Experimental results obtained by AlexNet-based FCN.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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For this reason, noise control plays an increasingly central role in the development of modern industrial and engineering applications. Nowadays, the noise control problem excites and attracts the attention of a great number of scientists in different disciplines. Indeed, noise control has a wide variety of applications in manufacturing, industrial operations, and consumer products. The main purpose of this book, organized in 13 chapters, is to present a comprehensive overview of recent advances in noise control and its applications in different research fields. The authors provide a range of practical applications of current and past noise control strategies in different real engineering problems. It is well addressed to researchers and engineers who have specific knowledge in acoustic problems. 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Until 2001, she was a researcher in the Acoustic and Vibration Department at C.I.R.A. (Italian Aerospace Research Center). From 2001 until now, she has been a researcher at the National Research Council of Italy (CNR) in the field of acoustic and vibration in the transport field. She is responsible for the Acoustic and Vibration Laboratory. She is qualified to University Associate Professor – Sector Machines and Systems for Energy and Environment, and is also qualified in the Engineer Profession. She had a scholarship granted by M.A.R.S.- Microgravity Advanced Research and Support - Dornier GmbH (Friedrichshafen - Germany), Euromaster in \\"Total Quality Management\\" at DIMP – Department of Mechanical and Production Engineering – University of Naples Federico II – Italy, as well as assistant in “Fluid Machines I”, “Fluid Machines II”, and “Applied Mechanics”. She is the tutor of more than 30 Master\\\'s students and 7 Ph.D. students. 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\r\n\tConventional drug administration has several issues and challenges such as full doses absorption and efficient targeting, some produce undesirable secondary effects and promote damage to organs such as the liver and kidneys, others trigger inflammation and immune response. Hence, drug carriers help to promote drug absorption, enhance targeting, and avoid or decrease secondary effects, others possess the ability to camouflage the drug from immune cells and proteins. Moreover, permit controlled release which provides prolonged delivery of a drug while maintaining its blood concentration within therapeutic limits.
\r\n\r\n\tThis book project will discuss different novel and traditional strategies to create and characterize systems to be used as drug carriers from the use of nanotechnology through the use of viruses and living organisms.
\r\n\r\n\tBook chapters aim to clearly describe, discuss in detail, compare and review the state of the art in drug carriers. physicochemical, mechanical characterization, tissue, and immune response to the drug carrier are also welcome.
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The introduction of new materials, sensors, actuators, software, communications and use scenarios converted Robotics in a pushing area that embraces our everyday life. New robotic morphologies are the most shocking aspect that society perceives (i.e., the first models of each type generally produce the largest impact), but the long-term success of robotics is found in its capability to automate productive processes. Manufacturers and developers know that the market is found not only in large-scale companies (car manufacturers and electronics mainly) but also in the SME that provides solutions to problems that are manually performed so far. Also, robotics has opened the doors to new applications that did not exist some years ago and are also attractive to investors. These facts, together with lower prices for equipment, better programming and communication tools, and new fast-growing user-friendly collaborative robotic frameworks, have pushed robotics technology at the edge in many areas.
It is clear that industrial robotics leads the market worldwide, but social/gaming uses of robots have increased sales. Nevertheless, the most promising scenario for the present time and short term is the use of robots in commercial applications out of the plant floor. Emergency systems, inspection, and maintenance of facilities of any kind, rescues, surveillance, agriculture, fishing, border patrolling, and many other applications (without military use) attract users/clients because their use increases the productivity of the different sectors, low prices and high profitability are the keys.
There exist many robot morphologies and types (surface, underwater, aerial, underground, legged, wheels, caterpillar, etc.) but authors want to draw attention in the unmanned aerial vehicles (UAVs), which have several properties that make them attractive for a set of application that cannot be done with any other type of robot. First, those autonomous robots can fly, and therefore, they can reach areas that humans or other robots cannot. They are light, easy to move from one area to another, and can be adapted to any area, terrain, soil, building, or facility. The drawback is the fragility in front of adverse meteorological events, and their autonomy is quite limited compared with unmanned surface vehicles (USVs).
UAVs have seen the birth of a new era of unthinkable cheap, easy applications up to now. The authors would like to focus its use in the maintenance and inspection of industrial facilities, but specifically in the inspection of pipes in big, complex factories (mainly gas and oil companies) where the manual inspection (and even location and mapping) of pipes becomes an impossible task. Manned helicopters (with thermal engines) cannot fly close to pipes or even among a bunch of pipes. Scaffolds cannot be put up in complex, unstable, and fragile pipes to manually inspect them. Therefore, a complex problem can be solved through the use of UAVs for inspecting pipes of different diameters, colors, textures, and conditions in hazardous factories. This problem is not new and some solutions have been brought to an incipient market. Works as those in [1, 2] propose the creation of a map of the pipe set navigating among it with odometry and inertial units [3]. Obstacle avoidance in a crowded 3D world of pipes becomes of great interest when planning a flight; in [4], some contributions are made in this direction although the accuracy of object is deficient to be a reliable technology. Work in [5] overcomes some of the latter problems with the use of a big range of sensors, cameras, laser, barometer, ultrasound, and a computationally inefficient software scheme made the UAV too heavy and unreliable due to the excessive sensor fusion approach.
Many of the technical developments that have helped robotics grow have had a wider impact, especially those related with increasing computational power and parallelization levels. Faster processors, with tens of cores and additional multiple threat capabilities, and modern GPUs (graphics processing unit) have led to the emergence of GPGPU (general-purpose computing on GPU). These type of computing techniques have led to huge advances in the artificial intelligence (AI) field, producing the emergence of the “deep learning” field. The deep learning (DL) field is focused in using artificial neural networks (ANNs) that present tens or hundreds of layers, exploiting the huge parallelization capabilities of modern GPU. This is used in exploiting computational cores (e.g., CUDA cores), which compared on a one-to-one basis with a processor core, they are less powerful and slower, but can be found in amounts of hundreds or thousands. This has allowed the transition from shallow ANN to the deeper architectures and innovations such as several types of convolutional layers. In this work, the authors present a novel approach to detect pipes in industrial environments based in fully convolutional networks (FCNs). These will be used to extract the apparent contour of the pipes, replacing most of the architecture developed in [6] and discussed in Section 2. To properly train these networks, a custom dataset relevant to the domain is required, so the authors captured a dataset and developed an automatic label generation procedure base in previous works. Two different state-of-the-art semantic segmentation approaches were trained and evaluated with the standard metrics to prove the validity of the whole approach. Thus, in the following section, some generalities about the pipe detection and positioning problem are discussed, and the authors’ previous work [6] on it, as it will be relevant later. The next section discusses the semantic segmentation problem as a way to extract the apparent contour, both surveying classical methods, considered for earlier works, and state of the art deep-learning-based methodologies. The fourth section describes how the automatic label generator using multimodal data was derived and some features to the process. The experimental section starts discussing the metrics employed to validate the results, the particularities of the domain dataset generated and describes how an AlexNet FCN architecture was trained through transfer learning and the results achieved. To conclude, some discussion on the quality of the results and possible enhancements is introduced, discussing which would be the best strategies to follow continuing this research.
As it has been discussed, inspection and surveying are a frequent problem where UAV technologies are applied. The most common scenario found is that of a hard to reach infrastructure that is visually inspected through different sensors onboard a piloted UAV. Some projects have proposed the introduction of higher level perception and automation capacities, depending on the specific problem. In these cases, it is common to join state-of-the-art academic and industrial expertise to reach functional solutions.
In one of these projects, the specific challenge of accurately detecting and positioning a pipe in real time using only the hardware deployable in a small (per industry standards) UAV platform was considered (Figure 1), with several solutions studied and tested (including vision- and LIDAR-based techniques).
One of the UAV used for the development of perception tasks in the AEROARMS project. Several sensors were deployed, processing them with a set of SBCs (single-board computers), including a Velodyne LiDAR, two different cameras, ultrasonic range-finder (height), and optical flow.
