Overview of flash-pumped Q-switched 2 micron solid-state lasers.
\r\n\tEqually important are the consequences deriving from the extraordinary nature of the present times. The COVID-19 pandemic and the restrictive measures to contain the infection (lockdown and "physical distancing" in primis) have revolutionized the lives, and a distortion/modification of habits, rhythms, arrangements will continue to be necessary.
\r\n\tGovernments have implemented a series of actions to mitigate the spread of infections and alleviate the consequent pressure on the hospital system. On the other hand, the Covid-19 pandemic has caused a series of other cascading effects that will probably be much more difficult to mitigate and which expose to complex consequences. The past two years have brought many challenges, particularly for healthcare professionals, students, family members of COVID-19 patients, people with mental disorders, the frail, the elderly, and more generally those in disadvantaged socio-economic conditions, and workers whose livelihoods have been threatened. Indeed, the substantial economic impact of the pandemic may hinder progress towards economic growth as well as progress towards social inclusion and mental well-being.
\r\n\t
\r\n\tAlthough in all countries the knowledge on the impact of the pandemic on mental health is still limited and mostly derived from experiences only partially comparable to the current epidemic, such as those referring to the SARS or Ebola epidemics, it is likely that the demand for intervention it will increase significantly in the coming months and years. The extraordinary growth of scientific research in the field of neuroscience now offers the possibility of a new perspective on the relationship between mind and brain and generates new scenarios in understanding the long wave of the pandemic and in the prospects for treatment. Moreover, the pandemic also has led to opportunities to implement remote monitoring and management interventions.
\r\n\t
\r\n\tOverall this volume will address the complex relationship existing between COVID-19, mental health, acquired knowledge, and possible interventions taking a highly multidisciplinary approach; from physiological and psychobiological mechanisms, and neuromodulation through medical treatment, psychosocial interventions, and self-management.
Artificial neural networks have become a widely used tool in several air pollution and meteorological applications. Yi and Prybutok (1996) used MPNN for surface ozone predictions, as well as Comrie (1997). Several prediction models were also made for other pollutants; for instance for SO2 (Božnar et al., 1993) and for CO (Moseholm et al., 1996). Marzban & Stumpf (1996) used MPNN for predicting the existence of tornadoes.
\n\t\t\tA review article by Gardner (1998) described a variety of applications, mainly in the field of air pollution forecasting and pattern classification. Though the number of applications is growing, especially in recent years, no special attention has been paid to the principles of artificial neural network usage in environmental applications.
\n\t\t\tOur group first established a method for short term forecasting of SO2 concentrations on the basis of a multilayer perceptron neural network (Božnar et al, 1993), but in the following years we use an artificial neural networks in several other applications that differ very much each another.
\n\t\t\tIn this article we intend to show examples of a variety of applications of artificial neural networks in air pollution and the meteorological field. Examples are taken from our past experience, extending over a decade.
\n\t\t\tSeveral applications in this field start from fundamentals and too much attention is paid to optimization and speeding up of the learning algorithms. From our experience this should be a minor problem for an environmental modeller and does not significantly affect the final model quality if modern tools are used. In the process of model construction other factors are much more crucial – such as feature determination, pattern selection, and learning process optimization. These are the methods that are derived from the basic principle of artificial neural networks – that is the ability to learn information from given examples.
\n\t\t\tIn this article we intend to show some solutions for the effective transformation of measured information into air pollution and meteorological models. We hope that the variety of examples will inspire new applications and methods that will serve the air pollution modelling community. The mystique of artificial neural networks, derived directly from their name, prevents many modellers from using them. It is the purpose of this article to demystify this useful mathematical tool and in this way encourage its usage.
\n\t\tArtificial neural networks can be divided into several groups according to their topology. The tool was firstly widely used in the pattern recognition field. The topologies vary from feed forward neural networks with several hidden layers, to topologies with backward loops that make the result sequence dependent, to fuzzy logic and several automatic sorting tools. A detailed explanation of this groups is far beyond the scope of this article. The reader interested in this issue can get information from several books (Lawrence, 1991).
\n\t\t\tIn this article we focus on two main “species” of artificial neural networks that can cover a huge variety of air pollution and meteorological modelling applications. The two selected are the Multilayer Perceptron artificial Neural Network (MPNN) and the Kohonen neural network (KNN). Both can be replaced by other artificial neural networks for the same purpose, but this does not change the method of using these tools. In this article MPNN and KNN can both be treated as one of the best possible solutions. The authors of this article have no intention to argue about the qualifications of other topologies.
\n\t\t\tIn this article it will be shown what the most suitable applications of MPNN and KNN are. The latter is not so widely used although it has great potential in environmental problems.
\n\t\t\tMPNN is mathematically speaking a universal approximator (Hornik, 1991; Kurkova, 1992). It can reconstruct arbitrary multivariable and highly non-linear functions. Therefore it is a suitable tool for modelling atmospheric phenomena whose behaviour has not yet been described by formulas but is only known from measured examples.
\n\t\t\tKNN, on the other hand, is a structure capable of sorting a multitude of multivariable samples or patterns into groups of similar ones. It is important that it can find these groups without a teacher – so-called unsupervised learning. This ability becomes extremely important when dealing with multivariable patterns where similarity rules are not obvious.
\n\t\tThe structure of MPNN was introduced by Rumelhart (1986). It is one of the basic neural network structures from which several others were derived.
\n\t\t\tThe basic element of the MPNN is a neuron. Several neurons are organized into layers – input, hidden (one or more) and output layer. Each neuron has a simple structure that mimics the functionality of the neuron found in animals and the whole structure of layers mimics the brain structure. This similarity gives rise to the name. Each neuron firstly summarizes the weighted input values and then passes the sum through the transfer function. If the transfer function is nonlinear, such as a basic sigmoid function or hyperbolic tangent, then the whole structure acquires its great ability as an universal approximator.
\n\t\t\tThe neurons in the input layer take the values from the model input variables and pass the values to the neurons in the hidden layer, the hidden layer neurons pass the values to the higher hidden layers and finally to the output layer that gives the model output value. The output of each neuron is passed to the input of all neurons in the next higher layer. All the connections between neurons are weighted. These interconnection weights are the basic parameters of the model that are adjusted during the learning process.
\n\t\t\tModel inputs take their values from the input features – measured parameters that determine the output of the model. Model output(s) represents the phenomenon that is being reconstructed (approximated). Outputs are called output features.
\n\t\t\tThe values of one particular realization of all inputs is called the input vector, and the model outputs values form the output vector. Both vectors together form a pattern. A pattern is therefore like one dot in the multivariable space lying on the surface of the function the model is approximating.
\n\t\t\tThe whole idea of constructing a model to approximate a multivariable function is the following: Firstly enough patterns should be available (for instance from the measurements) with known input and output features. These patterns should be uniformly spread over the whole investigated domain. Then the model topology is designed according to the number of input and output features. The model learning stage consists of several adjustments of model interconnection weights – in order to minimize the average error between the actual measured output values and the output values that are produced by the neural network. One of the algorithms that can be used for this purpose is the backpropagation algorithm. In the process of learning the MPNN takes the information (about phenomenon under investigation) that is available in the learning patterns and when learning is completed (the model constructed) it can give the results for previously unknown patterns – where only input values are presented to the network. This is possible if there were similar patterns (to the unknown pattern) in the learning set. This is the so-called generalizing capability of the MPNN. The similarity is mathematically speaking the distance between two patterns.
