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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
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Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Computer vision (CV) is a compelling field of Artificial intelligence that develops the theory and methods by which information about the real world can be automatically extracted and analyzed from image data. Image data can be in many forms, such as image, video, depth image, multi-camera views, or multidimensional data from a medical scanner.
The objective of CV is to model the real world or to recognize objects from digital images enabling computers or devices to “see”, interpret, manipulate, analyze, and understand what was seen and draw conclusions about the properties of the 3D world based on a given image or a sequence of images [1].
Basic CV tasks are image classification, segmentation, similarity calculation and object localization. Recognition of objects present on the scene and their features (e.g., shapes, textures, colors, sizes, spatial arrangement,) is often prerequisite for more complex CV tasks such as image retrieval, image description, object detection, object tracking, action recognition, image or scene analysis, and image understanding [2].
In all tasks, the starting point are the image features that carry important information and need to be extracted and processed in order to generate new information and conclusions. The image features can be divided into low-level features such as corners, edges, or contours that can be extracted with relatively simple image operations, and high-level features that require domain-knowledge to get structured information related to the object or action being taken [3].
Feature extraction can be described as a pre-processing step to remove redundant parts from the data and keeping the key information for accomplishing the task. Some well-known features that can be extracted are Optical flow for extracting motion information, Histogram of Oriented Gradients (HOG) and Silhouette for extracting shape information, Space–Time Interested Points for extracting interest points, etc.
To accomplish typical CV tasks, Image processing and Machine learning (ML) play an important role. Image processing is focused on low-level features and manipulation of image data for normalizing photometric properties of visual data, removing digital noise, data augmentation, etc., and is not concerned with understanding the content of visual data. However, when it comes to interpret the content and draw conclusions about the image to automate CV tasks, the most important fields are ML and its subfield Deep learning (DL) [2].
Before DL, computer vison tasks required a lot of coding and manual effort to define the features that can be extracted from images, with little automation involved [4]. With DL methods such as Convolutional Neural Network (CNN) [5], much of that work related the features to be extracted can be inferred automatically from data. Even though many features can be extracted automatically in the CNN framework for different tasks, manual feature extraction can still be useful for either augmenting the automatically extracted features or perform other tasks such as temporal segmentation of video or detection of active players.
Typical CV tasks such as object detection, object tracking, and action recognition, the tasks we will focus on the most here, are supervised learning tasks (Figure 1). Supervised learning relies on labeled ground truth data, based on which the learning algorithm infers the mapping between the raw data and the desired labels in the training stage. Thus, a prerequisite for supervised learning is data preparation, which includes data collection and labeling, pre-processing and feature extraction, followed by splitting data into a training and testing set, then selecting an appropriate learning algorithm and model structure for the specific task. After training and validating the model, the model needs to be tested and the obtained results need to be compared with the ground-truth data to evaluate the performance. The performance evaluation is represented with different metrics appropriate for the specific task.
Supervised learning process.
CV tasks can be implemented for image and video analysis in different domains, including the sports domain. Various CV techniques can be very useful for all parties interested in analyzing the game, including the coach, the reporters, the referee team, the physiotherapists and others, for making decisions about an occurred event, for monitoring and comparing the performance of each individual player, for choosing a strategy, for fast automatic analysis of video materials captured during a match or practice and the like.
In this chapter, the focus will be on handball, a team indoor sport played with a ball by two teams with seven players, one of whom is the goalkeeper. To analyze handball sports videos, different CV tasks can be combined. For example, the object or person detection can be applied to detect the players on the field, the object tracking to follow the players’ movements across the field, and action recognition to analysis of the players’ performances.
In the next sections, a created dataset for handball will be presented, and then the simple CNN architecture and typical measures for evaluation of model performance are described. In the following sections, CV tasks will be described and implemented in the context of handball. Object detection with YOLO and Mask R-CNN is presented in Section 5, object tracking with Hungarian algorithm and Deep SORT in Section 6, and action recognition using LSTM model in Section 7. Applications of optical flow and spatiotemporal interest points for temporal segmentation and active player determination are presented in Section 8.
The handball dataset used for the following experiments was recorded during a handball school, where participants were young handball players and their coaches.
The dataset consists of high-quality video recordings of practices and matches, filmed in a sport hall or in an outdoor handball field, without additional scene preparation or player instruction to preserve real-world conditions. The recordings were made using different stationary cameras positioned on the left or right border of the field on a tripod at 1.5 m, or from the spectator’s viewpoint at the height of approximately 3.5 m and the distance from the filed limit of 10 m. The recordings are in full HD resolution (1920x1080) and contain from 30 to 60 frames per second.
The dataset is quite challenging with a cluttered background, a variable number of players at different distance from the camera, who move fast and often change direction, are often occluded with another player, have jerseys of similar color to the background, etc.
The data needs to be prepared, processed and labeled for each specific task that will be considered here, so that domain- and task-specific models can be made by either training from scratch if there is sufficient data, or preferably, by tuning an existing model for a similar task using examples from the new domain.
For the player and ball detection task, 394 training and 27 validating images were extracted from the videos in the handball dataset and manually labeled, to form the PBD-Handball dataset [6].
To obtain the ground-truth data for the player tracking task, a subset of videos from the handball dataset was first processed using the YOLOv3 object detector, then with the DeepSORT tracker to bootstrap the annotation process, and lastly manually corrected. The total duration of the annotated dataset, named PT-Handball, prepared for this task is 6 min and 18 s [7].
For the action recognition task, parts of the videos containing the chosen actions were extracted from the whole handball dataset to get a subset of 2,991 short videos that were then labeled with one of the 10 action classes, or the Background class where action is not happening. This dataset is referred to as PAR_Handball.
Typical models used today for image classification and object detections tasks are based on Convolutional Neural Networks (CNNs), since they are adapted to solve the problems of high-dimensional inputs and inputs that have many features. The CNN network consists of a number of convolution layers, after which the network has been named, the activation and pooling layers, and one or more fully connected layers at the end of the network [8], (Figure 2).
Simplified CNN architecture.