In the case of LIDAR-based detection, finding a pipe is generally treated as a segmentation problem in the sensor space (using R3 data collected as “
With these results, discussed in [6], it was apparent that a new solution was needed, as the LiDAR approaches were too slow and the vision-based techniques probed themselves unreliable. The final proposed solution was based on integrating data from the laser and the vision sensors: the RANSAC over LiDAR approach would detect robustly the pipe and provide an initial position, which would then be projected into the image space (accounting for displacements if odometry is available) and used as a seed for the vision-based pipeline described.
In that same work [6], a sensibility analysis studying the effects of the relative pose between the sensor and pipes is provided. Once the pipe is detected in the LiDAR’s space sensor, the cylinder model is projected into the R2 image space using a projection matrix derived from the calibrated camera model (assumed to be a thin lens pinhole model, per classic literature [13]). This provides a region or band of interest where to look for the edges of the pipe in the image and is useful to solve the degenerate conic equation up to scale (i.e., being a function of the radius). An updated architecture version of the process is depicted in Figure 2.
The architecture of the multimodal perception pipeline combining LiDAR and camera vision. An updated version adds to previous works a validation step using odometric measurements.
The detailed architecture of the multimodal approach reveals how the LiDAR-based pipeline minimizes the data dimensionality by filtering non-curved surfaces (i.e., remove walls, floor, etc.) and also by removing entirely regions of the sensed space if priors or relevant data or the expected relative position of the pipe to the sensor is available. This was aimed at minimizing the size of the point cloud to be processed by the RANSAC step. To be able to project the detected pipe from the LiDAR sensor space into the camera image, some additional information was required: the rigid transformation between sensors (i.e., the calibration between LiDAR and camera) and an estimation of the odometry of the UAV. This is due because, even in the best assumption, with a performance slightly over 4 Hz, the delay between the captured point cloud and the produced estimation of the pipe would be over 200 ms. Therefore, the projection of the detected pipe to predict the area of interest to search the apparent contour has to consider the displacement during this period, not only the rigid LiDAR to camera transformation. This predicted region of interest is used in the vision process pipeline, with predictions of the appearance of the pipes into image space used to refine the contour search. This contour search relies on stacking a Hough transform to join line segment detector (LSD) detected segments (to overcome partial obstructions) on the relevant area and allows to choose the nearest correctly aligned lines. Notice that using a visual servoing library [14], an option to use data provided through human interaction was kept as available, though the integration of LiDAR detections as seeds into the visual pipeline made it unnecessary. To avoid degenerate or spurious solutions, a validation step (based on reprojection and “matching” of the Plückerian coordinates [15] for a tracked piped) was later introduced.
This architecture leads to a fast (limited by the performance of the vision-based part) and robust (based on the RANSAC resilience to spurious detections) pipe detector with great accuracy, which was deployed and test in a UAV. The main issue of the approach is the hardware requirements: access to odometry from the avionics systems, LiDAR, and camera sensors, and enough computing power to process them (beyond any other task required from the UAV). All this hardware is focused on solving what can be described as a semantic segmentation problem. This is relevant given the enormous changes produced in the last decade in the computer vision field, and how classic problems like semantic segmentation are currently solved.
In the context of computer vision, the semantic segmentation problem is used to determine which regions of an image present an object of a given category, that is, a class or label is assigned to a given area (be it a pixel, window, or segmented region). The different granularity accepted is produced by how the technique and its solution evolved: for a long time, it was completely unfeasible to produce pixel-wise solutions, so images were split according to different procedures, which added a complexity layer to the problem.
Current off-the-shelf technologies have changed the paradigm, as GPUs present huge capabilities in terms of parallelization, while solid-state disks make fast reliable storage cheap. These technical advancements have increased dramatically the performance, complexity, and memory available for data representation, especially for techniques inherently strong in highly parallelized environments. One of the fields where the impact has been more noticeable has been the artificial intelligence community, where the artificial neural network (ANN) has seen a resurgence thanks to the support this kind of hardware provides to otherwise computationally unfeasible techniques. The most impactful development in recent years has been the convolutional neural networks (CNNs), which have become the most popular computer vision approach for several of the classic problem and the default solution for semantic segmentation.
To understand the impact of deep learning into our proposed solution, we will discuss briefly how the classical segmentation pipeline worked and how the modern CNN-based classifier became the modern semantic segmentation techniques.
The classic semantic segmentation pipeline can be split into two generic blocks, namely image processing for feature extraction and feature level classification. The first block generally includes any image preprocessing done and resizing/resampling, splitting the image into the regions/windows, defining the granularity level of the classification, and finally, extracting the features itself. The features can be of any type and frequently the ones feed to the classification modules will be a composition of several individual features from different detectors. The use of different window/region-based approaches helps build up higher level features, and the classification can be refined at later stages with data from adjacent regions.
Notice that this kind of architecture generally relies on classifiers which required very accurate knowledge or a dataset with the classes to learn specified for each input so it can be trained. Figure 3 shows the detection of pipelines in classic semantic segmentation. Notice that to train the classifier, the image mask or classification result becomes also an input for the training process.
Block diagram of a classical architecture approach for semantic segmentation using computer vision.
So, it can be seen that solving the semantic segmentation problem though classic pattern recognition methods requires acute insight into the specifics of the problem domain, as the features to be detected and extracted are built/designed specifically. This implies (as mentioned earlier) working from low-level features and explicitly deriving the higher level features from them is a very complex problem itself, as they are affected by the input characteristics, what is to be found/discriminated, and which techniques will be used in the classification part of the pipeline.
Modern semantic segmentation techniques have organically evolved with the rise of the deep learning field to its current prominence. This evolution can be seen as a refinement in the scale of the inference produced from very coarse (image level probabilistic detection) to very fine (pixel level classification). The earliest ANN examples made probabilistic predictions about the presence of an object of a given class, that is, detection of objects with a probability assigned. The next step, achieved thanks to increased parallelization and network depth, was starting to tackle the localization problem, providing centroids and/or boxes for the detected classes (the use of
The first big break into the classification problem was done by AlexNet [13] in 2012, when it won the ILSVRC challenge, with a score of 84.6% in the top-5 accuracy test, while the next best score was only 73.8% (based on classic techniques). AlexNet has since then become a known standard and a default network architecture to test problems, as it is actually not very deep or complex (see Figure 4). It presents five convolutional layers, with max-pooling after the first two, three fully connected layers, and a ReLU to deal with non-linearities. This clear victory of the CNN-based approaches was validated next year by Oxford’s VGG16 [16], one of the several architectures presented, winning the ILSVRC challenge with a 92.7% score.
Diagram of the AlexNet architecture, showcasing its pioneering use of convolutional layers.
While several other networks have been presented with deeper architecture, relevant development focused on introducing new types of structures into the networks. GoogLeNet [17], the 2014 ILSVRC winner, achieved victory thanks to the novel contribution of the inception module, which validated the concept that the CNN layers of a network could operate in other orders different from the classic sequential approach. Another relevant contribution produced by technology giants was ResNet [18], which scored a win for Microsoft in 2016. The introduction of residual blocks allowed them to increase the depth to 152 layers while keeping initial data meaningful for training the deeper layers. These residual blocks architecture essentially forwards a copy of the received inputs of a layer; thus, later layers received the results and same inputs of prior layers and can learn from the residuals.
More recently, ReNet [19] architecture was used to extend recurrent neural networks (RNNs) to multidimensional inputs.
The jump from the classification problem with some spatial data to pixel level labeling (refining inference from image/region to pixel level) was presented by Long [20], with the fully convolutional network (FCN). The method they proposed was based on using the full classifier (like the ones just discussed) as layers in a convolutional network architecture. FCN architecture, and its derivatives like U-Net [21] are the best solutions to semantic segmentation for most domains. These derivatives may include classic methods, such as DeepLab’s [22] conditional random fields [23], which reinforces the inference from spatially distant dependencies, usually lost due to CNN spatial invariance. The latest promising contributions to the semantic segmentation problem are based on the encoder-decoder architecture, known as autoenconders, like for example SegNet [24].