\n\t\t\tThe basic rule of MPNN model construction is therefore to provide information rich learning patterns.
\n\t\t\tThere are some basic steps and methods that should be used in the model construction process to obtain effective models. These steps will be summarized in the following paragraphs and their practical use is shown in the exemplary applications that follow this section.
\n\t\t\tThe structure of a feedforward multilayer perceprtron neural network
Node (artificial neuron or perceptron)
Feature determination should be done in order to properly define the modelled domain (independent variables), to enable all important information to be captured, to simplify MPNN and therefore achieve more effective learning, to reduce the number of learning patterns needed and to increase the probability of finding the global minimum of the error function during learning.
\n\t\t\t\tFirstly the modeller should determine what the desired output of the model is. This can be one or several parameters that can be measured or calculated. These are the output features. For several output features it is usually more effective to establish one model for each feature than one model for all. Then the input features should be determined from several other measured parameters that represent the possible variables that cause or influence the output parameter. Input features are the ones that have significant influence on the outputs. Feature determination can be done heuristically (using expert knowledge about the phenomenon under investigation) or using other methods (feature reduction that can be extraction or selection (Devijver, 1982); examples of selection are contribution factors or Saliency metrics techniques). In both the latter methods basically the model is firstly trained with all available features and the higher absolute values of interconnection weights reveal the more important input features.
\n\t\t\t\tIt is extremely important that the feature determination process should not be based on a linear method. Most of the processes in the atmosphere over complex terrain are not linearly dependent on each other. Therefore if the input features are chosen from the possible input measurements by a linear criterion (for instance calculation of the linear correlation factor between the examined input measurement and output modelled parameter), then most probably the important ones are rejected. An MPNN has the very important ability of being able to simulate highly non-linear dependencies and the modeller should obtain the most advantage out of this. The above mentioned contribution factors and Saliency metrics techniques both allow highly-nonlinear relationships to be found.
\n\t\t\tThe data base of the measurements (values of input and output features for several situations – for instance for several measuring intervals) form the data base of patterns. It should be divided into several sets (training, testing, production, on-line, remaining). The training set is used to adjust the interconnection weights of the MPNN model. The testing set is used periodically during the learning process to test the model’s generalizing capabilities – this is optimization during learning. The final model is the one that gives the best results on the testing set. In this way we prevent the model from becoming too dependent on known patterns and therefore losing the generalizing capabilities. The training and testing sets together form the learning set. A third set different from the previous ones is the production set. This set is used for model verification to determine its expected error. All three sets should have known input and output vectors. When the model has been tained, it can be used on patterns with unknown output values. This set of patterns is the on-line set – when a newly measured situation arises, the model gives us an answer.
\n\t\t\tOnly patterns with valuable information should be put into the learning set, while others are rejected and form the remaining set. The pattern selection can be done either heuristically or a Kohonen neural network can be used to sort patterns into groups and in this way the KNN shows which ones are more important. The main goal of pattern selection techniques is to select patterns over the whole of the modelled domain. These patterns should contain all the information about the studied phenomenon. Patterns selected for the training and testing set should represent all important but usually rare situations that may appear during the further use of the model. Just having a lot of patterns that are the most common, but do not represent the rare complicated situations, is certainly not enough for an effective model.
\n\t\t\tThe topology (number of neurons in the input, hidden and output layers) is determined from the number of features and the number of patterns. Input and output features determine the number of neurons in the input and output layers. The number of neurons in the hidden layer(s) is usually determined as the number of inputs divided by two plus the square root of the number of patterns. There is no rule for a perfect solution – the user should acquire some experience.
\n\t\t\tAfter the topology has been determined and the patterns prepared, a training algorithm (for instance a backpropagation algorithm) should be used to determine the model’s interconnection weights. Basically the algorithms have parameters that determine the speed of learning. Learning is a process of finding the global minimum of the error function. If during the learning process we move in big steps, the model cannot reach the bottom of the minimum function, but escapes quickly to other local minima. If the steps are too small, the model can be stuck in a local minimum far from the global one. During the learning process, the network should be periodically tested on the testing set (not included in the training set) to prevent overtraining. At the end the model is the network giving the best results on the optimization – testing set. This is an optimizing process that finds the network with best generalizing capabilities instead of the best memorizing capabilities. Learning speed determination and optimization are usually far more important for successful learning than having a slightly better or worse algorithm.
\n\t\t\tWhen the model is trained, it should always be validated on the production set to determine the expected error in further on-line use. To obtain a fair judgement of the model’s abilities, the patterns that form the production set for validation should not be presented to the model in the training or testing set at all.
\n\t\t\t\tThe training, testing and production sets should reflect all the situations that can arise in the on-line use of the model.
\n\t\t\t\tFeature determination and pattern selection are therefore the most crucial steps in model construction and usually determine the model’s abilities.
\n\t\t\tFirst let us use the MPNN as a basis for short term ambient SO2 concentration forecasting. As an example for study the area around the Šoštanj Thermal Power Plant in Slovenia was used. The studied domain of 30 by 30 km with the TPP in the centre lies in very complex terrain – a basin surrounded by several hills that are cut by valleys. The area is characterized by very low wind speeds, frequent calm situations and thermal inversions in winter that cause severe air pollution. The whole of the studied area is covered by 6 ambient automatic measuring stations (measuring basic meteorological parameters like wind, air temperature, relative humidity and precipitation and pollutant concentrations) and emission stations in the TPP. All the stations collect data every half hour.
\n\t\t\tThe idea was to test the forecasting abilities of the new MPNN tool. Low winds and quick wind changes in the area cause severe air pollution peaks of very short duration (only a few intervals). We tried to establish a model that would forecast the SO2 concentration for the following half hour from the data available for present or past intervals (air pollution and meteorological measurements). The task was a difficult one, because the work was concentrated on rapid warning of short but severe SO2 peaks and on not causing false alarms.
\n\t\t\tThe data base of measurements was huge in all dimensions. There were over 50 parameters that were measured every half hour and several years of data were available for analysis (one year consists of over 17000 half hour intervals). It is obvious that all the data could not be simply used together because of the computational space and time problems (this was at the beginning of the PC era) and more importantly because the patterns with less information would prevail over the sparse patterns carrying crucial information. The same is valid for the different measured parameters that are the possible inputs to the model. This huge data base forced us to establish methods for feature determination and pattern selection. The idea was to find patterns that carry most of the available information and to determine which measurements influence the modeled ambient SO2 concentration at a chosen station. It is very important to stress that we were seeking for highly non-linear dependencies that the MPNN is able to model.
\n\t\t\tThe whole procedure of feature determination and pattern selection techniques is explained in detail in several our publications (Božnar, 1997; Mlakar, 1997; Mlakar & Božnar, 1996; Božnar et. al, 1993; Božnar & Mlakar, 2001).
\n\t\t\tThis approach resulted in a model for a chosen station that used around 15 input measurements from that and other stations to forecast the local SO2 air pollution. The model was trained with small (in comparison to the huge data base available) data sets of chosen patterns. This resulted in a significant improvement of model forecasting ability.
