*3.3. FSST*

There were no significant differences between FSST values baseline pre-climb day 1 (3.62 s ± 0.75) and post-startle day 2 (3.09 ± 0.62). There were significant differences (*p* = 0.006) between FSST scores baseline post-climb day 1 (3.45 s ± 0.61) and post-startle day 2 (3.09 s ± 0.62), and significant differences (*p* = 0.002) between males' baseline postclimb day 1 (3.42 s ± 0.53) and post-fatigue day 2 (2.84 s ± 0.43) scores. Results are shown in Tables 3–5.

**Table 3.** Summary of results for males and females. Significant differences (*p* = 0.003) in somatic anxiety post-startle between males and females. Significant differences (*p* = 0.019) between male and female self-confidence values pre-startle and post-fatigue (*p* = 0.02). Significant differences (*p* = 0.006) in FSST times between baseline post-climb and post-startle. Significant differences (*p* = 0.012) between baseline pre-climb left-hand grip strength and post-fatigue left-hand grip strength.

**Table 4.** Summary results for males. Significant differences (*p* = 0.019) in somatic anxiety pre- and post-startle in males. Significant differences (*p* = 0.002) between FSST baseline post-climb and post-fatigue scores. Significant differences (*p* = 0.035) between baseline pre-climb left-hand grip strength and post-fatigue left-hand grip strength. Significant differences (*p* = 0.00) between pre-startle and post-startle right-hand grip strength.

**Table 5.** Summary results for females.

#### *3.4. Anxiety and Self-Confidence*

There were significant differences (*p* = 0.019) in somatic anxiety pre-startle (14.23 ± 5.09) and post-startle (16.36 ± 5.54) in males, as well as significant differences (*p* = 0.035) between male and female self-confidence levels pre-startle (35.08 ± 4.94, 30.00 ± 5.29). There were also significant differences (*p* = 0.022) in self-confidence post-fatigue between sexes (34.43 ± 5.88, 29.00 ± 4.76). Results are shown in Tables 3–5.

#### *3.5. Grip Strength*

There were significant differences (*p* = 0.012) between baseline left-hand grip strength (48.60 kg ± 9.36) and post-fatigue left-hand grip strength (36.46 kg ±8.63). Males had significant differences (*p* = 0.035) between baseline left-hand grip strength (52.27 kg ± 6.94) and post-fatigue left-hand grip strength (35.35 ± 14.74). There were differences in female baseline left-hand grip strength (38.50 kg ± 7.94) and post-fatigue left-hand grip strength (29.25 kg ± 2.36), although not significant (*p =* 0.058). Males also had significant (*p =* 0.00) differences in pre-startle (50.55 ± 10.48) and post-startle (51.05 ± 8.11) right–hand grip strength. Results are reported in Tables 3–5.

#### *3.6. Fatigue*

There was an inverse correlation between time to fatigue and body weight (CI = 95%, r = 0.606, *p =* 0.025). There were significant differences (*p =* 0.022) between sexes: males reached muscular failure after 282.39 s ± 48.20, and females after 367.51 s ± 70.21.

#### **4. Discussion**

The results indicate that physical and psychological stress affects males and females in different ways and that cortisol concentrations are strongly affected by time of day. Salivary samples were utilized in this study because since cortisol follows a circadian rhythm, with the highest values occurring 20–40 min after waking, we thought it beneficial to obtain a baseline secretion curve for comparison with the rest of the values [18,19]. Baseline cortisol concentrations followed a normal diurnal pattern, with the highest values occurring at 8:00 a.m.