For the works discussed in this chapter, a FCN16 model with AlexNet as a semantic segmentation model was used. The main innovation introduced by the general FCN was exploiting the classification power via convolution of the common semantic segmentation DL network, but at the same time, reversing the downsampling effect of the convolution operation itself. Taking AlexNet as an example, as seen in Figure 4, convolutional layers apply a filter like operation while reducing the size of the data forwarded to the next layer. This process allows producing more accurate “deep features” but at the same time also removes high-level information describing the spatial relation between the features found. Thus, in order to exploit the features from the deep layers while the keeping information from spatial relation, data from multiple layers has to be fused (with element-wise summation). In order to be able to produce this fusion, data from the deeper layers are upsampled using deconvolution. Notice that data from shallow layers will be coarser but contain more spatial information. Thus, up to three different levels can be processed through FCN, depending on the quantity of layers deconvoluted and fused, as seen in Figure 5.
Detail of the skip architectures (FCN32, FCN16, and FCN8) used to produce results with data from several layers to recover both deep features and spatial information from shallow layers (courtesy of [
More information on the detailed working of the different FCN models can be found in [25]. It is still worth noting that the more shallow layers are fused, the more accurate the model becomes, but according to the literature, the gain from FCN16 to FCN8 is minimal (below 2%).
Classic methods using trained classifiers would pick designed features (based on several metrics and detectors, as discussed earlier) to parametrize a given sample and assign a label. This would allow creating small specific datasets, which could be used to infer the knowledge to create bigger datasets in a posterior step. The high specificity of the features chosen (generally with expert domain knowledge applied implicitly) with respect to the task generally made them unsuitable to export learning to other domains.
By contrast, deep learning offers several transfer learning options. That is, as it was proven by Yosinski [26], trained with a distant domain dataset are generally useful for different domains and usually better than training from an initial random state. Notice that the transferability of features decreases with the difference between the previously trained task and the target one and implies that the network architecture is the same up to the transferred layers at least.
With this concept in mind, we decided to build a dataset to train an outdoor industrial pipe detector with pixel level annotation to be able to determine the position of the pipe. While the ability of transfer learning allows us to skip building a dataset with several tens of thousands of images, and therefore, the authors will work with a few thousand, which were used to fine-tune the network. These orders of magnitude are required as a “shallow” deep network. For instance, the AlexNet already presents 60 million parameters.
Capturing and labeling a dataset is a cumbersome task, so we also set to automatize this task with minimal human supervision/interaction, exploiting the capabilities of the sensing architecture proposed in earlier works described in Section 2.
This framework, see Figure 6, uses the images captured by the UAV camera sensor, the data processed by the localization approach chosen (see Section 2) to obtain the UAV odometry, and pipe detection seeds from the RANSAC technique treating the LiDAR point cloud data. When a pipe (or generally a cylinder) is detected and segmented in the data sensor provided by the LiDAR, this is used to produce a label for the temporally near images, to identify the region of the image (the set of pixels) containing the pipe or cylinder detected and its pose w.r.t. the camera. Notice that even running the perception part, the camera works at a higher rate than the LiDAR, so the full odometric estimation is used to interpolate between pipe detections, to estimate where the label should be projected into the in-between images (just as it was described for the pipe prediction in Section 2).
The framework proposed to automatically produce labeled datasets with the multimodal perception UAV.
This methodology was used to create an initial labeled dataset with actual data captured in real industrial scenarios during test and development flights, as it will be discussed in the next section.
To evaluate the viability of the proposed automated dataset generation methodology, we apply it to capture a dataset and train several semantic segmentation networks with it. To provide some quantitative quality measurement for the solutions produced, we use modified standard metrics for state-of-the-art deep learning, accounting that in our problem we are dealing with only one semantic class:
Notice that usually, besides the PA the mean pixel accuracy (MPA) is provided, but in our case, it reduces to the same value of PA, thus it will not be provided.
An additional metric usually computed along with the MIU is the frequency weighted MIU, which just weighs the average IoU computed at MIU according to the relative frequency of each class. The MIU, in our case, IoU is the most relevant metric and the most widely used when reporting segmentation results (semantic or otherwise).
The system proposed was implemented over the ROS meta-operating system, just as in previous works [6], where the UAV system used to capture the data is described. A set of real flights in simulated industry environments was performed, where flights around a pipe were done. During these flights, averaging ~240 s, an OTS USB camera was used to capture images (at 640 × 480 resolution), achieving an average frame rate of around 17 fps. This translated in around 20,000 raw images captured, including the parts of flight where no industry-like elements are present, thus of limited use.
Notice that as per the method described, the pipe to be found can be only labeled automatically when the LiDAR sensor can detect it; thus, the number of images was further reduced due to the range limitations of the LiDAR scanner. Other factors, such as vibrations and disruptions in the input or results of required perceptual data, further reduced the number of images with accurate labels.
Around ~2100 images were automatically labeled with a mask assigning a ground truth for the pipe in the image. After an initial human inspection of the assigned label, a further ~320 were rejected, obtaining a final set of 1750. The image rejected produced spurious ground truths/masks. Some of them had inconsistent data and the reprojection of the cylinder detected in through RANSAC in LiDAR scans was not properly aligned (error could be produced by spurious interpolation of poses, faulty synchronization data from the sensors, or due to deformation of the UAV frame, as it is impossible for it to be perfectly rigid). Another group presented partial detections (only one of the edges of the pipe is visible in the image), thus making it useless for the apparent contour optimization. A third type of error found was produced by the vision-based pipeline, where a spurious mask was generated, commonly some shadows/textures displace/retort the edge, or areas not pertaining to the pipe are assigned due similarity of the texture and complexity of delimiting the areas.
A sample of the labeling process can be seen in Figure 7, with the original image, the segmented pipe image, and approximations to centroid and bounding bow.
Left: dataset image. Middle: bounding box and centroid of the region detected. Right: segmentation mask image.
Out of the several options available to test the validity of the dataset produced, the shallow architecture AlexNet was selected, as it could be easily trained and it would provide some insight in the performance that could be realistically expected from a CNN-based approach deployed in the limited hardware of a UAV.
According to previous literature, the dataset was divided into training, validation, and test at the standard ratio of 70, 15, and 15% respectively.
To match the input of AlexNet the images were resized to 256 × 256 resolution. This was mainly done to reduce the computational load, as the input size could be easily fit adjusting some parameters, like the stride. To train and test the network, the Pytorch library was used, which provides full support for its own implementation of AlexNet.
To produce some metrics relevant to the network architecture just trained, a modified version of the technique used to label the dataset was used. Note that this approach, as described in previous sections, uses LiDAR, cameras, and odometry to: acquire an initial robust detection (from LiDAR), track its projection and predict it in the camera image space (using odometric data), and finally determine its edges/contour in the image. The robustness of the LiDAR detection is mainly due to exploiting prior knowledge (in the form of the known radius of the pipe to detect) that cannot be introduced into the AlexNet architecture to produce a meaningful comparison. So, a modified method, referred to as NPMD (no-priors multimodal detector) was employed to estimate the accuracy of earlier work detector without priors. The main difference was modifying the LiDAR pipeline to be able to detect several pipes with different radius (as it should be considered unknown). This led to the appearance of false positives and spurious measurements, which in turn weakened the results produced by the segmentation part of the visual pipeline.
Thus, FCN with AlexNet classification was trained using a pre-trained model for AlexNet, with the standard stochastic gradient descend (SGD) with a momentum of 0.9. A learning rate of 10−3 was used, according to known literature, with image batches of 20. The weight decay and bias learning rate were set to standard values of 5.10−4 and 2, respectively. Without any prior data, and no benefit to obtain by doing otherwise reported in any previous works, the classifier layer was set to 0, and the dropout layer in the AlexNet left unmodified. This trained model produced the results found in Table 1.