\n\t\t\tIt is also important that the usual cost functions (linear correlation coefficient, mean square error, …) are not suitable for forecasting problems where most of the time nothing out of the ordinary is happening, but when the peak of concentration comes, it is severe and short. It was very easy to obtain very good values of the above mentioned cost functions – but this does not tell anything about the real model capabilities (if it really correctly and on-time predicts the coming SO2 peak). Therefore we defined a new cost function termed p6 (Mlakar, 1997). This is the probability of successful forecasting of a high concentration without causing false alarms. It is a very sharp cost function that clearly distinguishes good models from the ones that are in the range of naive predictors.
\n\t\t\tIn the process of SO2 modelling it was clearly proven that feature determination and pattern selection techniques influence the final model performance much more than the training algorithms and other details of the establishment of the model. This is caused by the fact that the information carried in the features and patterns of the available data set should be presented to the model in the learning phase in a “model understandable” way. To generalize this principle it can be stated that an understandable way is similar to a humanly understandable way. People also cannot learn effectively if the informative and key examples are hidden in large quantity of useless examples.
\n\t\tA model for ozone forecasting was established for the city of Nova Gorica in Slovenia close to the Adriatic sea (Grašič, 2006). During the hot summer period high ozone episodes are often recorded. The idea of constructing the model is to have information about the ozone pollution peak of the following day already available in the evening of the day before 19:00. That would allow the population sensitive to ozone to plan their activities for the following day.
\n\t\t\tSlovene legislation defines warning values for a one hour average ozone concentration and for eight hour moving average values. We concentrated our research on determination of the maximum hourly value of ozone concentration of the following day. Ozone peaks usually occur during the midday period, therefore the task deals with forecasting cca 17 hours in advance.
\n\t\t\tThe available data were measurements from a local air pollution measuring station (SO2, O3, NO, NO2, CO, VOC) that also measures ground level meteorological parameters (wind, air temperature, relative humidity, air pressure and global solar radiation). In principle in the evening meteorological forcasts are available for the city of Nova Gorica. Of these values two are more reliable – the maximum daily air temperature and the average wind speed and direction for the following midday. For the purpose of establishing the model from the historical data base, actual measurements of these two parameters on the following days were taken instead of prognostic values.
\n\t\t\tA two year data base was available for model construction and verification. In this case only one pattern per day is available. Therefore two years data give cca 700 patterns only. Out of this data base one winter and one summer month were excluded (were not used in the learning process at all) for independent model verification.
\n\t\t\tBecause of the small data base available for learning it was only divided into a randomly taken group of 10% for testing (optimising) and the remaining 90% used for adjustment of the model\'s weights (training). No other pattern selection was performed.
\n\t\t\tFeature selection was done in two steps. Firstly a wide selection of possible input features was made using chemical knowledge about ozone formation and other related processes. Then this wide range was narrowed using contribution factors. The finally selected input features were air temperature, global solar radiation, NO, NO2, NOx, CO, O3 all as 24h average values calculated at 19:00 on the previous day, prognostic vector wind speed, sine of wind direction and maximal hourly air temperature for the day of prediction (all three prognostic values were taken from the available measured data base).
\n\t\t\tThe verification of the model for approximately two months not used in the learning process showed that the model has a good performance. For final judgement, a longer verification period would be necessary. It is also expected that its performace would be slightly worse if actual meteorological prognostic model predictions were taken instead of real measurements (for the last three features).
\n\t\tAir pollution prediction was the first but not the only field where we successfully constructed MPNN based models.
\n\t\t\tRecently we encountered the problem of missing ground level wind data on the location of a planned industrial plant. The time available for the task was short and therefore it was not possible to perform one year of measurements, and only 6 weeks of measurements were available. The location was again in the complex terrain of Zasavje, Slovenia. Study of the winds in the area clearly show that ground level wind reconstruction from global prognostic meteorological models would not be useful because of the orographic complexity of the area.
\n\t\t\tBut there are six existing meteorological stations in the area on sited from 2 to 10 km from the planned location. None of these locations has the same characteristics as the new location, so their data could not be used directly.
\n\t\t\tOur idea was to reconstruct one year of ground level wind data on the new location from one year of wind data at the old station locations. This is a very suitable task for a MPNN based model. The six weeks data base when wind measurements were available at both old and new locations was used to train and verify the model.
\n\t\t\tIn contrast to the SO2 forecasting problem, this problem again has a small data base consisting of 6 weeks of half hour average values of wind speed and direction measurements at 7 locations. Therefore only the last week of measurements was reserved for final model verification and was not used for model learning. The remaining five week data base was again divided into a randomly taken 10% test set for optimization and 90% for training.
\n\t\t\tFor every station vector and scalar half hour average values and maximum values of wind speed were available, as well as wind direction. The vectors were also decomposed into cosine and sine components. The decomposition into cosine and sine components is a trick that should be used whenever we have a measurement of circular nature (such as azimuth angle or hour within a day). All these measurements and their combination at the old stations locations are candidates for model input features.
\n\t\t\tFirstly a heuristic feature selection was performed by simply comparing the similarity of wind roses for the new and old locations. Then the final feature selection was repeated using the contribution factors technique.
\n\t\t\tThe results of the model verification show better results than expected, considering the high complexity of the area studied.
\n\t\t\tThe reconstructed wind speed mean absolute error at the new location was less than0.4 m/s, the mean squared error 0.45 m/s and the linear correlation coefficient 0.84. The average absolute error for the wind direction was 35 degrees over the whole verification data set (which contains a lot of very low wind speeds and calms) and as little as15.5 degrees if only cases with a wind speed over 3 m/s were examined.
\n\t\tWe successfully applied MPNN in the following meteorological problems that will be only shortly explained:
\n\t\t\treconstruction of SODAR measurements,
short term forecasting of ground level wind,
reconstruction of diffuse solar radiation,
correction of long wave solar radiation measurements.
SODAR measurements are crucial for modern numerical Lagrangean particle models used for short scale air pollution reconstruction over complex terrain. But SODAR measurements are not always available. SO2 air pollution was studied in detail (fourth chapter of this article) in the Šoštanj area of Slovenia. In the Šoštanj basin SODAR measurements were available only for an aproximately two month period during a measuring campaign (Elisei et. al, 1992). The area of the basin and surrounding hills is well covered with ground level wind measuring stations.
\n\t\t\t\tWe made a MPNN based model to see whether it was possible to reconstruct SODAR upper layer (not ground level) measurements from the measurements at other stations. A test model was made for the level 50m above the ground. The results were quite good (comparable to the Trbovlje wind reconstruction). Some details can be found in paper by Božnar and Mlakar (1995).
\n\t\t\tIn the same area around Šoštanj short term ground level wind forecasts would also be very useful as an input to an SO2 concentration forecasting model. Forecasts of wind changes for the next few half hour intervals are more dependent on local thermal and solar radiation changes than on the movement of global fronts. Due to terrain complexity again such forecasts cannot be derived from regional prognostic meteorological models, because they operate in too sparse (time and space) coordinates.