It is possible that the variations in concentrations between the 8:00 a.m. samples of each day were due to individual error or individual variation in diurnal cortisol slope (DCS). Cortisol concentrations can be easily affected by acute stressors, age, sex, nutrition, sleep, hydration, physical activity, and circadian rhythm [4,9,18]. Salivary composition can also be affected by countless factors, including circadian rhythm, age, sex, smoking, diet, and medications [4]. Since external factors of the participants' day to day were not accounted for, it is possible that variations in these variables altered their DCS. In addition, it is possible that participants did not take the samples at the same time for each of the three baseline days. Although these may have been small variations, it may have been enough to affect the DCS substantially—especially in the waking hours [19,20]. Variations in sampling time may have also been due to difficulty in saliva production. Some participants reported spending 20 min in the PD position to produce sufficient saliva. This may have further delayed the time of day that the sample was obtained, thus influencing cortisol levels. This may have also been a factor on day 2, since some participants took substantially longer to produce enough saliva pre-startle, post-startle, and post-fatigue. Although the pre-startle samples were taken at 8:00 a.m., and the post-startle and post-fatigue samples were taken shortly after, time of day may have profoundly impacted the variance in salivary cortisol levels. Since the fatigue climb was the last test condition, this could explain the decrease in cortisol levels in males and females. Instead of obtaining baseline samples at 8:00 a.m., 11:00 a.m., and 2:00 p.m., perhaps obtaining samples at 8:00 a.m. and 9:00 a.m. would have been a better comparison for this study. Moreover, lead climbing and the auditory stimulus used may not have been strong enough stressors to provoke changes in cortisol levels in males due to their level of experience and more advanced skill level [1].

The 8:00 a.m. pre-startle cortisol levels were higher in both males and females when compared to their respective baseline 8:00 a.m. cortisol levels. This may have been due to an anticipatory cortisol response that primes the central nervous system [21]. This anticipatory response provides some insight into the relationship between psychological stress and physiological responses, as well as highlights the significance of psychobiological processes that occur prior to a stressor. It is possible that this neuroendocrine response was activated when instructions for the startle climb were provided. This would suggest that the stress (and increase in cortisol) that individuals experienced was triggered by their emotional and cognitive representations of what they thought would occur during the climb [21].

Females may have experienced a peak in cortisol levels post-startle because the relative difficulty of the climb may have been higher for them. Female participants were not as comfortable with the lead climbing technique, and this lack of confidence, in addition to the sustained isometric contractions and increasing difficulty of the climb, may have contributed to a peak in cortisol levels post-startle. This is supported by the significant differences in self-confidence between males and females prior to the startle climb.

Increased somatic anxiety post-startle in males may have been due to the added stress of lead climbing. Other studies have had similar findings, noting that participants had increased somatic anxiety when they had to lead climb a route, compared to top rope climbing [2]. Sex differences in somatic anxiety post-startle may be an indicator of differences in male and female responses to stress. There is evidence from functional magnetic resonance imaging (fMRI) that women are more attuned to negative stimuli and that they respond more rapidly to negative stimuli [22]. These sex differences may also explain differences in the self-reported self-confidence post-fatigue climb.

FSST times may have been faster post-startle because of heightened somatic anxiety and focus, due to the fight-or-flight response. It is also possible that the results were influenced by test familiarization and decreased anxiety of social judgment. In day 1, the FSST trials were carried out when the rock-climbing gym was open to the public. Therefore, there were other climbers present that served as an "audience" to the participants in the study. The participants may have also had difficulty focusing on the task at hand because of the various distractions in the gym. The fact that the participants did not know what to expect, that it was their first time performing the FSST, that there was an audience, and that their focus could have been affected, may have all contributed to slower day 1 scores.

It could be that average FSST times did not decrease post-fatigue because the value that was used to indicate fatigue was forearm muscle failure. It may be that although the forearm musculature fatigued to failure, focus and lower limb coordination did not decline. It is also worth noting that three participants were not able to complete the test post-fatigue, due to poor coordination and unsuccessful execution of the sequence.

#### **5. Conclusions**

Our results show that cortisol concentrations follow a normal standard curve, irrespective of the test condition. Cortisol samples were taken 15 min after the stressor, and values were lower post-startle and post-fatigue when compared to the pre-test. It may be that the stressors used in this study were not enough to provoke a stressful situation in the climbers of this study or that higher values were presented immediately after the climb and not 15 min later. Future studies should compare the cortisol response immediately after the stimulus, as well as 15 min later, to determine when the true peak in cortisol occurs. Studies should also look at ways to reduce the amount of time spent in saliva sampling, since extended sampling time may have profoundly affected cortisol levels.

There seem to be differences in the way that males and females psychologically prepare and react to stressful situations. It is difficult to draw conclusions from this sample because a major limitation was the number of participants, especially females. Evidently, there are countless factors that can influence the stress response during climbing, as well as several variables that can serve as indicators of the demand of the climb. Future studies should also take into consideration the biomechanical and strategic changes that occur with increased psychological and physiological stress. This can be done by analyzing video footage and utilizing electromyography (EMG) to determine premotor time and reaction time, as well as changes in muscle activity. Blood samples can also be taken to look at the impact that acute stressors have on biomarkers of oxidative stress, as well as on biomarkers that are suggested to be related to anxiety [4].