AlexNetFCN | UPMD | |
---|---|---|
PA | 73.4 | 56.7 |
IoU | 58.6 | 42.1 |
Experimental results obtained by AlexNet-based FCN.
It can be seen that eliminating the seed/prior data from the multimodal detector made it rather weak, with very low values for IoU, signaling the presence of spurious detections and probably fake positives. The FCN-based solution was around 1.5 times better segmenting the pipe, being a clear winner. This was to be expected as we deliberately removed one of the key factors contributing to the LiDAR-based RANSAC detection robustness, the radius priors, leading to the appearance of spurious detections.
It is worth noting that although the results are not that strong in terms of metrics achieved for a single-class case, there are no other vision-only pipe detectors with better results in the literature, neither other approaches actually tested in real UAV’s platforms, like authors’ previous works [6].
The field of computer vision has been greatly impacted by the advances in deep learning that have emerged in the last decade. This has allowed solving, with purely vision-based approaches, some problems that were considered unsolvable under this restriction. In the case presented, a detection and positioning problem, solved with limited hardware resources (onboard a UAV) in an industry-like uncontrolled scenario through a multimodal approach, has been solved with a vision-only approach. The previous multimodal approach relied in LiDAR, cameras, and odometric measurements (mainly from GPS and IMU) to extract data with complex algorithms like RANSAC and combine them to predict the position of a pipe and produce a measurement. This system was notable thanks to its robustness and performance but presented the huge requirements detailed in [6]. In order to solve the problem in a simpler and more affordable manner, a pure visual solution was chosen as the way to go, exploring the deep learning opportunities.
Although the switch to a pure visual solution meant that during its use, the procedure would only use the camera sensor, the multimodal approach was still used to capture data, and through a series of modifications, turn it into an automatic labeling tool. This allowed building a small but complete dataset with fully labeled images relevant to the problem that we were trying to solve. Finally, to test this dataset, we train a DL architecture able to solve the semantic segmentation problem. Thus, three different contributions have been discussed in this chapter: firstly, a dataset generator exploiting multimodal data captured by the perception system to be replaced has been designed and implemented; secondly, with this dataset generation tool, the data captured has been properly labeled so it can be used for DL applications; and finally, a sample lightweight network model for semantic segmentation, FCN with AlexNet classification, has been trained and evaluated to test the problem.
By the same reasons that there was no dataset available for our challenge and we had to capture and develop one dedicated to our domain, there were no related works to obtain metrics. In order to have some relevant metrics to compare the results of the developed approach, a modified version of [13] was produced and benchmarked without the use of prior knowledge. Under these assumptions, the new CNN-based method was able to clearly surpass the multimodal approach, though it still lacks robustness to be considered ready for industrial standards. Still, these initial tests have proven the viability of the built dataset generator and the utilization of CNN-based semantic segmentation to replace the multimodal approach.
This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness through Project 2016-78957-R.
Induction of Labour (IOL) is one of the common obstetric procedures and or intervention encountered in maternity unit(s) all over the world [1]. Usually, the risk of continued pregnancy is far more detrimental to the mother or her unborn baby or both [1, 2]. Therefore, a balance in favour delivery is anticipated and planned with the anticipating mother using a woman centered approach [3, 4]. Decision for induction of labour should be led by the most experienced obstetrician on ground in consultation with the team managing such pregnancy. IOL will set up a cascade of events cumulating to the delivery of the baby and automatically elevates that pregnancy state to high risk [2]. These processes are important that they are closely monitored to end point and beyond.
The incidence of induction of labour varies between different settings and appears to be on the rise especially in developed countries. In the United States the rate of IOL is quoted to be approximately 30 percent [5]. While in the United Kingdom (UK) the recent maternity figures show an increase rate of 2.1% from the 29.4% in 2016–2017 to 31.6% in 2017–2018 [6] according to the Hospital Episode Statistics. This increase in induction rates in developed countries and particularly in the UK is attributed to improved diagnostic tools and better understanding of maternal medicine, advanced maternal age, and socioeconomic class variation [7, 8, 9]. The incidence of induction in Africa has been found to be below 10 percent in most settings. In a recent cross-sectional study to assess the prevalence, outcome and associated factors among women delivered at public hospitals in Ethiopia, Lueth et al. [10] reported a prevalence of 9% and failed IOL was responsible for 3.3% increase in caesarean section rates. Vogel et al. [11] in a secondary analysis of data for World Health Organization (WHO) global survey on maternal and neonatal health found that the unmet needs of IOL were between 60% and 80% with an average rate of IOL of 4.4%. In resource-constrained settings there is less capacity due to inadequate facilities, lack of trained staff and limited centers where safe caesarean sections can be performed on a 24 h basis.
In order to understand the concept of induction of labour, some key terms need to be clearly understood:
Labour diagnosis is one of the most important clinical judgements in maternity care and is much more important to understand its onset. However, the mechanisms involved in the onset of labour still remains blurred and why some women will reach postdate or post term without spontaneous onset of labour is elusive. Our narrow understanding of the mechanism of onset of labour makes IOL difficult [19]. Usually, a cascade of biochemical events heralds the onset of a complex well-regulated process that results in cervical ripening, occasional membrane rupture and high frequency myometrial contraction that result in the expulsion of the fetoplacental unit [20].
The mechanism of spontaneous onset of labour has a fetal, placental/fetal membranes and maternal components that contribute to its onset via series of paracrine/autocrine hormones. Uterine quiescence that is maintained throughout pregnancy is switched off by the withdrawal of the functional inhibitory effect of progesterone via progesterone receptor (PR). There is an increase unbalance ratio of progesterone receptor key isoforms with more expression of progesterone receptor A (PR-A) compared with progesterone receptor B (PR-B) that increases with advancing gestational age and might also inhibit the PR-B genes. The overwhelming increased ratio of PR-A to PR-B may promote transcription of contractile protein CX43 [20, 21] and increase gap junctions’ formation in the myometrium [22]. Oxytocin receptors (OXTR) gradually increase with advancing gestational age which may directly increase intracellular calcium concentrations [23].
Prostaglandins (PG) play major roles in initiation and maintenance of uterine contraction via prostaglandin receptors. The prostaglandins that play major role in inducing myometrial contractility are prostaglandin E2 (PGE2) and prostaglandin F2α (PGF2α) which induces intracellular calcium by opening calcium gated channels. Spontaneous labour is also associated with sterile inflammation with increase infiltration of leucocytes and upregulation of inflammatory cytokines and chemokines, particularly interleukin 1B which increases expression of calcium transport protein. Though there are plethora of literatures toward the physiology of the onset of labour, its exact pathway remains elusive [20, 21, 22, 23].
The indication(s) for induction of labour should be weighed on the scale of clinical judgement to ascertain that the benefit(s) of delivery outweighs continuation or prolongation of such pregnancy. This decision should be taken seriously, and there should be shared decision between the managing clinicians and the woman as well as appropriate informed consent taken. Prior to informed consent there should be a dialogue on the primary indication for IOL and other contributory factors, risks and benefits, methods and rationale, and realistic expectations [24].
Indications [13, 25] | Contraindications [13, 25] ] confirmed |
---|---|
Post term >42 weeks Late term pregnancy/postdate 41 0–41 6 weeks Preeclampsia >37 weeks Eclampsia Chronic hypertension Gestational hypertension >38 weeks Twin gestation • Uncomplicated dichorionic twin pregnancy > 38 weeks • Uncomplicated monochorionic twin pregnancy > 36/37 weeks Diabetes in pregnancy or gestational diabetes Alloimmune disease near term or at term IUGR IUFD Intrahepatic cholestasis of pregnancy Prelabour rupture of membrane at term or near term Preterm Prelabour rupture of membrane with GBS colonisation Oligohydramnios Chorioamnionitis Significant but stable antepartum haemorrhage Obstetric Cholestasis | Maternal refusal Prior classical, De Lee or inverted T incision Significant prior uterine surgery (e.g. full thickness myomectomy) Previous hysterotomy Two or more previous caesarean sections Previous uterine rupture Fetal malpresentation (e.g. transverse lie, footling breech) Placenta previa Vasa previa Cord presentation Active genital herpes Invasive cervical cancer |
Controversial | |
Prior IUFD Care giver or maternal request Suspected fetal macrosomia One previous caesarean section | Previous vesicovaginal fistula Previous OASIS |
Indication for induction of labour (Table 1).