\n\t\t\t\tWe constructed a model for ground level wind forecasting for one of the stations in the Šoštanj region. The forecast was made for one averaging interval in advance. The input features were ground level wind measurements from the studied station and from two other stations for the current time interval. For wind speed one interval in the past was also used. The results were very good for wind speed and acceptable for wind direction prediction. Some details can be found in paper by Božnar and Mlakar (1995).
\n\t\t\tOur colleagues from Sao Paulo, Brazil made extensive research on the measurement of and construction of correlation based models for diffuse solar radiation in the Sao Paulo urban area (Oliveira et. al, 2002). The diffuse solar radiation component requires expensive measuring procedures in comparison to other basic meteorological measurements, including global and long wave solar radiation. Therefore it would be useful for many purposes if the diffuse solar radiation component could be reconstructed from other simpler meteorological measurements. An MPNN-based model was constructed for this purpose that gives significantly better results than previously available models. Details can be found in paper by Soares et. al, (2004).
\n\t\t\t\tAnother problem arose from this work – correction of long wave measurements according to the Fairall formula (Fairall et al, 1998). This correction requires additional measurements of the temperature of the long wave sensor’s dome and base. There exist several years of long wave solar radiation measurements for Sao Paulo but without the required additional measurements for correction. We solved the problem by several months measurements of the missing parameters and then establishing a MPNN-based model for reconstruction of the Fairall correction from the basic meteorological measurements that are available for several years (Oliveira, 2006). The model again gave very good results.
\n\t\t\t\tIn both the above explained models, feature determination and pattern selection techniques were applied in the model construction phase.
\n\t\t\tThe Kohonen neural network (KNN) (Kohonen, 1995) differs significantly from the MPNN. The main purpose of KNN is to sort multivariable patterns into groups (clusters) of similar ones. It is important that the grouping criteria need not be known – therefore this is unsupervised learning.
\n\t\t\tKNN is a very practical and effective tool for finding groups of similar patterns in data sets where it is not known in advance (through some other available knowledge) what their natural division into groups of similar patterns is.
\n\t\t\tThe sorting principle is as follows: firstly the user prepares a data set of multivariable patterns that should be searched for groups of similar ones. The pattern consists of input features (the same definition as in MPNN). The output feature is the number of the cluster that the pattern belongs to. The quantity of clusters should be determined by the user. The natural number of clusters (the number of clusters that best fits the examined problem) cannot be determined automatically. But there is a relatively simple way of finding it. The process of dividing data set into groups is repeated for several different quantities of groups. For each division the average standard deviation of the distance of all patterns from the corresponding centre of the group should be calculated. On increasing the number of groups, the standard deviation decreases rapidly until the natural number of groups is reached. After that, if we divide these groups into more groups, the standard deviation decreases significantly slower than before. Using this rule, the “natural” number of groups can be easily derived from a graph of the average standard deviation of the distance versus the number of groups.
\n\t\t\tThe crucial part of sorting is selection of the measure of distance appropriate to the problem examined. In most cases the Euclidean distance between two vectors can be used. But it should be noted that if the components of the vector represent measurements of different natural processes, then each process should be normalized. If this is not done, some components may prevail over others. Beside Euclidean distance, many other distance measures that are known from pattern recognition theory can also be used.
\n\t\t\tIn the iterative process when KNN sorts the available data set of patterns into a chosen number of groups, it actually puts together patterns that are close one to another in terms of the distance function used. The algorithm is again an iterative one and the user can stop the process of division when the groups become stable.
\n\t\t\tPresentation of wind roses for all clusters for the division into 10 clusters
Feature determination is also an important process when using KNN. In this case feature selection means that the user should find the inputs that can provide some information about how a particular pattern differs from other ones. With KNN, feature determination is mostly done heuristically according to the user’s knowledge about the examined phenomenon.
\n\t\t\tKNN is an extremely useful sorting tool for problems dealing with huge data bases and multivariable patterns.
\n\t\t\tIn the following paragraphs two successful problems that we solved using KNN will be presented.
\n\t\tThe Šoštanj area (a basin in complex terrain, explained in previous sections) has very colorful ground level wind field patterns due to the fact that the very low winds that prevail there meander in the basin and follow the shape of nearby lying valleys and become stronger over the hills and passes. Hence the wind roses of six ambient automatic measuring stations (that were examined for SO2 forecasting) look totally different, in spite of the fact that they are only a few kilometers away one from another. This fact illustrates the complexity of the ground level winds in the area.
\n\t\t\tWe examined the following problem (Mlakar & Božnar, 1996): is it possible to find groups of similar wind fields (a wind field in this case is represented by a packet of simultaneously measured wind data at all stations) occurring in this area, or is the problem too stochastic to be grouped? If there are groups, what is the “natural” number of groups there?
\n\t\t\tTo answer these questions we examined the wind data for five stations. One station was excluded because its location was inappropriate for representative wind measurements.
\n\t\t\tThe data base of over 26000 half hour intervals was examined when wind measurements were available for all five stations. Due to the complexity of the wind roses for the whole data set it was expected that the natural number of groups would be very large. Several divisions from 10 to 100 groups were tested. As a measure of division quality a special index was defined – the weighted sum of the standard deviation of wind speed and the standard deviation of wind direction within the obtained groups. The natural number of groups was found to be around 32.
\n\t\t\tThe quality of the division of the 26000 wind patterns into 32 groups was easily controlled by plotting wind roses for the new groups for each station. The new wind roses were not similar to the wind roses composed of the whole data set for each station. And also the wind roses of different groups (and the pattern for five stations) were different one from another. The pictures of the wind roses for 32 groups at five stations proved very obviously that the sorting process was done in a very effective and successful way.
\n\t\t\tThis example of wind data sorting is a very persuasive one to convince the user about the effectiveness of KNN. This is due to the fact that wind roses are a graphical presentation that can be easily comprehended and the differences or similarities visualized. And on the other hand, there is no way (because of the area complexity) to do this sorting manually - only on the basis of some meteorological knowledge.
\n\t\tAnother very successful application where we used KNN was as a pattern selection technique. When establishing methods for pattern selection that would not need user knowledge about meteorological phenomenon, we used KNN to sort a huge data base of air pollution patterns into natural groups of patterns. When the groups are obtained it is easy to construct a training and testing set to effectively train the MPNN.
\n\t\t\tThe method was developed for the case of SO2 prediction in the Šoštanj area (Božnar, 1997).
\n\t\t\tThe method of pattern selection using KNN showed the same improvement in the MPNN model effectiveness as the method of using all the available detailed expert knowledge about air pollution in the area. Therefore KNN pattern selection techniques are particularly suitable for problems where detailed expert knowledge is not available.
\n\t\tTwo types of artificial neural networks were shown to be useful tools for environmental modelling: the multilayer perceptron neural network MPNN and the Kohonen neural network KNN. MPNN is an universal approximator. Therefore it can be used for modeling phenomena where reconstruction or prediction of one (or several) parameters is required on the basis of other measured parameters. In the model construction phase there are two important steps that are often neglected. These are feature determination and pattern selection techniques. The methods that we suggest can be used in very different applications. They contribute much to the final model performance. Their contribution can be described as extraction of useful information from the available data base of measurements and presentation of this information to the neural network during the learning process in the most plausible way. The variety of presented examples from air pollution prediction to meteorological applications shows how flexible MPNN models can be. Meteorological applications especially demonstrate that MPNN models can be a useful additional tool in the field of meteorological preprocessors for modern air pollution models. The variety of examples presented also proves that feature determination and pattern selection techniques are more or less universal.