**Author Contributions:** Conceptualization, P.V. and C.B.; methodology, P.V., A.I. and C.B.; formal analysis, C.B.; investigation, P.V. and C.B.; resources, C.B.; writing—original draft preparation, P.V.; writing—review and editing, P.V., C.B. and S.S.; visualization, P.V., C.B. and S.S.; supervision, C.B. and A.I.; project administration, C.B. and A.I.; funding acquisition, C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to the observational nature of the study.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank Carles Ros and Carles Tudela and the team at Ingravita for their support and collaboration on the project. The authors would like to extend their gratitude to Albert Pare for his continued assistance and valuable insights during the investigation, to Sergio Vilches for his contribution in the development of the project and for providing resources for saliva analysis, to Jon González for his help with sample analysis, and to all the climbers that participated in the study, for their cooperation and commitment to the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**1. Introduction**

Water-based drill-in fluid (WBDIF) is used to drill the reservoir section, which carries the hydrocarbon. WBDIF should be designed to be non-damaging by building an impermeable layer on the face of the formation during the drilling process while this layer should be removed easily before casing and cementing the hole. WBDIF should provide stable rheological properties (RHPs) in order to provide a stable and clean hole and to prevent cutting accumulation, both of which lead to the possibility of pipe sticking, [1–3]. The main function of WBDIF is to support formation pressure and prevent reservoir fluid from entering the wellbore while drilling [4]. In addition, the drill-in fluid should cool and lubricate the bit and the drill string [5,6].

Sodium chloride-water-based drill-in fluid (NaCl-WBDIF) is mainly used while drilling the reservoir section. NaCl salt is used as a weighting material to increase mud density, while the Na+ ions work as shale stabilizers [7]. NaCl polymer mud is characterized as an inhibited, non-dispersed drilling fluid in which the viscosity control and the filtration properties are enhanced by using some types of polymers such as xanthan gum and starch. This helps reduce the possibility of formation damage [8].

RHPs play a key role in the success of the drilling operation. RHPs such as plastic viscosity (PV), yield point (YP), apparent viscosity (AV), flow consistency index (K), and flow behavior index (n) should be determined in real-time to calculate rig hydraulics and determine the required pressure to optimize hole cleaning. Increasing the PV value gives an indication about the increase in solid content, and it can highly affect the rate of penetration [9–11]. Paiaman et al. [12] stated that a lower YP value is preferred in turbulent flow, while a high YP value is required for laminar flow. The ratio of YP/PV is very important for hole cleaning. The YP/PV ratio should be greater than 1.5 [13]. The consistency index of the drilling fluid (k) is the main controlling parameter of the carrying capacity index (CCI) [14].

The common procedure in rig-sites is that the drilling crew usually measures the mud density and Marsh funnel time [15] every 15–20 min, and these measurements are used as indicators for the changes in the fluid properties [16]. Other RHPs (PV, YP, n, and K) are usually measured twice a day, as it requires a long time to heat the fluid, record, analyze the data, and clean the equipment. This process is tedious and time-consuming.

The effect of solid content on drilling fluid RHPs is very obvious, as can be seen throughout the literature. The objective of this study was to develop novel empirical models that are capable of acquiring the RHPs of NaCl polymer mud using one of the powerful artificial intelligence techniques—the artificial neural network (ANN)—using 900 field measurements of mud density (MD), Marsh funnel time (FT), and solid percent (SP). The novelty of this research is getting a real-time prediction (every 10–15 min) of the mud RHPs. Additionally, in this paper, a self-adaptive evolution algorithm was used to optimize the input parameters at the same time, and this was linked with the ANN model. The developed method depends on taking the reading from the automated Marsh funnel system (which contains different sensors) and applying artificial neural network models to predict the rheological properties every 10–20 min, which enable the driller to understand the changes of the drilling fluid properties as well as changes in the rig hydraulics. This make decisions regarding the required action based on given information much faster.

#### *Artificial Neural Network*

The concept of artificial networks was introduced into engineering research in the 1940s [17,18]. At early stages, artificial intelligence (AI) was used to solve the complex equations and mimic the nervous system [19,20].