The Bishop’s score is used to assess the pre-inductive state of the cervix and to forecast the favorability of the cervix to comply during IOL. IOL is highly likely to fail if it is embarked upon without a proper assessment of the woman and consideration of the bishop score. The cervix retains its firm rigidity throughout pregnancy. As the uterus enlarges, the cervix becomes softer and distensible in preparation for labour and delivery by ripening. Ripening is a complex series of biochemical processes that results in softening of the cervix, effacement, and dilation. This usually occurs prior to uterine contractions for both spontaneous and iatrogenic labour. The cervix is composed of fibroid connective tissue, collagen (type I, III predominately and a small amount of type IV), elastin, vasculature, fibroblast, and smooth muscle [25, 26].
Cervical remodelling is associated with increase vascularity, stromal and glandular hypertrophy. There is concomitant increase in inflammatory activities with production of cytokines which leads to the release of metalloproteases (2 and 9) that degrades cervical collagens. The extracellular matrix which is strengthened by proteoglycans is gradually reduced with an unbalanced increase in glycosaminoglycans. The remodelling of the cervix is associated with decreased cross-linkages between collagen helices which cumulates to increased compliance to softening, effacement, and dilation [26].
Various hormones are responsible for this complex interaction that leads to cervical ripening. Increase in cyclooxygenase −2 lead to rise in the level of local PGE2 and PGF2α. PGE2 will activate a series of reactions: increase dilatation of small vessels in the cervix, increase collagen degradation, increase in hyaluronic acid, increase chemotaxis for leucocytes and increase release of Interleukin −8. PGF2α stimulate an increase in glycosaminoglycans. The role of Nitric Oxide (NO) has been the focus on recent studies on its contribution to cervical ripening. Increased levels of induced Nitric Oxide Synthase (iNOS) activity by resident and migrating inflammatory cells is associated with cervical ripening by dramatic increase in NO. NO might play a role by increased activities of metalloproteases [27, 28, 29].
Dr. Edward Bishop in 1964 proposed a Prelabour scoring system to assess the likelihood of going into spontaneous labour [30]. It has now undergone several modifications to assess the favourability prior to IOL. The score is an aggregate sum of the cervical dilatation, consistency, effacement, position and fetal station [31]. A total score of 6 and above is considered favourable and score below that is deemed unfavourable. The Calder Modification replaces cervical effacement with cervical length with a total score of 12 [31, 32]. In a retrospective study [33] done recently to closely look at the relationship between bishop score and successful induction, a higher bishop score (8–10) at the beginning of IOL is directly proportional to the higher rates of successful IOL compared with bishop score between 6 and 7. There is recent evidence to show that the cervical length assessment by transvaginal ultrasound appear to be superior to the bishop score, however this is not a routine practice [34].
An induction check list is good clinical practice to ensure women are properly prepared, improve IOL success and prevent complications. Every maternity setting should have an IOL check list tailored to meet their local standard. A check list should have the patient’s biodata, informed consent, gestational age, indication for IOL, patient routine booking investigations, associated comorbidity, previous surgeries, and the bishop score. A recent ultrasound with estimated fetal weight - which also rules out any contraindication for vaginal delivery, an admission CTG and the method of cervical ripening and IOL with the responsible doctor’s name and signature should be clearly documented [35].
Cervical ripening and induction agents can be group into pharmacological, mechanical and combination of both. While some agents are intended to ripen the cervix before initiation of induction of labour, majority will progress to initiate uterine contractions. See Table 3.
Prostaglandins have gained wide acceptance as a cervical ripening and induction agent over the decade and is now considered a preferred agent for both. However, they are said to be associated with a small risk for tachysystole and fetal heart rate changes [36]. Misoprostol—a synthetic prostaglandin E1 analogue—is heat stable and low cost compared to Dinoprostone. This has seen its wide usage especially in low resource setting. Misoprostol availability is said to bring equity in the disparity of induction of labour between developed and resource constrained regions of the world and recently a low dose misoprostol preparation was approved for IOL in Nordic countries [37, 38]. The dosing and timing of misoprostol ranges 25–50 mcg every 2–6 h. However, in a study of balancing efficacy and safety, the minimum efficacious dose associated with less complication is 25 mcg [38], while the vaginal compared with the oral route appears to offer significant clinical advantage in successful vaginal delivery [39].
Dinoprostone is prostaglandin E2 that comes in two popular preparations as Cervidil, a control release hydrogel suppository 10 mg vaginal inserted every 12 h at 0.3 mg/h. and Prepidil administered as an intracervical gel of 0.5 mg/2.5 ml or 1 or 2 mg for intravaginal insertion every 4–6 h with a maximum of 3 doses in 24 h. Dinoprostone needs to be refrigerated and is relatively expensive for resource constrained settings [40, 41]. The International Federation of Obstetrics and Gynaecology (FIGO) recently established an expanded chart on the dosage of misoprostol in variety of Obstetric and Gynaecologic conditions in the light of new evidence and through expert contributions which has been endorsed by FIGO safe motherhood and newborn health committee (Table 4) [42].
Pharmacological | Mechanical | Combination |
---|---|---|
Prostaglandins
| Transcervical catheters
Stripping membrane Laminaria Cervical hygroscopic dilators Extra-amniotic saline infusion | Transcervical catheter + Prostaglandins Transcervical catheter + Oxytocin |
Cervical ripening and induction agents.
FIGO misoprostol-only recommended regimens 2017 [42] confirmed.
Oxytocin is a neuropeptide hormone produced by the hypothalamus via the paraventricular nuclei and stored in the posterior pituitary gland. It is secreted when triggered by labour, lactation, social interaction, and stressors. Oxytocin acts on the myometrium to induce uterine contractions via G-proteins with the subsequent release of intracellular calcium stores through a complex interplay on its activity on phospholipase C. Its potency is directly proportional with advancing gestational age when there are sufficient oxytocin receptors [43]. The synthetic oxytocin is a cyclic nonapeptide which is identical to the natural oxytocin obtained via chemical synthesis. Oxytocin acts within a minute of intravenous injection and 2–4 min via the intramuscular route. In a low dose infusion, it causes rhythmic uterine contractions with a repetitive pattern. It can cause sustained uterine contraction at high dose infusion. It reaches its steady state between 20 and 40 min during continuous infusion, and it is metabolised and cleared by the liver and kidneys with about 1 percent unchanged in the urine [43, 44].
Oxytocin might have plausible synergistic effect in cervical ripening, however its role as a cervical ripening agent is not well established [45]. There are still controversies on its optimal regimen for induction of labour with varied protocols in different settings. However, a low dose regimen initiated between 0.5 and 1 mU, and increased by 1 mU/min every 30–40 min interval is preferred over high dose regimen of 4–6 mU [44].
Maternal and Child Health (Obstetrics and Gynaecology Division) University of Namibia
Dosage
The dose of oxytocin is calculated using the formula: 10 units of oxytocin in 1000 ml of normal saline or ringers’ lactate is equivalent to 10,000 mU. Therefore, each ml (1 ml) of fluid mix is 10 mU. In 1000 ml give 6 ml/h (60 mU/h.)
Starting dose at 1 mU/min oxytocin infusion should be escalated at 30 min interval until adequate and strong contractions (3–4 lasting 40–50 s in 10 min) is established.