\n\t\t\tKNN is not used so widely as MPNN in atmospheric research. The examples presented here prove that KNN is a very effective tool for sorting problems. It actually performs very well also in cases where there is no a priori knowledge about similarities at all.
\n\t\t\tWe hope that the given examples of successful use of artificial neural networks will inspire other applications in atmospheric research.
\n\t\tThe study was partially financed by the Slovenian Research Agency, Project No. L1-2082.
\n\t\tIn 1916, Einstein proposed the theory of stimulated radiation, which laid a theoretical foundation for the emerging of lasers. Till 1960, the world’s first laser was invented by Mehman. Since then, flash-pumping has been widely employed for laser technology to generate beams with good monochromaticity, excellent coherence and promising directivity. Moreover, with the advent of laser diodes (LD), diode-pumped solid-state lasers have achieved rapid development for many advantages such as high efficiency, small size, and high beam quality compared with flash-pumped lasers. However, free-running lasers have relaxation oscillations, which deliver series of disordered small pulse spikes that do not possess high or stable peak powers. With the directional stored energy further modulated temporally, the laser operation regime would gradually evolve from continuous wave (CW) to Q-switching. It is well known that Q-switching is always an efficient method for generating pulsed lasers for any wavelength, since it provides an efficient way to obtain short laser pulses with high peak power, which is much beneficial for scientific or practical applications in terms of light-matter interactions. By using the Q-switching technology, the pulse peak power can reach KW or even MW level, and the pulse energy reaches mJ or even J level, which are very suitable for medical and industrial fields.
In recent years, 2 μm lasers located in the eye-safe spectral range have gained much attention because of their wide applications in the fields of environmental monitoring [1], laser medicine [2], laser radar [3, 4], micro-machining, material processing [5], and so on. In addition, the 2 μm lasers can also be used as pump sources for producing the mid- and far-infrared lasers. For example, they can be used as high-efficiency pumping sources for optical parametric oscillators (OPOs) and optical parametric amplifiers (OPAs) to achieve broadband tunable lasers in the mid-infrared spectral range of 3–5 and 8–12 μm [6, 7, 8]. However, it should be noticed that in practical applications, such as laser surgery, industrial processing, and nonlinear optics, laser pulses with high peak powers are required. Therefore, nanosecond 2 μm laser source with high peak power, large pulse energy and excellent stability is of great significance and has become an important topic of current research on 2 μm lasers.
As mentioned above, Q-switching technology is not an exceptional choice for obtaining nanosecond 2 μm solid-state lasers with high peak power. According to the different operation regimes, Q-switching technology can be divided into active and passive Q-switching (PQS). The basic principle of active Q-switching is regularly modulating the intracavity losses by a voltage controlled modulator, including electro-optic (EO) and acousto-optic (AO) modulators. Although actively Q-switched lasers are frequency adjustable, stable and reliable, they have the disadvantages of large size and expensive cost. Passive Q-switching technology is a good choice to overcome them. In passively Q-switched lasers, the saturable absorber (SA) is the key element and thus the output laser characteristics strongly depend on the nature of the SA materials. Up to now, semiconductor saturable absorber mirrors (SESAMs), chromium doped II-VI semiconductor materials (Cr2+: ZnSe/S), and low-dimensional nanomaterials have been widely studied and regarded as reliable SAs for 2 μm lasers. However, the utilization of SESAMs is restricted by the complicated and expensive fabrication process as well as narrow absorption bandwidth. At present, the low dimensional nanomaterials with advantages of broadband absorption and low cost have been attractive candidates as SAs for PQS.
Up to now, flash-pumped solid-state lasers are still widely used in industry and medical science, because of the advantage of high energy output. In general, the flash-pumping way can support larger mode area than that of diode-pumping regime, which is critical for high energy laser output. To increase the absorption efficiency, Cr3+ ions are usually co-doped as sensitizer/activator for Tm or Tm-Ho host materials when flash-pumped. Under this situation, the energy level diagram for Cr,Tm,Ho: YAG crystal is illustrated in Figure 1. The flash pump light is absorbed by the broadband Cr3+ ions. After a nonradiative decay to and within the 4T2 and 2E states, the excitation is transferred from the Cr3+ ion to the 3F3 and 3H4 states of the Tm3+ ion, via dipole–dipole interactions. Nonradiative decay of the 3F3 places virtually all the excited ions in the 3H4 state. Each excited Tm3+ ion then interacts with a ground state of Tm3+ ion in a cross-relaxation process which gives rise to two Tm3+ ions in the 3F4 state. Finally, these Tm3+ ions transfer their energy to two Ho3+ ions to populate the 5I7 upper laser level, and lasing occurs between the 5I7 and 5I8 transition at 2.097 μm [9].
Energy level diagram for Cr,Tm,Ho:YAG, CR: cross relaxation.
In 1962, Johnson et al. in Bell Labs, USA achieved the firstly 2.06 μm laser emission, but this laser was typically cooled by liquid-N2 at 77 K and the output power was very low. Lately, it was found that some rare earth ions can sensitize Ho3+, thus a Er,Tm,Ho:YAG laser was realized with a slope efficiency of 5% with aid of liquid-N2 cooling. In 1985, Antipenko et al. increased the flash-pumping efficiency and achieved the first 2 μm laser output at room temperature by replacing Er3+ with Cr3+. But CW operation cannot satisfy the requirements of high peak power and narrow pulse width in many applications. Then Q-switching technology was utilized including passive and active Q-switching methods.
In 1996, Kuo et al. used Ho:YVO4 and Ho:CaF2 crystals as SAs for Tm,Cr:YAG lasers, and the corresponding pulse energies and pulse widths were 3.5 mJ, 45 ns and 5.1 mJ, 60 ns at 2.017 μm, respectively [10, 11]. In 2001, flash-lamp-pumped Ho:YAG (2090 nm) and Tm:YAG (2017 nm) lasers were passively Q-switched based on a Cr2+:ZnSe SA for the first time, from which a Q-switched Ho laser with 1.3 mJ pulse energy and ~90 ns pulse duration and a Q-switched Tm laser with ~3.2 mJ pulse energy and 90 ns pulse duration were demonstrated [12]. In 2005, Gaponenko et al. realized passively Q-switched Cr,Tm,Ho:Y3Sc2Al3O12 and Cr,Tm,Ho:YAG lasers using PbS SAs, where the pulse energies and pulse widths were 2.4 mJ, 50 ns and 4.5 mJ, 70 ns at about 2.09 μm, respectively [13]. However, because the pulse energies were too low to satisfy the demands of practical application, active Q-switching technology was applied in flash-lamp-pumped 2 μm lasers.