The ANN has been considered as an effective AI tool; therefore, it has been widely applied in several fields such as classification and optimization tasks [21,22]. The ANN model is a system of neurons and hidden layers [23]. Usually, the whole data are grouped into two sets—training and testing data sets. The training group is used to train the network and capture the relationship between the input and output parameters, while the testing data are used to measure the reliability of the developed ANN system. During the training stage, the testing data remain unseen by the model, which provides more confidence regarding model reliability [24–26].

Alajmi et al. [27] predicted choke performance using an ANN. Alarifi et al. [28] estimated the productivity index for oil horizontal wells using an ANN, a functional network and fuzzy logic. Chen et al. [29] applied a NN and fuzzy logic to evaluate the performance of an inflow control device (ICD) in a horizontal well. Their model investigated the influences of reservoir parameters (such as reservoir size, thickness, reservoir heterogeneity, and permeability ratio) on ICD completion performance. Van and Chon [30,31] evaluated the performance of carbon dioxide (CO2) flooding using

ANN techniques. They developed ANN models for determining oil production rate, CO2 production, and gas-oil ratio (GOR).

The self-adaptive differential evolution (SaDE) was introduced by Qin et al. [32] to overcome the common issues of the differential evaluation (DE) [33]. The advantage of the SaDE is the ability to self-adapt the controlling parameters and mutation strategies based on the learning experience in the previous algorithm generations to obtain better results. Moussa and Awotunde [34] developed a modified SaDE that can be used for the optimization in different engineering problems.

Al-Khdheeawi and Mahdi [35] applied an ANN to predict the apparent viscosity of water-based drilling fluid using the mud density and Marsh funnel time. They concluded that the developed ANN correlation could predict AV with an average absolute percentage error (AAPE) of 8.6% and a correlation coefficient of 98.8%. Gowida et al. [36] stated that the ANN can be used efficiently to predict the rheological properties of the calcium chloride (CaCl2) water-based drilling fluid based on mud density and Marsh funnel time.

Zhang et al. [37] developed a new technique for breast cancer detection using a combination of rectified linear unit and rank-based stochastic pooling. They concluded that the detection efficiency of the new technique overcomes the known six standard techniques known for breast cancer detection. For abnormal breasts in mammogram images, Wang et al. [38] developed a combined system of a feed-forward neural network with principal computer analysis, a Jaya algorithm, and a weighted-type fractional Fourier transform. They concluded that Jaya was a better algorithm for training the feed-forward neural network than the common know algorithms, and the developed technique was able to detect the abnormal breast with high accuracy (>92.27%).

The main goal of this study was to develop new sets of empirical correlations, optimized using modified self-adaptive differential evolution (MSaDE), to determine the RHPs of NaCl-WBDIF using a hybrid ANN model.

#### **2. Methodology**

ANN variables such as percent of training to testing, number of neurons, training and testing functions, and the number of layers should be optimized to develop a robust ANN model, and from this model, empirical correlation can be extracted. In this study, MSaDE was applied to optimize the variable parameters of the ANN for different RHPs. Nine-hundred field measurements were used to train, test, and validate the ANN models. The data were selected randomly to train the model, with 65% of the data being used for training (570 data points), 23% of the data being used for testing (180 data points), and 12% of the data being used (150 data points) for further validation. The ANN models were built using 88% of the available data, including training and testing and based on the optimized models, and the new empirical correlation was developed. The 12% remaining of data were used to validate the developed empirical correlations. The correlation coefficient ®, AAPE, and visualization check were used as criteria to evaluate the developed models and correlations.

The AAPE is a measure of the relative deviation of the predicted data from the real data and can be calculated using Equation (1):

$$\text{AAPE} = \frac{1}{\text{n}} \sum\_{i=1}^{\text{n}} |\mathbf{E}\_i| \tag{1}$$

where n is the number of data points and Ei is the relative deviation of a predicted value from a real value, Equation (2);

$$\mathbf{E}\_1 = \left(\frac{\mathbf{y}\_{\text{real}} - \mathbf{y}\_{\text{predick}}}{\mathbf{y}\_{\text{real}}}\right) \ast 100\tag{2}$$

For the network training and transferring functions, two pools of 12 different training functions and 7 transferring functions were established, respectively. Each individual training/transferring function was indexed and used as one of the input parameters to the MSaDE optimization algorithm along with the other parameters such as the number of neurons, the percent of training to testing, and the number of hidden layers. Twenty independent optimization runs were performed to optimize the above-mentioned parameters. The optimization run that resulted best fit (in terms of the highest R and the lowest AAPE was considered as the best run, and its results are shown and discussed in this paper.


