age at which the road knowledge was obtained (ARK). In Model 2, the most significant variables were age (Age) and income (Income). It should be noted that the variable that resulted most significantly in vehicle drivers is driver’s license (2.4749) (Table 3).
Model 1
Model 2
Coef
SE Coef
Coef
SE Coef
Intercept
−1.2401*
0.6994
−2.9609**
1.1795
(−1.773)
(−2.510)
Age
—
—
−0.8669***
0.2815
—
−3.079
Income
—
—
0.4208**
0.1791
—
(2.349)
DL
2.2472***
0.6498
2.4749**
1.0788
(3.458)
(2.294)
ARK
−0.4100**
0.1866
—
—
(−2.197)
—
Table 3.
Logit models, vehicle driver probability to be involved in a traffic crash.
Note: ***, **, *, = significance at 1, 5%, and 10% level.
4.3 Motorcyclist
Two models were analyzed in Model 1, we can see the following variables that are significant: the level of road knowledge (LRK) and the courtesy and urbanity (C&U) that the user has. In Model 2, only a significant variable was obtained, which is the years with a driver’s license (YDL) that the user has. It is worth mentioning that the variable that was most significant in motorcyclists to determine the probability of being involved in a road accident is the courtesy and urbanity (27.5462) (Table 4).
Model 1
Model 2
Coef
SE Coef
Coef
SE Coef
Intercept
−5.9537 ·
3.7085
−4.8979 ·
3.6383
(−1.605)
(−1.346)
LRK
1.0178 ·
0.7227
—
—
(1.408)
—
YDL
—
—
−0.4753 ·
0.2785
–
(−1.707)
C&U
27.5462 ·
17.581
—
—
(1.567)
—
Table 4.
Logit models, motorcyclist probability to be involved in a traffic crash.
Note: ***, **, *, · = significance at 1, 5%, 10%, and 15% level.
4.4 Cyclists
For this user, only one model was analyzed, due to the fact that the data obtained show that they were not involved in an accident in the last 12 months. In the following model, the following variables were found to be significant: income (Income) and courtesy and urbanity (C&U) that these users may have on the infrastructure. It should be mentioned that the variable that was most significant in cyclists is the courtesy and urbanity of users (−18.9062) (Table 5).
Model
Coef
SE Coef
Intercept
0.9418 ·
1.9526
(0.482)
Income
0.5037 ·
0.3304
(1.524)
C&U
−18.9062 ·
13.2425
(−1.428)
Table 5.
Logit model, cyclist probability to be involved in a traffic crash.
Note: ***, **, *, · = significance at 1, 5%, 10%, and 15% level.
4.5 Pedestrian
As we analyzed Model 1, we can see the following variables that were significant: age (Age) and applied situations of users (AS). In contrast to Model 2, the most significant variables were the income (Income), level of road knowledge (LRK) they believe they have, and the age at which they obtained road knowledge (ARK). The significant variables that influence the probability of the pedestrian being involved in a traffic accident are applied situations (−10.2266) and the age at which they obtained road knowledge (−1.2199) (Table 6).
Model 1
Model 2
Coef
SE Coef
Coef
SE Coef
Intercept
−1.286*
0.6501
−6.0174*
3.2642
(−1.859)
(−1.843)
Income
—
—
0.8987*
0.4893
—
(−1.837)
Age
0.5841**
0.2457
—
—
(−2.377)
—
LRK
—
—
1.139*
0.6655
—
(−1.712)
ARK
—
—
−1.2199*
0.685
—
(−1.781)
AS
−10.2266*
5.358
—
—
(−1.909)
—
Table 6.
Logit model, pedestrian probability to be involved in a traffic crash.
Note: ***, **, *, · = significance at 1, 5%, 10%, and 15% level.