The convolution layer refers to a mathematical operator defined over two functions with real value arguments that give a modified version of one of the two original functions. The layer takes a map of the features (or, in the first layer, the input image) and convolves it with a set of learned parameters resulting in a new two-dimensional feature map. The sets of learned parameters (weights and thresholds) are called filters or kernels. Each filter is a 2D square matrix, small in size compared to the image to which it is applied (equal depths as well as the input). The filter consists of real values that represent the weights that need to be learned, so that the output feature map contains useful information such as a particular shape, color, edge in order to give the network good results.
The pooling layer is usually inserted between two successive convolution layers to reduce a map resolution and increase spatial invariance or network insensitivity to minor shifts such as rotations, and translations of features in the image. The pooling layer also reduces memory requirements for network implementation. The most commonly used pooling methods are arithmetic mean and maximum, but several other pooling methods are also used in CNN architecture, such as Mixed Pooling, Stochastic Pooling, Spatial Pyramid Pooling and others [8].
The activation function defines the output of a node given an input or set of inputs. In its simplest form, this function is binary and represents the action potential of neurons by propagating the output value of the neuron or by stopping it. There is a broad range of univariate functions of linear combination of the input variables acting as CNN activation functions such as linear activation functions, jump functions, and sigmoidal functions. The jump and sigmoidal functions are a better choice for neural networks that perform classification tasks while linear functions are often used in output layers where unlimited output is required. Newer architectures use activation functions, typically Rectified Linear Unit (ReLU), behind each layer.
A fully connected layer is the last layer in the network. The name comes because of its configuration: all neurons are linked to all the outputs of the neurons in the previous layer. Fully connected layers can be viewed as special types of convolution layers where all feature maps and all filters are 1 x 1.
Network hyperparameters are all parameters needed by the network that have to be set before the network is provided with data for learning. The hyper-parameters in convolutional neural networks are the learning rate, the number of epochs, the number and kind of network layers, the activation function, the initialization weights, input pre-processing and the error function.
Selecting the structure of the CNN network for feature extraction plays a vital role in object detection because the number of parameters and types of layers directly affect the memory requirements, speed, and performance of the detector.
In this paper, two types of CNN-based networks, YOLO and Mask-RCNN, have been used for object detection, while for the task of action recognition, the CNN network is not used on its own, but it forms a part of the more advanced LSTM network as the feature extractor.
The performance of object detectors is usually evaluated in terms of accuracy, recall, precision, and F1 score [9], for a given confidence threshold. The same measures can also be used for evaluation of the action classification task.
The detections are deemed true positive when the intersection over union of (IoU) the detected bounding box and the ground truth box is greater than 0.5. The IoU measure is defined as the ratio of the intersection of the detected bounding box and the ground truth (GT) bounding box and their union, see Figure 3.
Visualization of intersection over union (IoU) criteria equal to or greater than 50%.
Since the confidence threshold controls the tradeoff between recall and precision, Average Precision (AP) measure is frequently used to evaluate the performance of the detectors. The AP is the area below the precision-recall curve which is calculated for every class by varying the confidence threshold. To get the mean Average Precision (mAP) value, mean of AP values of all classes is calculated.
Since there is no single measure that can uniquely describe the complex behavior of trackers, several measures are used to evaluate the tracking performance. These measures are the number of identity switches (ID), identification precision (IDP), identification recall (IDR) and the identification F1 (IDF1) measures [10].
An identity switch occurs when an object that was assigned an ID j in previous frames, gets a new id k, k ≠ j in a subsequent frame. The IDF1 measure focuses on how long a target is correctly identified, regardless of the number of mismatches. It is the ratio of correctly identified detections over the average number of ground-truth and computed detections.
The task of object detection is to find instances of real-world objects in images or videos. A detected object is typically marked with a bounding box and labeled with a corresponding class label and classification confidence value. Thus, object detection includes both the problems of finding the location of the object on the scene and of classification for predicting the class to which the object belongs to.
In case of player detection, the object detector should be able to overcome challenging conditions such as variable number of players, different player positions, varying distance of the player from the camera, the possibility of changing shape and appearance of players in time, presence of the blur of due to the speed of the movement, occlusion, shadows of artificial and external light, as well as cluttered background.
Nowadays, the focus in object detection is on CNNs that have been extended to be able to both detect and localize individual objects on the scene. In the following subsections, two different object detectors YOLO and Mask R-CNN are described with a corresponding experiment of player and ball detection.
YOLO is a detection algorithm based on a single-stage CNN architecture that can detect multiple objects in an image in real-time. The main idea is to predict bounding boxes and confidence values for grid cells into which an image or frame is divided. In the cases when an object is spread across more than one grid cell, the holder of its prediction will be the center cell.
There have been four versions of YOLO since it was first published. In the original version, the network architecture has 24 convolutional layers with two additional fully connected layers. The purpose of the convolutional layers is the feature extraction, while for fully connected layers to calculate the bounding boxes predictions and probabilities. The bounding box predictions and class probabilities are associated with grid cells so that if an object occupies more than one cell, the center cell will be designated to be the holder of prediction for a particular object [11].
In the next version, YOLOv2 [12], five convolution layers were replaced with max-pooling layers, and instead of the fully connected layers, predefined anchor boxes are introduced. In the training phase, to define the anchor boxes, YOLOv2 uses k-means clustering on ground-truth bounding boxes where boxes translations are relative to a grid cell.
YOLOv3 [13] is the third version of the YOLO object detector. It consists of 53 convolutional layers of (3 × 3) and (1 × 1) filters with shortcut connections between layers (residual blocks) used for feature extraction. The last convolutional layer predicts the bounding boxes, the confidence scores, and the prediction class. It predicts possible bounding boxes at three different scales using a structure that is similar to feature pyramid networks. In this way three sets of boxes are predicted at each feature map cell for each scale, to improve the detection of objects of different sizes.
Player and ball detection performance of the YOLOv3 detector was tested on the handball dataset using two different models. The reference model, further marked as Y, is the pre-trained YOLOv3 model with 608 x 608 input image size with weights pre-trained on the COCO dataset and no additional training. The pre-trained model contains the person and sports ball among other classes from the COCO dataset. Transfer learning [14] was used to avoid training the models from the beginning.
The second model (YBP) was trained using transfer learning, on PBD-Handball part of the dataset. The input image resolution was increased to 1024 x 1024 from 608 x 608 of the original model and the model was trained for approximately 80 epochs. Figure 4 shows an example of detection results for the “person” class.