Meet the editor

Reccab Manyala is Professor of Physics at the University of Zambia where he lectures in physics and carries out research and consultancy in new renewable energy and renewable energy technologies. He obtained his PhD in Renewable Energy and Renewable Energy Technologies from Maseno University (Kenya) in 2009. Prof. Manyala holds a Bachelor's degree in Education Science (Physics and Mathematics) and a Master's degree in The-

oretical Physics (Plasma Physics and Mathematical Physics) from the University of Nairobi (Kenya), as well as a Master's degree in Experimental Physics (Condensed Matter Physics) from the University of Arkansas (USA). He has published extensively and has research and teaching experience in many areas of physics spanning over 30 years in Kenya and abroad. Prof. Manyala has edited two books for IntechOpen and this is his third.

Contents

**Preface III**

**Chapter 1 1**

**Chapter 2 13**

**Chapter 3 33** Energy Harvester Based on Magnetomechanical Effect as a Power Source for

**Chapter 4 63**

**Chapter 5 95** RF Energy Harvesting System and Circuits for Charging of Wireless Devices

**Chapter 6 111**

Thousand kW High-Temperature Solar Furnace in Parkent (Uzbekistan) –

*by Sajid Sajid, Jun Ji, Haoran Jiang, Xin Liu, Mingjun Duan, Dong Wei, Peng Cui, Hao Huang, Shangyi Dou, Lihua Chu, Yingfeng Li, Bing Jiang and Meicheng Li*

Quest for Lead-Free Perovskite-Based Solar Cells

*by Jerzy Kaleta, Rafał Mech and Przemysław Wiewiórski*

Small-Scale Energy Harvesting from Environment by Triboelectric

Thermoelectric Energy Harvesting

Multi-node Wireless Network

*by Jie Wang, Linglin Zhou, Chunlei Zhang*

*by Naser Ahmadi Moghaddam and Alireza Maleki*

*by Chris Gould*

Nanogenerators

*and Zhong Lin Wang*

Using Spectrum Sensing

Energetical Characteristics

*by Akbarov Rasul*


*2.6. Sensitivity Analyses*

Alternate multivariate models were used to examine sensitivity of our results to model specification. Additional covariate specifications were examined including with education as single indicator of socioeconomic status, ethnicity (White, all others) and rurality (Urban or Rural), and the removal of proportions of restaurants, fast food outlets and cafés.


**Author details**

Baoxing Chen1 \* and Sombel Diaham2,3

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**7. Conclusions and perspectives**

Biofloc technology will enable aquaculture grow toward an environmentally friendly approach and biosecurity. Consumption of microorganisms in BFT reduces FCR and consequently costs in feed. Also, microbial community is able to rapidly utilize dissolved nitrogen leached from shrimp/fish feces and uneaten food and convert it into microbial protein, maintaining the water quality. The physical, chemical, and biological interactions that occur into the biofloc systems are complex; further studies can elucidate specific phenomena and their possible applications to other biotechnological fields.


*Difficult Airway in Obstetric Patients DOI: http://dx.doi.org/10.5772/intechopen.108341*

airway scope videolaryngoscope following failed direct laryngoscopy in a morbidly obese parturient. International Journal of Obstetric Anesthesia. 2011;**20**(2):200-201

[90] Tonidandel A, Booth J, D'angelo R, Harris L, Tonidandel S. Anesthetic and obstetric outcomes in morbidly obese parturients: A 20-year follow-up retrospective cohort study. International Journal of Obstetric Anesthesia. 2014;**23**(4):357-364

[91] Benumof JL, Cooper SD. Quantitative improvement in laryngoscopic view by optimal external laryngeal manipulation. Journal of Clinical Anesthesia. 1996;**8**(2):136-140

[92] Latto IP, Stacey M, Mecklenburgh J, Vaughan RS. Survey of the use of the gum elastic bougie in clinical practice. Anaesthesia. 2002;**57**(4):379-384

[93] Hodzovic I, Latto IP, Henderson JJ. Bougie trauma--What trauma? Anaesthesia. 2003;**58**(2):192-193