5. Conclusions
The chapter shows how the statistical logit probability model can characterize the effect of socioeconomic and educational factors on the population and the probability of being involved in a traffic accident. The overall result for the population surveyed identify both the level of road education and the income of the users’ infrastructure. The significant variables that influence the probability of the user being involved in a traffic accident by transport mode are as follows:
Amongst freight drivers, it was found that the most significant variables influencing the probability of being involved in a road accident are income and years with a driver’s license. Vehicle drivers, age (Age), income (Income), if you have a driver’s license (DL), and the age at which you gained road knowledge (ARK) were found to be the most significant variables to determine the probability of being in a road accident. It was found that for motorcyclists the factors were the level of road knowledge (LRK) they were considered to have, years of driver’s license (YDL) and Courtesy and Urbanity (C&U) as being the most significant variables for these users. For cyclists, it was found that income as well as courtesy and urbanity were the most significant variables. On the other hand, for pedestrians, it was found that the income, age, level of roadway knowledge that they considered to have, the age at which they obtained road knowledge, and the situations applied were the most significant variables.
In the case of motorized means of transport, the following aspects should be considered; age of users, socioeconomic characteristics, age and origin of acquired knowledge, and courtesy and urbanity. In the case of nonmotorized means of transport, the aspects to be taken into account are age, socioeconomic characteristics, age and origin of acquired knowledge, courtesy and urbanity, and the situations applied in this way.
The results of this research can be useful in defining road safety policies. In this sense, Mirzaei et al. [7] suggest that campaigns could be carried out to strengthen educational programs to minimize the probability of road accidents, considering the socioeconomic status and road education aspects of road users.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"traffic accidents, probability, road education, socioeconomic level, transport modes, logit",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82970.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82970.xml",downloadPdfUrl:"/chapter/pdf-download/82970",previewPdfUrl:"/chapter/pdf-preview/82970",totalDownloads:4,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 24th 2022",dateReviewed:"July 5th 2022",datePrePublished:"August 6th 2022",datePublished:null,dateFinished:"August 6th 2022",readingETA:"0",abstract:"Road education is one of the most relevant issues focused to reduce traffic accidents, so it is important to analyze the driver’s behavior on the roads. International research has found evidence for a relationship between socioeconomic characteristics and traffic accidents. In this sense, the chapter shows a methodology to estimate the probability to be involved in a road accident, considering the road education and the socioeconomic characteristics of the population of a specific region, taking the Santiago de Querétaro city (in México) as a study case. Through a logit model estimation and a survey applied to pedestrian, cyclist, motorcyclist, car driver, and freight driver allow us to determine which socioeconomic variables and road education are significant to determine the probability of being involved in a road accident.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82970",risUrl:"/chapter/ris/82970",signatures:"Saúl Antonio, Obregón Biosca, José Luis Reyes Araiza and Miguel Angel Pérez Lara y Hernández",book:{id:"12021",type:"book",title:"Applied Probability Theory - New Perspectives, Recent Advances and Trends",subtitle:null,fullTitle:"Applied Probability Theory - New Perspectives, Recent Advances and Trends",slug:null,publishedDate:null,bookSignature:"Dr. Abdo Abou Jaoudé",coverURL:"https://cdn.intechopen.com/books/images_new/12021.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83768-296-6",printIsbn:"978-1-83768-295-9",pdfIsbn:"978-1-83768-297-3",isAvailableForWebshopOrdering:!0,editors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}],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. Background",level:"1"},{id:"sec_2_2",title:"2.1 Multi-criteria models for the decision-making process",level:"2"},{id:"sec_4",title:"3. Methodology",level:"1"},{id:"sec_4_2",title:"3.1 Step 1: knowledge of the context of the variables to be evaluated and their development",level:"2"},{id:"sec_5_2",title:"3.2 Step 2: structuring the questionnaire and evaluation",level:"2"},{id:"sec_6_2",title:"3.3 Step 3: experimental design and sample size for survey operation",level:"2"},{id:"sec_7_2",title:"3.4 Step 4: database processing",level:"2"},{id:"sec_8_2",title:"3.5 Step 5: the probabilistic model",level:"2"},{id:"sec_10",title:"4. Results and discussion",level:"1"},{id:"sec_10_2",title:"4.1 Freight driver",level:"2"},{id:"sec_11_2",title:"4.2 Vehicle driver",level:"2"},{id:"sec_12_2",title:"4.3 Motorcyclist",level:"2"},{id:"sec_13_2",title:"4.4 Cyclists",level:"2"},{id:"sec_14_2",title:"4.5 Pedestrian",level:"2"},{id:"sec_16",title:"5. Conclusions",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'World Health Organization (WHO). Global Status Report on Road Safety 2018. Geneva: World Health Organization; 2018. p. 420'},{id:"B2",body:'García J, Acosta S, Vázquez C. Educación vial y sustentabilidad: Hacia una convivencia y equilibrio urbano México. Mexico: Universidad Autónoma del Estado de México; 2010. p. 152'},{id:"B3",body:'Shell DF, Newman IM, Córdova-Cazar AL, Heese JM. Driver education and teen crashes and traffic violations in the first two years of driving in a graduated licensing system. Accident; Analysis and Prevention. 