In 1991, Bowman et al. designed an AO Q-switched Cr,Tm,Ho:YAG laser using a quartz crystal with Brewster angle placed in the cavity. At temperature of 20°C, the pulse energy of 110 mJ with the pulse width of 40 ns was obtained at the repetition rate of 1 Hz [14]. Two years late, they achieved AO Q-switched Cr,Tm,Ho:YAG laser with pulse energy of 24 mJ and duration of 90 ns at a repetition rate of 29 Hz [15]. In 2000, using flash-lamp-pumping, an AO Q-switched Cr,Tm:YAG laser was realized with a maximum pulse energy exceeding 0.8 J and a pulse width of 135 ns [16]. In 2005, Zheng et al. reported a AO Q-switched Cr,Tm,Ho:YAG laser delivering a pulse energy of 120 mJ and a pulse width of 90 ns at the repetition rate of 10 Hz [17].
Besides AO modulator, EO modulator is also a common method to modulate flash-lamp-pumped 2 μm lasers, which could further shorten the pulse duration. In 1981, Gettermy et al. reported a LiNbO3 (LN) crystal based EO Q-switched Ho:YAG laser, which had to be cooled down to 77 K to obtain high energy and short pulses. A pulse energy of 80 mJ with a pulse width of 30 ns at the repetition rate of 5 Hz was obtained [18]. In 1990, Henderson et al. reported a EO Q-switched Cr,Tm,Ho:YAG laser using a LN crystal as modulator. At the repetition rate of 3 Hz, the pulse energy of 50 mJ with a corresponding pulse width of 150 ns was obtained [19]. In 1993, Kim et al. also achieved a pulse energy of 50 mJ from a LN based EO Q-switched Cr,Tm,Ho:YAG laser at 170 K [20]. In 2008, Nieuwenhuis et al. utilized RbTiOPO4 (RTP) crystal as EO modulator in a lamp-pumped Cr,Tm,Ho:YAG laser, and obtained a pulse width of 100 ns with a pulse energy of 42 mJ [21]. In 2012, a flash-lamp-pumped 2.09 μm Cr,Tm,Ho:YAG laser utilizing a La3Ga5SiO14 (LGS) crystal as the EO modulator is proposed and demonstrated for the first time, which results are shown in Figure 2. Operated at a repetition rate of 3 Hz, a pulse energy as high as 520 mJ with a 35 ns pulse width was achieved by optimizing the delay time of EO modulator and compensating for the thermal depolarization with a quarter-wave plate. The corresponding pulse peak power was 14.86 MW, and the energy extraction efficiency was 66.3% [22].
Schematic setup of flash-lamp-pumped EO Q-switched Cr,Tm, Ho:YAG laser. [Reprinted/Adapted] With permission from Ref. [
In conclusion, as shown in Table 1, flash-pumped 2 μm solid-state lasers have been well developed for generating high pulse energy, especially in combination with active Q-switching methods. To now, the highest single pulse energy obtained at 2 μm was 800 mJ, with corresponding pulse duration of 135 ns by using an AO modulator. As for flash-pumped EO Q-switched 2 μm solid-state lasers, the highest single pulse energy of 520 mJ was achieved with a corresponding pulse width of 35 ns, which delivered the highest pulse peak power from the flash-pumped 2 μm solid-state laser, to the best our knowledge. In the future, flash-pumped 2 μm solid-state lasers still have great potential for various applications demanding high energy.
Modulator | Gain media | Durations (ns) | Energy (mJ) | Time | Ref. |
---|---|---|---|---|---|
Ho:YVO4 | Tm,Cr:YAG | 45 | 3.5 | 1966 | [10] |
Ho:CaF2 | Tm,Cr:YAG | 30 | 5.1 | 1966 | [11] |
Cr:ZnSe | Ho:YAG | 90 | 1.3 | 2001 | [12] |
Cr:ZnSe | Tm:YAG | 90 | 3.2 | 2001 | [12] |
PbS | Cr,Tm,Ho:YSAG | 50 | 2.4 | 2005 | [13] |
PbS | Cr,Tm,Ho:YAG | 70 | 4.5 | 2005 | [13] |
AO | Cr,Tm,Ho:YAG | 40 | 110 | 1991 | [14] |
AO | Cr,Tm,Ho:YAG | 90 | 24 | 1993 | [15] |
AO | Cr,Tm:YAG | 135 | 800 | 2000 | [16] |
AO | Cr,Tm,Ho:YAG | 90 | 120 | 2005 | [17] |
LN | Ho:YAG | 30 | 80 | 1981 | [18] |
LN | Cr,Tm,Ho:YAG | 150 | 50 | 1990 | [19] |
LN | Cr,Tm,Ho:YAG | — | 50 | 1993 | [20] |
RTP | Cr,Tm,Ho:YAG | 100 | 42 | 2008 | [21] |
LGS | Cr,Tm,Ho:YAG | 35 | 520 | 2012 | [22] |
Overview of flash-pumped Q-switched 2 micron solid-state lasers.
The AO modulator has the advantages of high damage threshold, easy operation, and low insertion loss. It is capable of generating peak power as high as several hundred kilowatts, high repetition frequency, and short pulse width with several tens of nanoseconds. Thus, AO Q-switching has attracted a lot in generating 2 μm laser pulses based on Tm3+ or Ho3+ ions doped crystals.
The actively Q-switched Tm3+ doped laser can be traced back to 1991, when Suni et al. realized a LD-pumped AO Q-switched Tm:YAG laser with the pulse width of 330 ns and the single pulse energy of 1 mJ at the repetition frequency of 100 Hz [23]. In 2004, Sullivan et al. realized a high-power Q-switched Tm:YAP laser with a maximum output power of 50 W at 1940 nm. At the repetition rate of 5 kHz, the maximum pulse energy was 7 mJ with a corresponding pulse width of 75 ns [24]. But it was low temperature of −10°C. In 2008, Cai et al. realized a diode-pumped AO Q-switched Tm:YAP laser with a maximum single pulse energy of 1.57 mJ and the minimum pulse width of 80 ns at a repetition rate of 1 kHz under room temperature [25]. In 2010, Li et al. showed another double-diode end-pumped AO Q-switched c-cut Tm:YAP laser, which delivered a maximum average output power of 12.5 W at a repetition rate of 10 kHz with a pulse width of 126 ns [26]. In 2015, Yumoto et al. realized AO Q-switched Tm:YAG laser at a center wavelength of 2.013 μm. The maximum pulse energy of 128 mJ was obtained at a repetition rate of 10 Hz, corresponding to a minimum pulse width of 160 ns [27], which was the largest pulse energy for AO Q-switched 2 μm laser. In 2015, Luan et al. realized a 790 nm diode-pumped doubly Q-switched Tm:LuAG laser simultaneously with AO modulator and multilayered graphene as Q-switches, from which a minimum pulse width of 170 ns with a corresponding pulse energy of 0.53 mJ was obtained at a repetition rate of 1 kHz [28]. The results well indicated that doubly Q-switching technique could efficiently shorten the pulse duration.
Efforts are not stopped being paid on exploring diode-pumped solid-state AO Q-switched lasers based on novel kinds of Tm3+ and Ho3+ doped crystals. In 2017, Liu et al. demonstrated an AO Q-switched Tm,Y:CaF2 laser in a V-type cavity as shown in Figure 3, which could run at high repetition rates from 1 to 10 kHz [29]. Under the modulation frequency of 1 kHz, pulses with the shortest duration of 280 ns and the maximum pulse energy of 0.335 mJ were delivered, corresponding to a maximum peak power of 1.19 kW. In 2019, Zagumennyi et al. reported a novel AO Q-switched Tm:Yb3Al5O12 (Tm:YbAG) laser pumped by the 1.678 μm laser. The pulse energy was 100 μJ with a pulse duration of 45 ns at the repetition rate of 6.7 kHz [30].