[94] Kadry M, Popat M. Pharyngeal wall perforation—An unusual complication of blind intubation with a gum elastic bougic. Anaesthesia. 1999;**54**(4):404-405

[95] Arndt GA, Cambray AJ, Tomasson J. Intubation bougie dissection of tracheal mucosa and intratracheal airway obstruction. Anesthesia & Analgesia. 2008;**107**(2):603-604

[96] Pandit JJ, Andrade J, Bogod DG, Hitchman JM, Jonker WR, Lucas N, et al. 5th National Audit Project (NAP5) on accidental awareness during general anaesthesia: Summary of main findings and risk factors. British journal of anaesthesia. 2014;**69**:1089-1101

[97] Odor PM, Bampoe S, Moonesinghe SR, Andrade J, Pandit JJ, Lucas DN. Pan-London perioperative audit and research network (PLAN), for the DREAMY investigators group, A'Court A, Abdel-Gadir D, Abdu A, Abisogun C. General anaesthetic and airway management practice for obstetric surgery in England: A prospective, multicentre observational study. Anaesthesia. 2021;**76**(4):460-471

[98] Sakles JC, Patanwala AE, Mosier JM, Dicken JM. Comparison of video laryngoscopy to direct laryngoscopy for intubation of patients with difficult airway characteristics in the emergency department. Internal and Emergency Medicine. 2014;**9**(1):93-98

[99] Mosier JM, Stolz U, Chiu S, Sakles JC. Difficult airway management in the emergency department: GlideScope videolaryngoscopy compared to direct laryngoscopy. The Journal of Emergency Medicine. 2012;**42**(6):629-634

[100] Park R, Peyton JM, Fiadjoe JE, Hunyady AI, Kimball T, Zurakowski D, et al. The efficacy of GlideScope® videolaryngoscopy compared with direct laryngoscopy in children who are difficult to intubate: An analysis from the paediatric difficult intubation registry. BJA: British Journal of Anaesthesia. 2017;**119**(5):984-992

[101] Dillon JK, Christensen B, Fairbanks T, Jurkovich G, Moe KS. The emergent surgical airway: Cricothyrotomy vs tracheotomy. International Journal of Oral and Maxillofacial Surgery. 2013;**42**(2):204-208

[102] Kristensen MS, Teoh WH, Baker PA. Percutaneous emergency airway access; prevention, preparation, technique and training. British Journal of Anaesthesia. 2015;**114**(3):357-361

[103] Royal College of Obstetricians & Gynaecologists. Maternal collapse in pregnancy and the puerperium. Green Top Guideline. 2011;(56):2-6

[104] Law JA, Duggan LV, Asselin M, Baker P, Crosby E, Downey A, et al. Canadian airway focus group updated consensus-based recommendations for management of the difficult airway: Part 2. Planning and implementing safe management of the patient with an anticipated difficult airway. Canadian Journal of Anesthesia/Journal canadien d'anesthésie. 2021;**68**(9):1405-1436

[105] McDonnell NJ, Paech MJ, Clavisi OM, Scott KL, ANZCA Trials Group. Difficult and failed intubation in obstetric anaesthesia: An observational study of airway management and complications associated with general anaesthesia for caesarean section. International journal of obstetric anesthesia. 2008;**17**(4):292-297

[106] Malik D, Mushambi M, Ali P, Dravid MR. Tracheal extubation practices amongst members of the difficult airway society: A national survey. Anaesthesia. 2013;**68**(6):656-656 111 River St, Hoboken 07030-5774, NJ, USA: Wiley-Blackwell

*Edited by Jessica A. Lovich-Sapola, Kelly Lebak, Michael D. Bassett and Kasia P. Rubin*

Tracheal intubation is a common medical procedure. It is relatively simple to accomplish, although there are cases in which tube placement is difficult or even impossible. This book provides a comprehensive overview of endotracheal tube placement with chapters on advanced techniques, ultrasound use for airway management, airway management techniques for obstetric patients, and much more.

Published in London, UK © 2023 IntechOpen © chaikom / iStock

Advances in Tracheal Intubation

Advances in Tracheal

Intubation

*Edited by Jessica A. Lovich-Sapola, Kelly Lebak, Michael D. Bassett*

*and Kasia P. Rubin*