2015;2015(82):45-52. DOI: 10.1016/j.aap.2015.05.011'},{id:"B4",body:'Lonero L, Mayhew D. Large-scale Evaluation of Driver Education: Review of the Literature on Driver Education Evaluation 2010 Update. Washington, D.C.: AAA Foundation for Traffic Safety; 2010. 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Belmont: Lifetime Learning Publications; 1982. p. 292'},{id:"B26",body:'INEGI. Censo de población y vivienda 2010. México: INEGI; 2010'},{id:"B27",body:'Obregón S, Betanzo E. Los viajes urbanos en una ciudad media mexicana, caso de estudio: Santiago de Querétaro Economía. Sociedad y Territorio. 2015;XV(47):61-98. DOI: 10.22136/est002015554'},{id:"B28",body:'Shinar D, Schechtman E, Compton R. Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. Accident; Analysis and Prevention. 2001;33:111-116. DOI: 10.1016/s0001-4575(00)00021-x'},{id:"B29",body:'Tay R. A random parameters probit model of urban and rural intersection crashes. Accident; Analysis and Prevention. 2015;84:38-40. DOI: 10.1016/j.aap.2015.07.013'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Saúl Antonio",address:"saul.obregon@uaq.mx",affiliation:'
Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro, México
Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro, México
'},{corresp:null,contributorFullName:"José Luis Reyes Araiza",address:null,affiliation:'
Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro, México
'},{corresp:null,contributorFullName:"Miguel Angel Pérez Lara y Hernández",address:null,affiliation:'
Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro, México
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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.
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Permanent and unrestricted online access to your work
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Open Access Funding
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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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Choosing to publish with IntechOpen ensures the following benefits:
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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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Viana and Carole L. Wallis",authors:[{id:"54659",title:"Dr.",name:"Carole",middleName:null,surname:"Wallis",slug:"carole-wallis",fullName:"Carole Wallis"},{id:"60112",title:"Ms",name:"Raquel",middleName:null,surname:"Viana",slug:"raquel-viana",fullName:"Raquel Viana"}]},{id:"65764",title:"Supplier Evaluation and Selection in Automobile Industry",slug:"supplier-evaluation-and-selection-in-automobile-industry",totalDownloads:1841,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In automobile industry, to operate effectively the supply chain management, the purchasing function is very important to perform effectively. It is the responsibility of purchasing department to choose the correct suppliers to purchase the required products for their company. Thus, supplier evaluation technique is essential for purchase manager’s point of view to choose the best supplier among available suppliers. The literature addresses quality, delivery, technology, value and service as the five most common criteria used for supplier quality evaluation. In this chapter, approach of evaluation and selection of supplier has been presented as per the ISO 9000/TS16949 standards. Considering the most important criteria for evaluating the quality of suppliers based on a review of the literature and observation in practice. Finally, these organizations continuously review and implement effective quality systems following the rigorous ISO 9000/TS16949 series of standards and most automobile companies have developed in-house procedures and software for the supplier selection process.",book:{id:"7454",slug:"industrial-engineering",title:"Industrial Engineering",fullTitle:"Industrial Engineering"},signatures:"Lokpriya Gaikwad and Vivek Sunnapwar",authors:[{id:"246830",title:"Prof.",name:"Lokpriya Mohanrao",middleName:null,surname:"Gaikwad",slug:"lokpriya-mohanrao-gaikwad",fullName:"Lokpriya Mohanrao Gaikwad"},{id:"251857",title:"Dr.",name:"Vivek K.",middleName:null,surname:"Sunnapwar",slug:"vivek-k.