Player detections in handball scene with YOLOv3 (bounding boxes with confidences).
To evaluate the performance of a model, the average precision (AP) metric of both classes and the mean average precision are used and shown on Table 1.
PBD-Handball | |||
---|---|---|---|
Model | Ball AP | Person AP | mAP |
Y | 13.53 | 66.13 | 39.83 |
YPB | 35.44 | 63.77 | 49.61 |
Evaluation of the object detector.
The best results for ball detection in terms of AP were achieved with the YPB model, which was trained on additional examples for both ball and person class and had an increased input image size. A small amount of training data can significantly improve detection results as can be seen in the example of ball detection which improved for 23%. The achieved results are satisfactory given the demanding environment but are not sufficient for commercial application, so the training dataset should be increased.
Mask R-CNN [15] is a two-stage CNN that can not only detect and localize multiple objects simultaneously present in the image, but also provides a segmentation mask of the objects, that is, assigns a membership value to each of the pixels belonging to the object. The first stage of the network is a region proposal network that finds the regions of the image that are likely to contain objects (regions of interest, RoI) and proposes candidate object bounding boxes. A sliding window is applied to the feature map to examine the probability whether there is an object class or a background in the examined region. Then, bounding boxes and masks are generated with the corresponding confidence values for all possible classes.
In the second stage, there are two parallel branches of the network, a fully convolutional branch for predicting the segmentation masks and a fully connected branch used on each RoI for classification and for adjusting the proposed box size.
There are similar networks like R-CNN, Fast R-CNN, Faster R-CNN [16] on which Mask R-CNN is based to look up for object detection purpose.
The performance of the Mask R-CNN for player detection was tested on the PBD-Handball dataset using the standard Resnet-101-FPN network configuration with pre-trained parameters on the COCO dataset. For player detection experiment, only the bounding boxes that refer to the “person” class were considered.
To obtain a good balance of high detection rates and low false positive detections, detections with confidence values below a threshold experimentally set to 0.55 were discarded. The detector performance was evaluated in terms of recall, precision, F1 scores and inference time per frame (using the NVIDIA 1080ti GPU). The results and comparison with the YOLOv3 detector are shown in Table 2. Detection was considered as true positive when the intersection of the detected bounding box and the ground truth box was above 50%.
Object Detector/Measure | Inference time / frame | Recall | Precision | F1 |
---|---|---|---|---|
YOLOv3 | 0.04 s | 68% | 95% | 79% |
Mask R-CNN | 0.3 s | 76% | 98% | 85% |
Results of player detection with mask R-CNN and YOLOv3.
One handball scene with the bounding boxes, class confidence value, and segmentation masks obtained with Mask R-CNN is shown on Figure 5.
Player detections obtained with mask R-CNN in a handball scene (bounding boxes with segmentation mask and confidence value).
It can be concluded that the results of both the YoloV3 and Mask R-CNN detector are good enough to be used for further analysis of player performance. However, the YOLOv3 detector is much faster, so it can be used not only for offline analysis of recordings, but also for real-time detection, at the cost of somewhat reduced recall. The detection results could be improved if more data is used, but the performance depends on the number and size of the players on the scene, the contrast between a player and a background, illumination, etc.
Tracking of handball players in video is an example of a Multi-Object Tracking (MOT) problem, where the goal is to track both the position and the identity of multiple objects present in video, so that the same unique ID is assigned to each object in every frame it appears. In an ideal case, in every video frame, all the present players should be detected in their correct position and a unique ID for each player, that stays the same throughout the video, should be assigned. This is a difficult task as many players can be on the field, from 14 to 25, depending if it is a practice or a match, and every one of them needs to be tracked. Furthermore, players can leave and re-enter the camera field of view, move very quickly, often change directions, occlude each other and wear similar clothes, or clothes with similar color as the background [17].
Thanks to improvements in performance of object detectors, and thanks to the ability to deal with challenges such as cluttered scenes or dynamics of tracked objects, tracking-by-detection has become a leading paradigm for MOT.
When using tracking-by-detection, the tracking algorithm relies on the object detector to detect and locate the objects on the scene in each frame, while the role of the tracking algorithm itself is reduced to the problem of associating the detections across frames that belong to the same object. To do so, the tracker may use the information about bounding boxes obtained by object detection, such as their dimensions, the locations of their centroids, the relative position to the boxes in previous frames, or some visual features extracted from the image.
The Hungarian algorithm [18] solves the problem of finding the globally optimal assignment of IDs to detected player bounding boxes, with respect to some cost function that is defined for an individual assignment. Here the cost function is defined only in terms of the parameters of the bounding boxes detected by the object detector in the current and previous frame, without using any visual features extracted from the video frames. Its value depends on the Euclidean distance of each detected object’s bounding box centroid from the predicted centroid of an object in the track, and on the size difference of the bounding box and the last assigned bounding box to the same track.
Formally, the assignment cost
where
For the prediction of the centroid location
Moreover, a unique track ID is assigned to each detected bounding box whose detector confidence is higher than a set threshold during the initial assignment of bounding boxes to tracks. Afterwards, whenever the number of detected objects exceeds the number of currently active tracks, new tracks are created and initialized using the unassigned object’s bounding box.
An existing track is considered inactive when no detections are assigned to that track for several frames. Once a track is marked inactive, no further detections are added to it, so if an object later reappears or is detected again, it will get a new track ID and will be considered a new object.
Deep SORT [19] is a tracking algorithm that builds upon the Hungarian algorithm, adding the appearance information about the tracked objects into consideration when associating new detections with previously tracked objects. The appearance information is particularly useful for re-identifying players that were occluded or have temporarily left the scene. As in the previous case, a unique track ID is assigned to each bounding box within the first frame, and the Hungarian algorithm is used to assign the new detections to existing tracks so that the assignment cost function reaches the global minimum.
The cost function consists of the spatial distance
where λ is a tunable parameter that determines the relative influence of the spatial distance
The spatial distance
where
The visual distance
where
The
The appearance descriptors are extracted using a wide residual neural network comprising two convolutional layers followed by six residual blocks that output a 128-element vector, and then normalized to fit within a unit hypersphere so that the cosine distance can be used. The network was pre-trained on a person re-identification dataset of more than a million images of 1261 pedestrians. The appearance information helps with re-identification of objects that have not been tracked for some time because of missed detections, because they were under occlusion or because they have briefly left the scene.