The 2 IU in 200 ml is recommended because it is cost effective with less water intoxication in resource constrained settings. Oxytocin dose of greater than 20 mU/min highlighted in red is to be administered only by a senior resident doctor or a specialist. Low dose oxytocin infusion should be administered by infusion pump, however in settings where there are no infusion pumps, a senior doctor should closely monitor the infusion at a rate of 10 drops/min in a diluent of concentration of 10 IU in 1000 ml escalated every 30 min maximum of 60 drops/min.
Preparation
Obtain informed consent for oxytocin induction and or augmentation of labour
Do a pre induction and or augmentation CTG before the onset of oxytocin
Continuous CTG is advised for all patients
Do haemoglobin and obtain blood for blood group and save serum
Do not administer oxytocin on a previous uterine scar except by a specialist instruction (such specialist should monitor the patient)
Clearly document the indication for oxytocin use in the labour care guide
Inform all the managing/incoming team/theatre staff/Anaesthetist
For induction of labour: use oxytocin only in patients with bishop score >6 and perform artificial rupture of membrane if the membranes are intact
Do not give oxytocin within 6 h of prostaglandin use
Monitoring
Continuous CTG
Escalate the dosage every 30 min after assessment of maternal condition, fetal condition, and uterine contraction
Maintain, reduce, or stop dose when adequate uterine contractions are achieved (3–4 contractions in 10 min lasting 40–50 s)
Use the labour care guide in monitoring all patients on oxytocin infusion
STOP oxytocin and give plain normal saline whenever there is uterine hyperactivity:
Tachysystole
Hypertonus
Hyperstimulation
The woman should adopt a left lateral position.
Consider supplemental oxygen, especially when there are signs of maternal hypoxia or category III CTG changes characterised by absent baseline variability with any of the following: recurrent late decelerations, recurrent variable decelerations, bradycardia, or sinusoidal pattern. Consider delivery if fetal heart rate abnormality persists.
Fetal rate abnormality may be the earliest sign of uterine rupture especially in patients on oxytocin infusion
After 4 h, stop infusion to reassess if there is any benefit of further infusion
The transcervical extra-amniotic catheters have been one of the oldest methods of cervical ripening and induction of labour [46]. The Foley catheter has a single balloon with a capacity between 30 and 80 ml that is inserted into the extra-amniotic space compared to the more recent Cook’s catheter with double balloons with one inserted into the extra-amniotic space as the uterine balloon and another below the cervix as the vaginal balloon with capacity of 80 ml for both [47]. In a Cochrane data base of systematic review of 23 studies [48], there were no statistically significant difference in comparing Foley catheter and prostaglandins in achieving vaginal delivery within 24 h with similar incidence of caesarean section rates. However, the transcervical Foley’s catheter had lower incidence of tachysystole and or fetal heart rate abnormalities [48].
In a randomised controlled trial comparing a 30 and 80 ml Foley catheter balloon for preinduction cervical ripening, 80 ml provided faster labour, more dilatation and decrease need for oxytocin [49]. However, no significant difference was found between 12- and 24-h duration in induction delivery interval [46]. Other additives like applying traction and weight to the catheter has demonstrated faster expulsion of the Foley without any effect on the induction delivery interval (Figure 1) [50].
Transcervical extra-amniotic Foley’s catheter [
A single transcervical Foley catheter without extra-amniotic saline has better patient satisfaction with less discomfort compared with the double balloon catheter and it is cost effective especially in resource constrained settings [51]. Recent evidence is suggesting that highly motivated women can use transcervical Foley balloon in an outpatient setting with no increase in morbidity and or adverse outcome [52].
One of the major concerns of transcervical balloon catheters is theoretical risk of infection. This risk has not been validated and the rates of puerperal and neonatal infection appear similar to other methods of induction [53, 54, 55].
Combination of mechanical and pharmacologic methods has been one of the recent advances in labour induction to increase efficiency and efficacy, reduce cost and reduce adverse effects of induction agents to the mother and her baby. The BIGIN trial was a randomised control trial that compared buccal versus vaginal misoprostol with Foley catheter. The vaginal misoprostol with Foley catheter resulted in a shorter induction delivery time with similar caesarean section rates, perinatal and maternal morbidity among both arms of the study. Therefore, vaginal misoprostol combination is recommended as the preferred method for combination method [56].
This is an old but reliable method of inducing cervical remodelling to comply with induction of labour. It is a mechanical method that involves a digital vaginal examination with the aim of placing one or two gloved fingers -usually the index and the middle - into the cervix and thereafter performing a circular sweeping motion. This is an artificial separation of the membrane that increases the activity of PGF2 and phospholipase which induces the complex cascade of cervical ripening [57]. In a recent Cochrane review [58], there is more likelihood to have spontaneous vaginal delivery among women who had their membranes swept. NICE guidelines on IOL recognised sweeping of membrane as an adjunct to formal induction [13]. Membrane sweeping can be offered to women from 39 weeks of gestation and thereafter additional membrane sweep could be offered at 40 and 41 weeks if there is no onset of spontaneous labour after the first membrane sweep [13]. There are concerns on the possibility of large doses of bacteria pushed above the internal os resulting in increased risk of maternal and fetal infection especially when there is a prolonged delay between membrane sweep and the onset of labour [57]. The role of cervical massage as an alternative method to membrane sweeping appears promising with significant effect on bishop score and can be considered as a reasonable option; especially if the cervical os is closed [59].
Laminaria are dried seaweed stems (Laminaria japonica or Laminaria digitalis) that can be placed in the cervix to induce cervical ripening by absorbing water and expanding to cause cervical dilation and provoke endogenous prostaglandin release [60, 61]. Dilapan-S (MEDICEM, the Netherlands) is a sterile hygroscopic cervical dilator that has demonstrated no increased risk of infection like the laminaria tents. They are polymer rods which consist of the dilating part made of hydrogel and a polypropylene handle. An international observation study has revealed that Dilapan-S has not increased the risk of hyperstimulation and may be used in an outpatient setting [61, 62].
Amniotomy can be used as an adjunct to induction of labour in a woman whose cervix is accessible and favourable. It is usually a prelude to oxytocin induction. However, in recent NICE guideline, it is not recommended as a sole method of induction with or without oxytocin except where prostaglandins are contraindicated [13, 61]. A recent randomised controlled trial has found that immediate oxytocin administration after AROM was not associated with shorter AROM to delivery time compared to delay of 4 h. Therefore, the decision to use any method should follow local protocols based on resources and maternal choice [63]. It is important to remember that there is a major risk of the cord prolapse with an unengaged presenting part.
Other methods of cervical ripening and induction of labour are yet to be clinically validated. Therefore, the use of mifepristone, acupuncture, homoeopathy, hypnotic relaxation, baths, enema, sexual intercourse, breast stimulation, intracervical hyaluronidase, relaxin, corticosteroids, and oestrogen need further research [64].
Certain obstetric conditions occur more commonly, therefore a brief overview of IOL in these conditions are highlighted.
Prolonged pregnancy is a loose term that applies to all pregnancy beyond estimated due date which comprises of postdate and post term pregnancy. It occurs in about 5–10% of all pregnancy and the rate is reduced to incidence of 2–5% with ultrasound dating in the first trimester. A better classification of term pregnancy underscores the importance of late term (41 0/7–41 6/7 and post term (42 0/7 and beyond) on uteroplacental insufficiency with increase perinatal morbidity and mortality. The risk of still birth, macrosomia, shoulder dystocia, birth injury, postpartum haemorrhage and meconium aspiration syndrome are higher at late term and post term compared with early term (37 0/7–38 6/7) and full term (39 0/7–40 6/7) [13, 65]. Therefore, NICE guidelines and WHO recommends IOL at 41 weeks and beyond. In the rare circumstances when the woman declines IOL beyond 42 weeks, she should have twice weekly CTG and ultrasound and be counselled based on findings [1, 13]. Neonatal outcome did not improve following IOL at 39 weeks compared with expectant management in a multicenter trial (ARRIVE) in the United States, however the rate of caesarean section was lower in the intervention arm [66].