Experimental setup of the actively Q-switched Tm,Y:CaF2 laser. [Reprinted/Adapted] With permission from Ref. [
In addition to the Tm lasers emitting around 1.9–2.0 μm, Ho3+ ions can emit laser radiations at around 2.0–2.1 μm due to the transition 5I7–5I8, which can keep away from the water vapor absorption and is helpful for the applications requiring free space transmission. Moreover, Ho3+ ion with long upper laser level lifetime are attractive for Q-switched operation. Since Tm3+ ions have absorption bands around 800 nm where commercial GaAs/AlGaAs diodes are available, a Tm sensitized Ho laser is an ideal way to achieve a ∼ 2.1 μm laser. In 2005, Yao et al. realized the AO Q-switched Tm,Ho:GdVO4 laser under liquid nitrogen refrigeration. And the maximum pulse energy of 1.1 mJ and minimum pulse duration of 23 ns were obtained at a repetition rate of 3 kHz [31]. In 2010, Yao et al. investigated the characteristics of AO Q-switched Tm,Ho:YVO4 laser under low temperature conditions. A maximum output power of 19.4 W was achieved with a pulse width of 24.2 ns at a repetition rate of 15 kHz [32]. In 2017, Li et al. achieved a stable AO Q-switched Tm,Ho:YAP laser with a pulse width of 66.85 ns and a single pulse energy of 0.97 mJ at the repetition rate of 7.5 kHz and temperature of 77 K cooled by the liquid nitrogen in Dewar bottle [33]. However, the above Tm,Ho co-doped laser were operated under low temperature conditions, which limited their applications.
As for Ho laser emitting around 2 μm, another way is resonantly pumping Ho laser by an extracavity Tm laser, which has high conversion efficiency and low thermal load. Thus, Ho-doped laser is also a vital approach to obtain high output power and pulse energy laser at wavelength over 2 μm. In 2012, Lamrini et al. realized an actively Q-switched Ho:YAG laser based on an AO modulator at 2.09 μm by using a 1.9 μm LD as the pump source, which schematic setup is shown in Figure 4. At the repetition rate of 100 Hz, the obtained maximum pulse energy and minimum pulse width were 30 mJ and 100 ns, respectively [34]. In 2014, an AO Q-switched Ho:GdVO4 was realized end-pumped by a 1942 nm Tm-fiber laser, delivering a pulse width of 4.7 ns and an output pulse energy of 0.9 mJ at the repetition frequency of 5 kHz, corresponding to a peak power of 187.2 kW [35]. In 2014, Wang et al. used a 1.908 μm Tm:YLF solid-state laser as the pump source to realize an actively Q-switched Ho:YAG ceramic laser at 2.097 μm based on an AO Q-switch. The maximum pulse energy and minimum pulse width were 10.2 mJ and 83 ns at the repetition rate of 100 Hz [36]. In 2016, Ji et al. used a 1.94 μm LD as the pump source to realize an actively Q-switched Ho:YLF solid-state laser based on an AO Q-switch at 2.06 μm. The maximum pulse energy of 1.1 mJ was achieved with a corresponding minimum pulse duration was 43 ns at the repetition rate of 100 Hz [37]. Up to present, many Ho lasers based on various hosts have been demonstrated. However, there is rare report on the Ho laser with more than 100 W output power. In 2018, Duan et al. reported a Ho:YAG laser with the output power of up to 108 W in CW mode and 106 W in Q-switching mode, respectively [38]. As far as we know, this is the highest output power of CW and Q-switched Ho lasers ever reported. A pulse energy of 5.3 mJ and a pulse duration of 21 ns were obtained, corresponding to a pulse peak power of approximately 252 kW.
Resonator of the AO Q-switched Ho(0.5%):YAG laser formed by mirror M1 and mirror M3 (output coupler). [Reprinted/Adapted] With permission from Ref. [
In conclusion, AO Q-switching is an effective method to obtain pulsed 2 μm lasers. Table 2 shows the summaries of diode-pumped AO Q-switched 2 micron solid-state lasers reported in recent years. From it, we can see that the maximum single pulse energy ever achieved based on AO Q-switching method is 128 mJ. However, for high-energy or high-power lasers, the turn-off capacity of AO switching is poor. In practical, most applications demand pulsed 2 μm lasers with large power, high pulse energy and repetition rate, which sets an important challenge for AO modulators to overcome.
Gain media | Durations (ns) | Frequency (kHz) | Energy (mJ) | Wavelength (μm) | Time | Ref. |
---|---|---|---|---|---|---|
Tm:YAG | 330 | 0.1 | 1 | 2.015 | 1991 | [23] |
Tm:YAP | 75 | 5 | 7 | 1.94 | 2004 | [24] |
Tm:YAP | 80 | 1 | 1.57 | 1.99 | 2008 | [25] |
Tm:YAP | 126 | 6 | 1.6 | 1.99 | 2010 | [26] |
Tm:YAG | 160 | 0.01 | 128 | 2.015 | 2015 | [27] |
Tm:LuAG | 170 | 1 | 0.53 | 2.023 | 2015 | [28] |
Tm,Y:CaF2 | 280 | 1 | 0.335 | 1.912 | 2017 | [29] |
Tm:YbAG | 45 | 6.7 | 0.1 | 2.02 | 2019 | [30] |
Tm,Ho:GdVO4 | 23 | 3 | 1.1 | 2.05 | 2005 | [31] |
Tm,Ho:YVO4 | 24.2 | 15 | 1.3 | 2.055 | 2010 | [32] |
Tm,Ho:YAP | 66.85 | 7.5 | 0.97 | 2.119 | 2017 | [33] |
Ho:YAG | 100 | 0.1 | 30 | 2.09 | 2012 | [34] |
Ho:GdVO4 | 4.7 | 5 | 0.9 | 2.05 | 2014 | [35] |
Ho:YAG | 83 | 0.1 | 10.2 | 2.097 | 2014 | [36] |
Ho:YLF | 43 | 0.1 | 1.1 | 2.06 | 2016 | [37] |
Ho:YAG | 21 | 20 | 5.3 | 2.09 | 2018 | [38] |
Overview of AO Q-switched 2 μm solid-state lasers.
EO Q-switching is an important way of obtaining high peak power and narrow pulse width which has the advantages of fast switching speed, high extinction ratio and small volume. The peak power of this kind of laser can easily reach hundreds of megawatts. The most important component in EO Q-switch is EO crystal, many EO crystals including LiNbO3 (LN), RbTiOPO4 (RTP) and La3Ga5SiO14 (LGS) have been successfully applied in the 2 μm EO Q-switched lasers.