New tracks are formed whenever there are more detections in a frame than there are existing tracks or when a detection cannot be assigned to any track, because its spatial or visual distance is too far from any existing track. The maximum allowed
The tracking of players previously detected with the YOLOv3 detector using pre-trained tiny-yolo weights and confidence threshold set to 0.5 was performed on the PT-Handball dataset with the Hungarian algorithm and Deep SORT [20].
An example of a tracking situation in sequential frames when occlusions occurred is shown in Figure 6. The numbers above the bounding boxes represent the tracking ID of each player. The shown situation is quite demanding, resulting in rather unstable and inconsistent tracking in the selected frame. The Hungarian algorithm successfully tracked one of four players, and two of the players got new IDs after occlusion, while one player has switched ID with another, so that 807 got previously existing ID 812 (Figure 6, top row). Deep SORT managed to track correctly all four players (Figure 6, bottom row).
An example of tracking situation with occlusion. Top: Hungarian algorithm, bottom: Deep SORT. The left and right frames are 1 second apart.
Since the best results were obtained with the Deep SORT algorithm, its ability to assign the correct IDs to detections is analyzed in more detail using the common MOT evaluation measures. The results are shown in Table 3.
Measure | Value |
---|---|
#tracks in the ground truth | 279 |
#tracks | 1554 |
Identity switches | 1483 |
IDF1 | 24.7% |
Performance evaluation of deep SORT.
For each player that should be tracked, the identity switches caused, 5 to 6 additional tracks on average, so there are 5 times more tracks than in ground-truth annotated data. Furthermore, a large number, precisely 1483, of identity switches are present, due to a relatively large number of players in the video that move fast, exit the camera field view, frequently change positions, and occlude each other.
The number of players that are simultaneously present in the frame obviously affects the tracking performance, and according to the IDF1 measure, the players can be correctly identified for 24,7% of the time.
Tracking mistakes can be attributed to several factors. As in all tracking-by-detection algorithms, the accuracy of tracking is greatly influenced by the accuracy of the object detector. If a player is inaccurately detected, the tracking will be inaccurate as well. Furthermore, the scale of an object, occlusion, and the similar color of the players’ clothes with the background often cause tracking problems. To overcome these problems, in further work, multiple camera systems will be investigated [21], which can also allow for a more robust generation of a top-view trajectory [22].
In Figure 7, the top row shows the problem of re-identification after occlusion, and the bottom row the problem of identity switch due to small scale and similar colors.
Problem of re-identification after occlusion.
The goal of action recognition is to infer which action takes place in a set of image or video observations. Some simple actions, like eating or cutting, could be recognized using just a single frame, but actions are mostly much more complex and take place over a period of time, so they need to be analyzed across consecutive video frames.
Here, for recognition of handball actions, a simple long short-term memory (LSTM)-based artificial recurrent neural network is used.
During the handball game, every player is moving around the field performing different actions with a ball, such as shot, jump-shot or dribbling, or without the ball, such as running or defending. Some actions are performed by more players such as passing the ball or crossing.
Unlike CNNs and other so-called feedforward neural (FFNN) networks, recurrent neural networks (RNNs) [23] have connections that feed the activations of an input in a previous time step back into the network, to influence the output for the current input. These activations from the previous time step are held potentially indefinitely in the internal state of the network, so the temporal context is not limited to a fixed window that could be used as an input to a FFNN. This property makes RNNs especially appropriate for modeling sequences, such as text or a sequence of video frames in action recognition.
Long Short-Term Memory (LSTM) [24] is a type of recurrent neural network (RNN) designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In the recurrent hidden layers, the LSTM contains special units called memory blocks. Those units contain memory cells, that have self-connections storing the temporal state of the network, and gates, which are special multiplicative units that control the flow of information. The flow of input activations into the memory cell is being controlled by the input gate, while the output gate controls the output flow of cell activations into the rest of the network. The forget gate scales the internal state of the cell before adding it as input to the cell through the self-recurrent connection of the cell, thus causing the adaptive forgetting or resetting the cell’s memory.
In the experiment handball actions from the PAR-Handball dataset are considered: Throw, Catch, Shot, Jump-shot, Running, Dribbling, Defense, Passing, Double-pass, and Crossing. An example of jump-shot action is shown in Figure 8. The action consists of a sequence of different phases of jump-shot action from running, take-off, flight, throw, and landing that are captured on different video frames.
Active player collage for jump-shoot action.
Different actions can take different amounts of time to perform, so the average number of frames in a video depends on the action class it belongs to, as shown in Figure 9.
Average number of frames per action from the handball dataset.
Only Throw and Catch actions are significantly shorter (two or three times) than the other action classes that have a duration of around 60 frames on average.
Because these two actions are also parts of the more complex ones, like Passing, the model is trained once with all 11 classes and once with 9 classes, excluding Throw and Catch.
The model selected for action recognition is a LSTM-based network with one LSTM layer with 1,024 units, followed by a dropout layer with 0.5 dropout, one fully connected layer with 512 neurons, also followed by a dropout layer with 0.5 dropout rate. and the output layer with 11 neurons.
The input to the LSTM consists of a sequence of features extracted from video frames using the InceptionV3 [25] network with the ImageNet [26] re-trained weights as the starting point. The model is trained with the Adam optimize with a learning rate of 0.00001 and decay of 10–6 for up to 100 epochs, stopping early if the validation loss does not improve for more than 20 epochs.
Different frame selection strategies and different input size of sequences from 20 to 80 were used to train the model, because actions might have most distinctive characteristics in different parts of the sequence.
In videos containing more frames than expected, the chosen number of frames were selected consecutively from either beginning, middle or the end of the video, or from the whole video by decimation, i.e., by skipping some frames at regular intervals. Conversely, copies of existing frames were inserted between frames to extend the number of frames.
The action recognition results obtained by the described LSTM model, considering the frame selection strategies and different class numbers are shown in Figure 10 in terms of validation accuracy.
Validation accuracy for different lengths of input sequences and 9 or 11 action classes.