-sunnapwar",fullName:"Vivek K. Sunnapwar"}]},{id:"43383",title:"Improving Operations Performance with World Class Manufacturing Technique: A Case in Automotive Industry",slug:"improving-operations-performance-with-world-class-manufacturing-technique-a-case-in-automotive-indus",totalDownloads:26585,totalCrossrefCites:11,totalDimensionsCites:23,abstract:null,book:{id:"3216",slug:"operations-management",title:"Operations Management",fullTitle:"Operations Management"},signatures:"Fabio De Felice, Antonella Petrillo and Stanislao Monfreda",authors:[{id:"161682",title:"Prof.",name:"Fabio",middleName:null,surname:"De Felice",slug:"fabio-de-felice",fullName:"Fabio De Felice"},{id:"167280",title:"Dr.",name:"Stanislao",middleName:null,surname:"Monfreda",slug:"stanislao-monfreda",fullName:"Stanislao Monfreda"},{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo"}]},{id:"63005",title:"Industry 3.0 to Industry 4.0: Exploring the Transition",slug:"industry-3-0-to-industry-4-0-exploring-the-transition",totalDownloads:1803,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This work is a How-To-Guide for DigitALIZAtion of Industry 4.0 Manufacturing. It provides a novel ALIZA Canvas and ALIZA Process supported by a comprehensive ALIZA Toolset. This output is derived from observed, tangible deficiencies in contemporary functional communications in manufacturing. This study proposes an innovative approach with robust methodologies for strategic alignment of the technical and business components in manufacturing. The requirement for a supplementary educational infrastructure, to address the pronounced educational shortcomings and knowledge gaps in the transition to Industry 4.0 is outlined. An explanation is provided of how E-Cubers (our own educational organization) will design, develop, and deliver educational programmes on Topics relevant to achieving Industry 4.0 Equipment Engineering Excellence. It defines and tests the novel concept of the E-Cubers Eight Ps; encompassing prioritized problem solving, via portfolios and projects, through peer collaboration within a defined technology playground with emphasis on learning and playing with passion. The E-Cubers Eight Ps is combined with The E-Cubers Library to deliver a truly comprehensive specialist, national learning framework. This holistic approach will ultimately enable Ireland to lead the way in Industry 4.0 by doing what we do best “ag spraoi agus ag imirt” (Gaelic – playing by having fun and competing).",book:{id:"7436",slug:"new-trends-in-industrial-automation",title:"New Trends in Industrial Automation",fullTitle:"New Trends in Industrial Automation"},signatures:"Shane Loughlin",authors:null}],onlineFirstChaptersFilter:{topicId:"119",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"1082338",title:"Capacitated Clustering Models to Real Life Applications",slug:null,totalDownloads:1,totalDimensionsCites:0,doi:"10.5992/intechopen.1000213",abstract:'
This chapter considers the use of different capacitated clustering problems and models that fits better in real-life applications such as household waste collection, IT teams layout in software factories, wholesales distribution, and staff’s home collection or delivery to/from workplace. Each application is explored in its regular form as it is being developed by contractors and/or users. We consider for each application the aspects of solving the problem by the appropriate mathematical programming model and decision support methodology (using aggregated Geographical Information System and mobile technology) to hold correctly and most precisely the problems and difficulties related to instances in evaluation. The experience on these fields is here revealed in detailed form as the results obtained by using the techniques here explained.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Marcos J. Negreiros, Nelson Maculan, Augusto W.C. Palhano, Albert E.F. Muritiba and Pablo L.F. Batista"},{id:"81676",title:"Multiscale Modeling Framework for Defect Generation in Metal Powder Bed Fusion Process to Correlate Process Parameters and Structural Properties",slug:"multiscale-modeling-framework-for-defect-generation-in-metal-powder-bed-fusion-process-to-correlate-",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.104493",abstract:"Powder Bed Fusion (PBF) is one of the most popular additive manufacturing methods employed extensively to fabricate complex parts especially in industries with stringent standard criteria, including aerospace, medical, and defense. DMLS/PBF fabrication of parts that is free of defects represents major challenges. A comprehensive study of thermal defects, contributing parameters, and their correlation is necessary to better understand how process specifications initiate these defects. Monitoring & controlling temperature and its distribution throughout a layer under fabrication is an effective and efficient proxy to controlling process thermal evolution, which is a completely experimental technique. This being highly costly specifically for metal printing, computer-based numerical simulation can significantly help the identification of temperature distribution during the printing process. In this paper, a multiscale modeling technique is demonstrated with commercially available software tools to correlate the defect generation in metal PBF process and significant process parameters. This technique can help efficiently design the process setting in addition to or even absence of experimental monitoring data. This research work is a part of a larger project of closed-loop control strategy development using physics-based modeling and graph-based artificial neural network implementation for reducing thermally induced part defects in metal 3D printed process.",book:{id:"11171",title:"Trends and Opportunities of Rapid Prototyping Technologies",coverURL:"https://cdn.intechopen.com/books/images_new/11171.jpg"},signatures:"Suchana Akter Jahan and Hazim El-Mounayri"},{id:"1089787",title:"Differences between Universal-Deterministic and Probabilistic Hypotheses in Operations Management Research",slug:null,totalDownloads:4,totalDimensionsCites:0,doi:"10.5992/intechopen.1000218",abstract:'