Hypertension complicates about 10% of pregnancies and remains one of the major causes of perinatal and maternal morbidity and mortality [67, 68]. Preeclampsia is considered a severe form of hypertension in pregnancy with an incidence of 2–5% of all hypertensions in pregnancy. Early onset (<34 weeks) and preterm preeclampsia (<37 weeks) could present with severe disease to prompt immediate delivery [68]. Following the HYPITAT Trial I, women with gestational hypertension or preeclampsia at term should be offered delivery. While HYPITAT II trial, recommends that non severe hypertension between 34 and 37 weeks should be delivered at 37 weeks [69, 70].
The rupture of amniotic sac before the onset of labour is termed as prelabour rupture of membrane which can occur before (preterm prelabour rupture of membrane) or after 37 weeks of gestation (term prelabour rupture of membrane) [71]. Preterm (24/0–36/6) prelabour rupture of membrane (PPROM) complicates about 3% of pregnancies and is responsible for about 30–40% of preterm births. The Royal College of Obstetricians and Gynaecologists recommend conservative management for uncomplicated PPROM to 37 weeks. Prelabour rupture of membrane should be offered delivery after 37 weeks if it occurs and there is no onset of labour within 24 h or when PPROM is complicated by infection [72]. The induction method can either be by prostaglandin or oxytocin, however, the Bishop Score should be made favourable before such induction is embarked upon.
Hyperglycaemia is considered as one of the most common medical conditions in pregnancy with one in every 6 live births occurring in women with some degree of hyperglycaemia. Gestational Diabetes Mellitus is the most common type, and it is responsible for 84% of hyperglycaemia in pregnancy. There are higher incidences of perinatal and maternal morbidities and mortalities among pregnant women with any form of hyperglycaemia. A balance of glycaemic control and fetal maturity should be weighed to consider the timing of delivery. FIGO recommends delivery for women at 40–41 weeks for a well-controlled hyperglycaemia with fetal weight of <3800 g while delivery via induction is recommended at 38–39 weeks for poorly controlled hyperglycaemia with babies that are between 3800 and 4000 g. Elective caesarean section is the preferred delivery mode for babies weighing 4000 g and above [73].
The high rates of uterine rupture in patients with previous caesarean section is well established. There is a 2–3 times higher risk of uterine rupture in patient induced or augmented with previous caesarean section. This risk should be understood by both the physician preferably, a senior obstetrician, and the patient before undergoing induction or augmentation of labour in women with previous caesarean section. The risk of uterine rupture is lower with mechanical methods (Foley catheter and amniotomy) compared with prostaglandins [74].
Improving the efficiency of uterine contractions by increasing the frequency, duration, and intensity in women with inadequate or uncoordinated uterine contractions of spontaneous onset to reduce or prevent adverse outcome for the babies and their mother associated with prolonged labour is best describe as augmentation [75, 76]. The term AOL is loosely applied to women undergoing induction of labour, but this is a misnomer because contractions in induction of labour are not of spontaneous onset. The decision to augment the labour process should be carefully and meticulously evaluated after a thorough history and examination excluding any cephalopelvic disproportion and establishing that the cause of unsatisfactory progress of cervical dilatation and descent of the presenting part is solely the problems of power. Every local maternity setting should have a protocol of AOL using oxytocin infusion which is usually the same strength and frequency with induction protocol (see Table 5). Low dose oxytocin regimen is favoured because of less complications related to augmentation [76]. AROM can be considered as a sole method AOL because it enhances uterine contractions by increasing plasma prostaglandins, however the effectiveness of AROM appears to be debatable [77, 78].
Oxytocin regimens | |||||
---|---|---|---|---|---|
Time from start (min) | Oxytocin dose (mU/min) | Volume of infusion (ml/h) | |||
10 IU in 500 ml | 10 IU in 1000 ml | 2 IU in 200 ml | 10 IU in 1000 ml Without infusion set | ||
0 | 1 | 3 | 6 | 6 | 10 drops |
30 | 2 | 6 | 12 | 12 | 20 drops |
60 | 4 | 12 | 24 | 24 | 30 drops |
90 | 8 | 24 | 48 | 48 | 40 drops |
120 | 12 | 36 | 72 | 72 | 50 drops |
150 | 16 | 48 | 96 | 96 | 60 drops |
180 | 20 | 60 | 120 | 120 | |
210 | 24 | 72 | 144 | 144 | |
240 | 28 | 84 | 168 | 168 | |
270 | 32 | 96 | 192 | 192 |
Low dose oxytocin infusion for induction and augmentation of labour. Version (1.0) 2021.
Oxytocin dose of greater than 20 mU/min highlighted in red can only be administer by a specialist or a senior resident doctor.
Prior to preforming AROM, an informed consent is necessary, and patient should be informed of the benefits of AROM and the possible complications therein and the fetal heart rate is checked. AROM can be performed with an Amniotomy finger cot (Amnicot) or Amniotomy hook (AmniHook) or a spinal needle for control release of amniotic fluid. The woman adopts a supine position and flexes her hips and knees (frog legged), or it can be done in the lithotomy position. A sterile glove finger is introduced using the dominant hand to performed vaginal examination and noting the cervical dilatation, effacement, position, station and to exclude cord presentation and possible vasa previa is noted. During the vaginal examination the amniotic perforator can be introduced with the non-dominant hand and the tip of the amniotomy hook is pushed against the sac with the index and middle finger guiding it and pull back. With successful rupture of membrane, the examining fingers should be held back to exclude cord prolapse and the amniotic fluid should be assessed for volume, colour, smell or particle within. It is good practice to recheck fetal heart and observe the woman closely for any vaginal bleeding [75, 76, 79]. For control release with spinal needle a bivalved speculum is required.
Artificial contractions are said to be more painful than spontaneous ones, more so the intensity and the duration might be more exaggerated than natural contractions. Therefore, the rates of complications associated with artificial uterine contractions are multiple folds compared with contractions from spontaneous onset [13]. The complications of IOL and AOL could range from tachysystole, hypertonus or an outright tachysystole with fetal heart rate abnormalities, placenta abruption or uterine rupture [80, 81] which occur in about 1–5% of women undergoing IOL. It is estimated that IOL could fail in 15% of patient with unfavourable cervix [81]. Both IOL and AOL could result in cord prolapse following AROM. There is a tendency of increasing the risk of infection, operative vaginal delivery and increased caesarean section rate [80] in women undergoing IOL. Poor childbirth experience was encountered in about 4.5% of patient undergoing IOL in a recent study to assess maternal childbirth experience [82]. The risk of primary post-partum haemorrhage in patients undergoing IOL is well documented [83]. Fetal complications can be in the form of fetal distress, meconium stained liquor and neonatal jaundice. Litigation from the abuse of oxytocin has been enormous in the past decade globally [84].