Having to classify a smaller number of classes can generally be considered a simpler task, so, as expected, the models trained on 9 classes have better results on average than the models trained on 11 classes. However, the best result overall of 70.94% is obtained for the model with 11 classes and 45 frames taken from the middle of the sequences. This is possibly due to the overlap of some actions that make them more difficult to recognize, as the actions Throw, and Catch that are parts of other actions such as Passing, Double-pass, Crossing, Shot and Jump-shot. Closely behind with 70.55% is the model trained on 9 classes with 20 frames in the middle, and in the third place is the model trained on 9 classes with last 45 frames with 70.47% validation accuracy.
Taking into consideration only the number of input frames and ignoring the number of classes or frame selection, the best results are obtained with 45 frames followed by 20 frames. In most cases the additional frames in the sequence do not improve the result much over the models trained with 20 frames. Considering only the way the sequence is selected, the highest average accuracy of 67.69% is achieved by the model while trained on 9 classes and the last frames selection, followed by 67,05% by skipping frames.
It can be noted that regardless the strategy of selecting frames, increasing the number of input frames does not contribute to a better result. The number of frames and frame selection strategies appear to be highly dependent on the type of action being performed.
Low-level features extracted from video frames, combined with specific knowledge about the problem domain can sometimes be used for solving specific tasks and generate conclusions about the objects in the image or for scene analyzes. For example, optical flow can be used as a measure of motion in video, and for rough temporal segmentation of the input video in order to automatically cut periods of inactivity or detect intervals of repetition of a certain exercise in handball training.
If the low-level features such as optical flow or spatio-temporal interest points are used with additional information such as the detected player bounding boxes, conclusions can be drowned about the most active player on the scene and automatic detection of players that are at a certain time likely to be performing the action that is of most interest for the interpreting the scene [27].
A low-level feature that captures motion information is optical flow, which is estimated from the time-varying image intensity. A moving point on the image plane produces a 2D path
The movement of points can be estimated from consecutive video frames, using some optical flow estimation algorithm, e.g., the Lucas-Kanade method [28]. This method assumes that small sections, i.e., groups of pixels of the initial images move with the same velocity, so the result is a vector field V of velocities of each image section. At each point
A visualization of an optical flow field calculated between two video frames from the dataset is shown in Figure 11. The direction and magnitude of optical flow at each point is represented by the direction and length of each arrow.
Two consecutive frames in video and the corresponding optical flow field.
In an uninterrupted recording of a handball training, there are usually periods of repletion of a certain exercise, where all players repeat the exercise either simultaneously or taking turns, followed by short pauses where the coach explains the next exercise to be performed. The periods of higher activity, when players perform the exercises are characterized with the higher magnitude of extracted motion features from video, while the periods when players queue or wait for instruction (Figure 12) are characterized with lower magnitude of motion features [29].
A typical training situation. Two players on the right are performing the current task, while the rest are queuing.
To mark the periods of inactivity and segment the videos into sections where a single exercise is repeatedly practiced, an optical flow threshold is used. First, the optical flow field is calculated between two consecutive frames sampled each N frames (here, N = 50). Then, mean optical flow magnitude is calculated for each field, resulting in a single value for each sampled time point in video. The video is cut at time points when the mean magnitude of optical flow is lower than an experimentally determined threshold value. An example of the mean optical flow magnitude calculated for a video sequence where there were short pauses of 10–20 seconds between active repetition of an exercise was is shown in Figure 13. It can be seen that the normalized flow threshold of about 0.07 clearly separates the periods of inactivity from parts of video showing exercise.
An example of segmentation of a video sequence using optical flow magnitude.
In a typical footage of a handball game or training session, at a given time only one player or a small proportion of players present on the scene participate in the action that is currently in focus, e.g., jump-shot, passing, while others may perform actions that are not currently relevant for interpreting the situation, e.g., moving into their positions. To train the action recognition models, the actions of those players that perform the action of interest should be annotated. The annotation process is at least partly manual, so it is time-consuming and tedious given the large amounts of video data to process. To assist with annotation, a method is proposed to select among the detected and tracked players, the ones that are currently the most likely to be performing the action of interest, here called active players. Thus, instead of reviewing every single players’ activity at all times, the manual annotation is reduced to verification of only the proposed active players’ tracks.
First, the players are detected and tracked as described in previous chapters. Then, the information about player positions, i.e., the detected bounding boxes, is combined with the low-level movement features, such as optical flow or spatiotemporal interest points, to obtain a measure of each player’s activity in the considered time interval.
The optical flow-based measure of player’s activity (
An alternative feature to optical flow for defining the activity measure are spatiotemporal interest points, or STIPs. STIPs are an extension from the spatial domain into both spatial and temporal domain of the notion of interest points in images, which are points with a significant local variation of image intensities. STIPs are thus points in the image with large variation of values in both spatial and temporal directions around these points.
As for calculating the optical flow, there are several algorithms that can be used to detect the STIPs, e.g. the method presented in [30] is based on the Harris corner operator (Harris3D) that is extended into the spatiotemporal domain, [31] uses a Gaussian filter in the spatial domain and a Gabor band-pass filter in the temporal domain, the algorithm presented in [32] is based on Hessian3D derived from SURF, and the selective STIPs detector [33] is designed to specifically detect the STIPs that likely belong to persons and not to the background.
Given that movement during the performance of various sports actions causes significant variation of appearance in that part of image, it is expected that there will be more detected STIPs in image regions with more intense player’s activity [34].
An activity measure based on density of STIPs in the area near the detected player,
In the experiment here, the Harris3D detector with the default parameters was used to extract the STIPs. Figure 14 shows an example of the detected player bounding boxes and STIPs in a video frame.
Detected players and spatiotemporal interest points.
A threshold of activity measure can be used to filter active from inactive players, since the players that perform sports actions should make more sudden movements corresponding to higher activity measures than other players.
Looking at activity measures in a sequence of frames, the ranking of players’ activity can change between frames. So, to choose the players that are active throughout the sequence, the Active player score is calculated as the average activity measure of the player along the trajectory of the player’s bounding boxes. The result is a set of player trajectories with corresponding player activity scores (Figure 15).
Detected leading player (white box) and his trajectory through the whole sequence (yellow line).
In this chapter, the applications of deep learning methods on typical CV tasks such as object detection, object tracking and action recognition are presented on videos from the handball domain, recorded during training and matches.