Very few papers in the operations management (OM) field have taken the themes of universal-deterministic (UD) and probabilistic hypotheses as their main topics of investigation and discussion. Our investigation continues a recent line of research that focuses on a better understanding of these critical issues. Specifically, we attempt to respond to some pointed criticisms that experts in the field have made when the topic UD and probabilistic hypotheses have emerged in academic settings/discussions. A detailed analysis of those criticisms shows that they lack merit, thereby reinforcing our argument that it is most important to distinguish between the two types of scientific hypotheses in order to advance in the rigor of OM theoretical and empirical research. Ideas for future research are outlined.
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In recent years, supply chains in the manufacturing industry have become more and more complicated, and many cases of supply chain disruptions due to natural disasters have been confirmed. It is necessary for manufacturers to build a system that can help them alleviate losses and shorten recovery periods due to supply chain disruptions. Supplier diversification, as well as supplier evaluation and selection, are discussed as risk aversion measures in many papers. However, even if the procurement source has been evaluated enough, there are problems, such as opportunity loss during recovery periods and soaring procurement costs during normal periods. In this chapter, to help Japanese manufacturers to alleviate opportunity loss under component procurement disruption situations and keep cost competitiveness in normal periods, decision-making models of supply chain structure assessment, supplier selection, procurement allocation, and trading contracts are designed and verified.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Kotomichi Matsuno, Jiahua Weng, Noriyuki Hosokawa and Takahiro Ohno"},{id:"1085055",title:"Performance Measurement Using Deterministic and Stochastic Multiplicative Directional Distance Functions",slug:null,totalDownloads:5,totalDimensionsCites:0,doi:"10.5992/intechopen.1000179",abstract:'
Performance measurement is essential for fostering continuous improvement of the production and operation management in a firm or organization. We consider a deterministic scenario based on a flexible structure of production technology and establish a multiplicative relationship between the generalized multiplicative directional distance function (GMDDF) and geometric distance function (GDF). We also introduce a stochastic multiplicative directional distance function (SMDDF). Based on a stochastic scenario, the SMDDF can be estimated by the method of convex nonparametric least squares. As an illustrative application, we investigate the productive performance of Japanese life insurance companies using a panel dataset spanning 2016 to 2020.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Yu Zhao"},{id:"1085559",title:"Assessment of Medical Equipment Maintenance Management",slug:null,totalDownloads:20,totalDimensionsCites:0,doi:"10.5992/intechopen.1000210",abstract:'
Today's modern hospital is highly dependent on different types of medical equipment to help diagnose, monitor, and treat patients. Medical equipment maintenance is important to reduce costs, reduce patient dissatisfaction, treat the patient in a timely manner, and reduce mortality and risks during patient care. Good maintenance management is important to have well-planned and implemented programs through which hospitals can minimize medical device failures or other problems with the operation of medical equipment. Medical equipment plays an important role in the hospital system; therefore, the acquisition, maintenance, and replacement of medical equipment are key factors in hospitals for the implementation of the health service. Thus, in order to ensure the quality of medical devices for the provision of medical care, it is imperative to evaluate the safety of using hospital maintenance management. In order to achieve these goals, hospitals must develop checklists that identify the state of performance of medical equipment maintenance. It is essential for clinical managers and engineers not only to increase the capacity of the hospital but also to predict the risks of sudden failure. Given the lack of unique and comprehensive maintenance management checklists, the current goal is to design and develop medical equipment maintenance management checklists.
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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. 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