There is an increased rate of IOL especially across developed countries, however the unmet need for IOL in developing countries is bridged by the availability of misoprostol and Foley’s catheter at cost effective rates. Timely and appropriate IOL can lead to reduction in perinatal and maternal morbidity and mortality. Therefore, there is need for obstetricians and physicians that provide care in women’s health to keep abreast with the best external available evidence on the subject matter. It is important to employ the concept of respectful maternity in the pre-induction, induction, and post induction phase to improve outcomes, reduce litigations and enhance women satisfaction. A diagnosis of failed induction does not translate to automatic caesarean section if the fetal and maternal conditions are adjudged to be normal and stable; a pause and restart can be initiated after 24–48 h with lower threshold for intervention in the second cycle.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nIntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
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\n\nAll scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
\n\n\n\nThe Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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\n\n\n\n\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{mdrv:"www.intechopen.com"},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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Overall, optical methods can support traditional periodontal diagnosis and improve treatment planning and clinical periodontal care.",book:{id:"7244",slug:"periodontology-and-dental-implantology",title:"Periodontology and Dental Implantology",fullTitle:"Periodontology and Dental Implantology"},signatures:"Fardad Shakibaie and Laurence Walsh",authors:[{id:"179467",title:"Prof.",name:"Laurence",middleName:null,surname:"Walsh",slug:"laurence-walsh",fullName:"Laurence Walsh"},{id:"235443",title:"Dr.",name:"Fardad",middleName:null,surname:"Shakibaie",slug:"fardad-shakibaie",fullName:"Fardad Shakibaie"}]},{id:"24363",title:"Biomechanics of Tooth-Movement: Current Look at Orthodontic Fundamental",slug:"biomechanics-of-tooth-movement-current-look-at-orthodontic-fundamental",totalDownloads:26821,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"277",slug:"principles-in-contemporary-orthodontics",title:"Principles in Contemporary Orthodontics",fullTitle:"Principles in Contemporary Orthodontics"},signatures:"Joanna Antoszewska and Nazan Küçükkeles",authors:[{id:"50158",title:"Prof.",name:"Joanna",middleName:null,surname:"Antoszewska",slug:"joanna-antoszewska",fullName:"Joanna Antoszewska"}]},{id:"71271",title:"Flap Techniques in Dentoalveolar Surgery",slug:"flap-techniques-in-dentoalveolar-surgery",totalDownloads:2638,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Most dentoalveolar procedures involve the reflection of mucosal flaps. 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Mandibular fractures are among the most common traumatic injuries of the maxillofacial region. Even though treatment modalities are well established and being practiced for a long time, untreated and postoperative complications still decrease the patient’s quality of life. This chapter aims to describe the cause, clinical presentations, diagnoses, and current treatment methods on the basis of resent literature.",book:{id:"7572",slug:"trauma-in-dentistry",title:"Trauma in Dentistry",fullTitle:"Trauma in Dentistry"},signatures:"Guhan Dergin, Yusuf Emes and Buket Aybar",authors:[{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin"},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes"},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar"}]},{id:"56461",title:"Permanent Maxillary and Mandibular Incisors",slug:"permanent-maxillary-and-mandibular-incisors",totalDownloads:2732,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The permanent incisors are the front teeth that erupt between 6 and 8 years of age. They are eight in number, four upper and four lower, two centrals and two laterals. They have sharp biting surfaces designed for shearing and cutting of food materials into small chewable pieces. They are the teeth most visible to the others during eating, smiling and talking, and thus, they have high aesthetic value for the individuals. The unique characteristics, arch position, function, development and chronological age of each tooth will be highlighted. In addition, the different aspects with their geometric outlines, outlines and surface anatomy of these teeth will be described. A brief explanation about the pulp cavity, tooth socket and normal occlusion for each tooth will be included.",book:{id:"5814",slug:"dental-anatomy",title:"Dental Anatomy",fullTitle:"Dental Anatomy"},signatures:"Mohammed E. Grawish, Lamyaa M. Grawish and Hala M. 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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. 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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:"38",title:"Pollution",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",isOpenForSubmission:!0,editor:{id:"110740",title:"Dr.",name:"Ismail M.M.",middleName:null,surname:"Rahman",slug:"ismail-m.m.-rahman",fullName:"Ismail M.M. Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/110740/images/2319_n.jpg",biography:"Ismail Md. Mofizur Rahman (Ismail M. M. 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He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. 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The research focus of Dr. Zinnat includes the effect of the relative stability of metal-chelator complexes in the environmental remediation process designs and the development of eco-friendly soil washing techniques using biodegradable chelators.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"39",title:"Environmental Resilience and Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/39.jpg",isOpenForSubmission:!0,editor:{id:"137040",title:"Prof.",name:"Jose",middleName:null,surname:"Navarro-Pedreño",slug:"jose-navarro-pedreno",fullName:"Jose Navarro-Pedreño",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRAXrQAO/Profile_Picture_2022-03-09T15:50:19.jpg",biography:"Full professor at University Miguel Hernández of Elche, Spain, previously working at the University of Alicante, Autonomous University of Madrid and Polytechnic University of Valencia. Graduate in Sciences (Chemist), graduate in Geography and History (Geography), master in Water Management, Treatment, master in Fertilizers and Environment and master in Environmental Management; Ph.D. in Environmental Sciences. His research is focused on soil-water and waste-environment relations, mainly on soil-water and soil-waste interactions under different management and waste reuse. His work is reflected in more than 230 communications presented in national and international conferences and congresses, 29 invited lectures from universities, associations and government agencies. 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He has developed his research activity in the fields of fauna and soil ecology, and in the treatment of organic waste, having been the founder and principal investigator of the Environmental Biotechnology Group of the University of Vigo.\r\nHis research activity in the field of Environmental Biotechnology has been focused on the development of novel organic waste treatment systems through composting. 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She has focused her research activity on the taxonomy, fauna and ecology of aquatic beetles, in addition to other lines of research such as the conservation of biodiversity in freshwater ecosystems; conservation of protected areas (Red Natura 2000) and assessment of the effectiveness of wetlands as priority areas for the conservation of aquatic invertebrates; studies of water quality in freshwater ecosystems through biological indicators and physicochemical parameters; surveillance and research of vector arthropods and invasive alien species.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorThree:{id:"464288",title:"Dr.",name:"Francisco",middleName:null,surname:"Ramil",slug:"francisco-ramil",fullName:"Francisco Ramil",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RI7lHQAT/Profile_Picture_2022-03-31T10:15:35.png",biography:"Fran Ramil Blanco (Porto de Espasante, A Coruña, 1960), is a doctor in biology from the University of Santiago de Compostela and a Professor of Zoology at the Department of Ecology and Animal Biology at the University of Vigo. 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He was also invited to serve as an associate editor for special issues of the Journal of the American Water Resources Association. He has served as an editorial member for international journals such as Hydrology, Journal of Ecology & Natural Resources, and Hydro Science & Marine Engineering, among others. He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. 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He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. 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Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:null,institution:null},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"417317",title:"Mrs.",name:"Chiedza",middleName:null,surname:"Elvina Mashiri",slug:"chiedza-elvina-mashiri",fullName:"Chiedza Elvina Mashiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"352140",title:"Dr.",name:"Edina",middleName:null,surname:"Chandiwana",slug:"edina-chandiwana",fullName:"Edina Chandiwana",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"342259",title:"B.Sc.",name:"Leonard",middleName:null,surname:"Mushunje",slug:"leonard-mushunje",fullName:"Leonard Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"347042",title:"Mr.",name:"Maxwell",middleName:null,surname:"Mashasha",slug:"maxwell-mashasha",fullName:"Maxwell Mashasha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Midlands State University",country:{name:"Zimbabwe"}}},{id:"2941",title:"Dr.",name:"Alberto J.",middleName:"Jorge",surname:"Rosales-Silva",slug:"alberto-j.-rosales-silva",fullName:"Alberto J. Rosales-Silva",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"437913",title:"Dr.",name:"Guillermo",middleName:null,surname:"Urriolagoitia-Sosa",slug:"guillermo-urriolagoitia-sosa",fullName:"Guillermo Urriolagoitia-Sosa",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"435126",title:"Prof.",name:"Joaquim",middleName:null,surname:"José de Castro Ferreira",slug:"joaquim-jose-de-castro-ferreira",fullName:"Joaquim José de Castro Ferreira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"437899",title:"MSc.",name:"Miguel Angel",middleName:null,surname:"Ángel Castillo-Martínez",slug:"miguel-angel-angel-castillo-martinez",fullName:"Miguel Angel Ángel Castillo-Martínez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"289955",title:"Dr.",name:"Raja",middleName:null,surname:"Kishor Duggirala",slug:"raja-kishor-duggirala",fullName:"Raja Kishor Duggirala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Jawaharlal Nehru Technological University, Hyderabad",country:{name:"India"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. Physiology research may be linked to development, aging, environment, regular and pathological processes, adaptation and evolution, exercise, or several other factors affecting, or involved with, animal physiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11406,editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,series:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261"},editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón 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