Handball is a team sport, played with a ball, with well-defined player’s roles, goals and rules. During the game, the athletes move quickly throughout the field, change positions and roles from defensive to offensive and vice versa, use different techniques and actions, and doing so often get partially or completely occluded by another athlete, making player detection, tracking and action recognition challenging problems of ongoing research interest.
For detection, the algorithm must be able to locate an object in relation to its environment and, define that object. It is important for the detector to be as accurate and fast as possible especially if the real time detection is needed. State of the art deep learning-based detectors such as YOLOv3 and Mask R-CNN, prove to be successful for player detection, while the performance on ball detection still lags due to the combination of its small size, great speed and occlusion by the players.
Once objects such as players are detected, they can be tracked. Here, the Hungarian assignment algorithm and SORT with a deep association metric (Deep SORT) are considered for tracking. The goal of a tracker is to assign the same unique track ID to the same player in consecutive frames, which is complicated by the changes of appearance and sudden motions of players. Thus, the trackers can model this motion or the changing appearance to help the association process. The Deep SORT adds an appearance model based on deep neural network features. This appearance model allows the Deep SORT method to re-identify players that have been temporarily occluded or left the scene much more successfully than the other tested methods, making it more appropriate for use in the handball domain.
For the action recognition task, LSTM network is used, as it is suited to deal with both image information contained in a single video frame and its temporal evolution during the performance of actions. The obtained action recognition results are promising, however due to dependence of the action recognition model on the performance of previous stages, i.e. object detection and tracking, the challenge remains to improve all three tasks. As in all deep learning tasks, an important factor is gathering enough training data, which can be facilitated by methods that reduce the manual effort of labeling ground truth data. To that end, the experiments for automatic temporal segmentation of the raw footage and a method for detecting the active player in a sequence using low level visual features were presented.
Advances in deep learning methods promise continued improvement in the analysis of dynamic sports scenes, in order to recognize more complex activities, plan competitive tactics and monitor player progress.
This research was fully supported by the Croatian Science Foundation under the project IP-2016-2106-8345 “Automatic recognition of actions and activities in multimedia content from the sports domain” (RAASS) and by the University of Rijeka under the project number uniri-drustv-18-222.
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\\n\\nCorresponding Author: The Author of the Chapter who serves as a Signatory to this Agreement. The Corresponding Author acts on behalf of any other Co-Author.
\\n\\nCo-Author: All other Authors of the Chapter besides the Corresponding Author.
\\n\\nIntechOpen: IntechOpen Ltd., the Publisher of the Book.
\\n\\nBook: The publication as a collection of chapters compiled by IntechOpen including the Chapter. Chapter: The original literary work created by Corresponding Author and any Co-Author that is the subject of this Agreement.
\\n\\n2. CORRESPONDING AUTHOR'S GRANT OF RIGHTS
\\n\\n2.1 Subject to the following Article, the Corresponding Author grants and shall ensure that each Co-Author grants, to IntechOpen, during the full term of copyright and any extensions or renewals of that term the following:
\\n\\nThe aforementioned licenses shall survive the expiry or termination of this Agreement for any reason.
\\n\\n2.2 The Corresponding Author (on their own behalf and on behalf of any Co-Author) reserves the following rights to the Chapter but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Chapter as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\\n\\nThe Corresponding Author confirms that they (and any Co-Author) are and will remain a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\\n\\nSubject to the license granted above, copyright in the Chapter and all versions of it created during IntechOpen's editing process (including the published version) is retained by the Corresponding Author and any Co-Author.
\\n\\nSubject to the license granted above, the Corresponding Author and any Co-Author retains patent, trademark and other intellectual property rights to the Chapter.
\\n\\n2.3 All rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the Corresponding Author's or any Co-Author’s specific approval.
\\n\\n2.4 The Corresponding Author (on their own behalf and on behalf of each Co-Author) will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Chapter as a consequence of IntechOpen's changes to the Chapter arising from translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits.
\\n\\n3. CORRESPONDING AUTHOR'S DUTIES
\\n\\n3.1 When distributing or re-publishing the Chapter, the Corresponding Author agrees to credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen. The Corresponding Author warrants that each Co-Author will also credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Chapter.
\\n\\n3.2 When submitting the Chapter, the Corresponding Author agrees to:
\\n\\nThe Corresponding Author will be held responsible for the payment of the Open Access Publishing Fees.
\\n\\nAll payments shall be due 30 days from the date of the issued invoice. The Corresponding Author or the payer on the Corresponding Author's and Co-Authors' behalf will bear all banking and similar charges incurred.
\\n\\n3.3 The Corresponding Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Chapter worldwide for the full term of the above licenses, and shall provide to IntechOpen upon request the original copies of such consents for inspection (at IntechOpen's option) or photocopies of such consents.
\\n\\nThe Corresponding Author shall obtain written informed consent for publication from people who might recognize themselves or be identified by others (e.g. from case reports or photographs).
\\n\\n3.4 The Corresponding Author and any Co-Author shall respect confidentiality rights during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Corresponding Author and any Co-Author are confidential and are intended only for the recipient. The contents may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\\n\\n4. CORRESPONDING AUTHOR'S WARRANTY
\\n\\n4.1 The Corresponding Author represents and warrants that the Chapter does not and will not breach any applicable law or the rights of any third party and, specifically, that the Chapter contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy. The Corresponding Author warrants and represents that: (i) the Chapter is the original work of themselves and any Co-Author and is not copied wholly or substantially from any other work or material or any other source; (ii) the Chapter has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) they themselves and any Co-Author are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) they themselves and any Co-Author have not assigned and will not during the term of this Publication Agreement purport to assign any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\\n\\nThe Corresponding Author also warrants and represents that: (i) they have the full power to enter into this Publication Agreement on their own behalf and on behalf of each Co-Author; and (ii) they have the necessary rights and/or title in and to the Chapter to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licenses expressed to be granted in this Publication Agreement. If the Chapter was prepared jointly by the Corresponding Author and any Co-Author, the Corresponding Author warrants and represents that: (i) each Co-Author agrees to the submission, license and publication of the Chapter on the terms of this Publication Agreement; and (ii) they have the authority to enter into this Publication Agreement on behalf of and bind each Co-Author. The Corresponding Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each such Co-Author.
\\n\\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\\n\\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\\n\\n5. TERMINATION
\\n\\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\\n\\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\\n\\n6. INTECHOPEN’S DUTIES AND RIGHTS
\\n\\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\\n\\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\\n\\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\\n\\n7. MISCELLANEOUS
\\n\\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\\n\\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\\n\\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\\n\\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\\n\\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\\n\\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\\n\\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\\n\\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\\n\\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\\n\\nLast updated: 2020-11-27
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The Corresponding Author (acting on behalf of all Authors) and INTECHOPEN LIMITED, incorporated and registered in England and Wales with company number 11086078 and a registered office at 5 Princes Gate Court, London, United Kingdom, SW7 2QJ conclude the following Agreement regarding the publication of a Book Chapter:
\n\n1. DEFINITIONS
\n\nCorresponding Author: The Author of the Chapter who serves as a Signatory to this Agreement. The Corresponding Author acts on behalf of any other Co-Author.
\n\nCo-Author: All other Authors of the Chapter besides the Corresponding Author.
\n\nIntechOpen: IntechOpen Ltd., the Publisher of the Book.
\n\nBook: The publication as a collection of chapters compiled by IntechOpen including the Chapter. Chapter: The original literary work created by Corresponding Author and any Co-Author that is the subject of this Agreement.
\n\n2. CORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\n2.1 Subject to the following Article, the Corresponding Author grants and shall ensure that each Co-Author grants, to IntechOpen, during the full term of copyright and any extensions or renewals of that term the following:
\n\nThe aforementioned licenses shall survive the expiry or termination of this Agreement for any reason.
\n\n2.2 The Corresponding Author (on their own behalf and on behalf of any Co-Author) reserves the following rights to the Chapter but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Chapter as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Corresponding Author confirms that they (and any Co-Author) are and will remain a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Chapter and all versions of it created during IntechOpen's editing process (including the published version) is retained by the Corresponding Author and any Co-Author.
\n\nSubject to the license granted above, the Corresponding Author and any Co-Author retains patent, trademark and other intellectual property rights to the Chapter.
\n\n2.3 All rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the Corresponding Author's or any Co-Author’s specific approval.
\n\n2.4 The Corresponding Author (on their own behalf and on behalf of each Co-Author) will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Chapter as a consequence of IntechOpen's changes to the Chapter arising from translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits.
\n\n3. CORRESPONDING AUTHOR'S DUTIES
\n\n3.1 When distributing or re-publishing the Chapter, the Corresponding Author agrees to credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen. The Corresponding Author warrants that each Co-Author will also credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Chapter.
\n\n3.2 When submitting the Chapter, the Corresponding Author agrees to:
\n\nThe Corresponding Author will be held responsible for the payment of the Open Access Publishing Fees.
\n\nAll payments shall be due 30 days from the date of the issued invoice. The Corresponding Author or the payer on the Corresponding Author's and Co-Authors' behalf will bear all banking and similar charges incurred.
\n\n3.3 The Corresponding Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Chapter worldwide for the full term of the above licenses, and shall provide to IntechOpen upon request the original copies of such consents for inspection (at IntechOpen's option) or photocopies of such consents.
\n\nThe Corresponding Author shall obtain written informed consent for publication from people who might recognize themselves or be identified by others (e.g. from case reports or photographs).
\n\n3.4 The Corresponding Author and any Co-Author shall respect confidentiality rights during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Corresponding Author and any Co-Author are confidential and are intended only for the recipient. The contents may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\n4. CORRESPONDING AUTHOR'S WARRANTY
\n\n4.1 The Corresponding Author represents and warrants that the Chapter does not and will not breach any applicable law or the rights of any third party and, specifically, that the Chapter contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy. The Corresponding Author warrants and represents that: (i) the Chapter is the original work of themselves and any Co-Author and is not copied wholly or substantially from any other work or material or any other source; (ii) the Chapter has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) they themselves and any Co-Author are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) they themselves and any Co-Author have not assigned and will not during the term of this Publication Agreement purport to assign any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Corresponding Author also warrants and represents that: (i) they have the full power to enter into this Publication Agreement on their own behalf and on behalf of each Co-Author; and (ii) they have the necessary rights and/or title in and to the Chapter to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licenses expressed to be granted in this Publication Agreement. If the Chapter was prepared jointly by the Corresponding Author and any Co-Author, the Corresponding Author warrants and represents that: (i) each Co-Author agrees to the submission, license and publication of the Chapter on the terms of this Publication Agreement; and (ii) they have the authority to enter into this Publication Agreement on behalf of and bind each Co-Author. The Corresponding Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each such Co-Author.
\n\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\n\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\n5. TERMINATION
\n\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\n\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\n\n6. INTECHOPEN’S DUTIES AND RIGHTS
\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\n\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\n7. MISCELLANEOUS
\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\n\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\n\nLast updated: 2020-11-27
\n\n\n\n
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{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. 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Many studies have shown their applications in various fields, specifically in photocatalysis. There are different methods to synthesis of rare-earth nanostructures. In this study, we discuss about modification of rare-earth-based materials. Also production methods and their advantages and disadvantages have been presented, briefly. Finally, photocatalytic applications of rare-earth nanostructures are highlighted.",book:{id:"7671",slug:"concepts-of-semiconductor-photocatalysis",title:"Concepts of Semiconductor Photocatalysis",fullTitle:"Concepts of Semiconductor Photocatalysis"},signatures:"Sahar Zinatloo-Ajabshir and Zahra Sayyar",authors:[{id:"292583",title:"Dr.",name:"Sahar",middleName:null,surname:"Zinatloo_Ajabshir",slug:"sahar-zinatloo_ajabshir",fullName:"Sahar Zinatloo_Ajabshir"},{id:"296400",title:"Dr.",name:"Zahra",middleName:null,surname:"Sayyar",slug:"zahra-sayyar",fullName:"Zahra Sayyar"}]},{id:"67697",title:"TiO2 Nanoparticles Supported on Hierarchical Meso/Macroporous SiO2 Spheres for Photocatalytic Applications",slug:"tio-sub-2-sub-nanoparticles-supported-on-hierarchical-meso-macroporous-sio-sub-2-sub-spheres-for-pho",totalDownloads:925,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"Supporting a photocatalyst, such as titania nanoparticles (TiO2 NPs), is a good strategy to improve its performance since it can facilitate the photocatalyst recovery from the aqueous media and provides a high surface area for pollutant adsorption. Among the several advanced functional materials used as TiO2 NP support, the hierarchical meso/macroporous SiO2 spheres not only show the advantages associated to its chemical nature but also the dendritic fibrous structure provides a porous network that offers many benefits to be exploited in optical and catalytic devices. In this chapter, different synthetic approaches to design hierarchical meso/macroporous silica and the strategies to support TiO2 NPs regarding the photocatalytic performance of these materials are shown.",book:{id:"7671",slug:"concepts-of-semiconductor-photocatalysis",title:"Concepts of Semiconductor Photocatalysis",fullTitle:"Concepts of Semiconductor Photocatalysis"},signatures:"Keyla M. Fuentes, Margarita Sánchez-Dominguez and Sara A. Bilmes",authors:[{id:"93593",title:"Dr.",name:"Margarita",middleName:null,surname:"Sanchez-Dominguez",slug:"margarita-sanchez-dominguez",fullName:"Margarita Sanchez-Dominguez"},{id:"290978",title:"Ph.D.",name:"Keyla M.",middleName:null,surname:"Fuentes",slug:"keyla-m.-fuentes",fullName:"Keyla M. Fuentes"},{id:"300173",title:"Prof.",name:"Sara",middleName:null,surname:"Aldabe Bilmes",slug:"sara-aldabe-bilmes",fullName:"Sara Aldabe Bilmes"}]},{id:"64785",title:"Effect of Annealing on Metal-Oxide Nanocluster",slug:"effect-of-annealing-on-metal-oxide-nanocluster",totalDownloads:793,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"Recently, the development of optoelectronic devices based on metal-oxide nanocluster has attracted intensive research interest. Nanoclusters are suitable for these because of their large surface-to-volume ratio and the presence of abundant oxygen vacancies or trap states. Metal–oxides such as ZnO, In2O3, and TiO2 synthesized using different technique produces high surface area films consisting of clusters and provides complete control over the film morphology. In this chapter, some of the metal oxides nanocluster film has investigated, and the effect of annealing on the structural, optical and electrical properties of the grown films when subjected to different annealing temperatures will be studied. Theoretically, these properties are presumed to improve after the heat treatment as the crystallinity, and the grain size of the film has increased due to the diminishing of oxygen vacancies. Thus, the greater surface-to-volume ratio, the better stoichiometry and higher level of crystallinity compared to bulk materials make nanocluster-based devices very promising for the mentioned application.",book:{id:"7671",slug:"concepts-of-semiconductor-photocatalysis",title:"Concepts of Semiconductor Photocatalysis",fullTitle:"Concepts of Semiconductor Photocatalysis"},signatures:"Naorem Khelchand Singh and Rajshree Rajkumari",authors:[{id:"263847",title:"Dr.",name:"Naorem Khelchand",middleName:null,surname:"Singh",slug:"naorem-khelchand-singh",fullName:"Naorem Khelchand Singh"},{id:"276260",title:"Ms.",name:"Rajshree",middleName:null,surname:"Rajkumari",slug:"rajshree-rajkumari",fullName:"Rajshree Rajkumari"}]}],onlineFirstChaptersFilter:{topicId:"958",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:16,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:4,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"June 25th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!1,annualVolume:null,editor:null,editorTwo:null,editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:54,paginationItems:[{id:"81595",title:"Prosthetic Concepts in Dental Implantology",doi:"10.5772/intechopen.104725",signatures:"Ivica Pelivan",slug:"prosthetic-concepts-in-dental-implantology",totalDownloads:22,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Concepts in Dental Implantology - From Science to Clinical Research",coverURL:"https://cdn.intechopen.com/books/images_new/10808.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80963",title:"Pain Perception in Patients Treated with Ligating/Self-Ligating Brackets versus Patients Treated with Aligners",doi:"10.5772/intechopen.102796",signatures:"Farid Bourzgui, Rania Fastani, Salwa Khairat, Samir Diouny, Mohamed El Had, Zineb Serhier and Mohamed Bennani Othmani",slug:"pain-perception-in-patients-treated-with-ligating-self-ligating-brackets-versus-patients-treated-wit",totalDownloads:21,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Trends in Orthodontics",coverURL:"https://cdn.intechopen.com/books/images_new/10780.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80964",title:"Upper Airway Expansion in Disabled Children",doi:"10.5772/intechopen.102830",signatures:"David Andrade, Joana Andrade, Maria-João Palha, Cristina Areias, Paula Macedo, Ana Norton, Miguel Palha, Lurdes Morais, Dóris Rocha Ruiz and Sônia Groisman",slug:"upper-airway-expansion-in-disabled-children",totalDownloads:35,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"80839",title:"Herbs and Oral Health",doi:"10.5772/intechopen.103715",signatures:"Zuhair S. Natto",slug:"herbs-and-oral-health",totalDownloads:55,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg",subseries:{id:"1",title:"Oral Health"}}}]},overviewPagePublishedBooks:{paginationCount:8,paginationItems:[{type:"book",id:"6668",title:"Dental Caries",subtitle:"Diagnosis, Prevention and Management",coverURL:"https://cdn.intechopen.com/books/images_new/6668.jpg",slug:"dental-caries-diagnosis-prevention-and-management",publishedDate:"September 19th 2018",editedByType:"Edited by",bookSignature:"Zühre Akarslan",hash:"b0f7667770a391f772726c3013c1b9ba",volumeInSeries:1,fullTitle:"Dental Caries - Diagnosis, Prevention and Management",editors:[{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",institutionURL:null,country:{name:"Turkey"}}}]},{type:"book",id:"7139",title:"Current Approaches in Orthodontics",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",editedByType:"Edited by",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",hash:"2c77384eeb748cf05a898d65b9dcb48a",volumeInSeries:2,fullTitle:"Current Approaches in Orthodontics",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null}]},{type:"book",id:"7572",title:"Trauma in Dentistry",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7572.jpg",slug:"trauma-in-dentistry",publishedDate:"July 3rd 2019",editedByType:"Edited by",bookSignature:"Serdar Gözler",hash:"7cb94732cfb315f8d1e70ebf500eb8a9",volumeInSeries:3,fullTitle:"Trauma in Dentistry",editors:[{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. 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His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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