From 1970s on, LN crystals have been used as the EO crystal to generate laser pulses, which is exceptional for its no deliquescence, low half-wave voltage, and lateral modulation, etc. In 2015, based on LN crystal Liu et al. reported a diode-pumped EO Q-switched Tm:LuAG laser generating a pulse energy of 2.51 mJ with a pulse width of 88 ns at a repetition rate of 50 Hz [39]. In 2016, with Tm,Ho:YAP crystal as gain material and EO Q-switch based on LN crystal, a maximum pulse energy of 1.65 mJ and a shortest pulse duration of 107.4 ns were obtained at a repetition rate of 200 Hz [40]. In 2018, to lower down the thermal effect of gain medium, Guo et al. incorporated diode-wing-pumping technique into a LN EO Q-switched Tm:LuAG laser. A maximum pulse energy of 10.8 mJ and a minimum pulse width of 52 ns at a repetition rate of 100 Hz was delivered, as shown in Figure 5 [41]. However, further increase of pule energy is limited by the relatively low damage threshold of LN crystal.
Schematic of EO Q-switched Tm:LuAG laser. [Reprinted/Adapted] With permission from Ref. [
RTP crystal belongs to orthorhombic crystal system and has large EO coefficient, high damage threshold and broad transmission range. In 2013, using a 1.94 μm Tm3+ fiber laser as the pump source, Fonnum et al. realized a RTP EO Q-switched Ho:YLF laser utilizing the setup shown in Figure 6. A maximum pulse energy of 550 mJ and a minimum pulse width of 14 ns at a repetition rate of 1 Hz were obtained [42]. In 2016, a diode-pumped RTP EO Q-switched Tm:YAG slab laser delivered a maximum pulsed energy of 7.5 mJ with a pulse width of 58 ns [43]. However, since RTP crystal is biaxial, two crystals with the same size and direction are required to combine with each other to offset natural birefringence, which greatly increases the difficulty of crystal processing. Moreover, under high-power operation regime, thermal induced birefringence can deteriorate switching performance of the RTP EO Q-switch, which subsequently limits the obtained pulse energy and output power.
Schematic setup of the Tm:fiber pumped RTP EO Q-Switched Ho:YLF laser. [Reprinted/Adapted] With permission from Ref. [
In 2003, LGS crystal was firstly grown and studied in Institute of Crystal Materials of Shandong University. LGS crystal is uniaxial and thus free of birefringence induced problems. Compared with LN, the LGS crystal has a 9.5-times-higher anti-photo-damage threshold. Additionally, LGS crystal has a broadband transparency from 190 to 2400 nm, good physical and chemical stability, small thermal expansion coefficient and considerable electro-optic coefficient (γ11 = 2.3 × 10−12 m/V). Kong et al. firstly employed LGS crystal as EO Q-switch in a Nd:YAG laser and achieved pulsed operation at 1 μm [44]. Very recently, Ma et al. realized a LGS EO Q-switched Tm:YAP laser with a maximum repetition rate of 200 kHz, a maximum average output power of 2.79 W and a minimum pulse width of 5.5 ns [45].
In conclusion, EO Q-switch plays a crucial role in generating high-energy laser pulses and suitable choice of EO crystal can determine the performance of EO Q-switch. At present, LN, RTP, and LGS are the most successful EO crystals. For LD pumped 2 μm solid lasers, 550-mJ single pulse energy set a record, to the best of our knowledge. However, exploring excellent EO crystals with high damage threshold, low driven voltage, high repetition rate and the small volume are still on the way (Table 3).
EO crystal | Gain media | Duration (ns) | Frequency (kHz) | Energy (mJ) | Wavelength (μm) | Time | Ref. |
---|---|---|---|---|---|---|---|
LN | Tm:LuAG | 88 | 0.05 | 2.51 | 2.023 | 2015 | [39] |
LN | Tm,Ho:YAP | 107 | 0.2 | 1.65 | 2.13 | 2016 | [40] |
LN | Tm:LuAG | 52 | 0.1 | 10.8 | 2.023 | 2018 | [41] |
RTP | Tm:LuAG | 3 | 100 | 0.0122 | 2.013 | 2016 | [46] |
RTP | Ho:YLF | 14 | 0.001 | 550 | 2.051 | 2013 | [42] |
RTP | Tm:YAG | 58 | 1 | 7.5 | 2.015 | 2016 | [43] |
LGS | Tm:YAP | 5.5 | 200 | 0.0139 | 1.99 | 2019 | [45] |
Overview of EO Q-switched 2 μm solid-state lasers.
In recent years, two-dimensional (2D) material based SAs have been widely used in generating laser pulses. The family of 2D materials includes graphene, black phosphorus (BP), transition metal dichalcogenides (TMDs) and topological insulators (TIs), and so on [47]. There are several methods available for fabricating 2D materials, including micromechanical exfoliation, chemical synthesis, pulsed laser deposition (PLD) and liquid phase exfoliation (LPE) [48, 49]. To fabricate 2D material, LPE is widely used because of its simplicity and effectiveness. Layered materials can be directly exfoliated from their bulk counterparts by this method.
Graphene, a single atomic layer of carbon atom, has attracted particular interest due to its broadband absorption, controllable modulation depth and low non-saturable loss. However, low absorption in graphene limits its applications. In 2012, graphene was firstly used as SAs at 2 μm region [50]. The obtained maximum average output power, pulse repetition rate, and single pulse energy were 38 mW, 27.9 kHz, and 1.74 μJ, respectively. Since then, graphene has been widely applied in many kinds of Tm−doped crystals based 2 μm lasers, such as Tm:LSO, Tm:YAP, Tm:LuAG, Tm,Y:CaF2, and et al. [28, 51, 52, 53].
Another kind of 2D material, TMDs, has also been widely studied in recent years. Properties of few-layered TMDs depend on the number of layers. For instance, bulk MoS2 has an indirect 1.29 eV (961 mm) bandgap, while monolayer MoS2 has a direct 1.8 eV (689 nm) bandgap. However, many reports show the saturable absorption property of few-layered TMDs at near-infrared wavelength region, which corresponds to photon energies smaller than material bandgap for most of TMDs. In a perfect crystalline semiconductor, incident photons with energy lower than the bandgap cannot be absorbed. However, crystallographic defects, including edges and vacancies, enable such absorption [54]. In 2014, Wang et al. concluded that by introducing defects with a suitable quantity range, the bandgap of MoS2 could be reduced from 1.08 (R = 1:2) to 0.08 eV (R = 1:2.09), corresponding to an absorption wavelength from 1.1 to 15.4 μm, which showed that MoS2 with S defects could be used as a kind of broad SA. Passively Q-switched lasers based on MoS2 SA in the range of 1–2 μm have been demonstrated [48].
BP, a layered allotrope of phosphorus, also exhibits a layer-dependent direct bandgap, which is tunable from 0.3 eV (bulk) to 2.0 eV (monolayer), corresponding to an absorption band from 620 nm to 4.13 μm. However, different from MoS2, which owns indirect band-gap at multilayer format, BP always has the direct transition for all thickness. But it has an intrinsic disadvantage of easy oxidation. In 2015, Lu et al. reported the broadband and enhanced SA property of multi-layered BP (with a thickness of ~10 nm) by wide-band Z-scan method [49]. At the same year, Jiang et al. demonstrated passively Q-switched operation of Tm−doped fiber laser based on BP SAs [55]. In 2016, Xie et al. achieved a BP Q-switched Tm:YAG ceramic laser generating pulses with a maximum average output power of 38.5 mW, pulse energy of 3.32 μJ, and pulse width of 3.12 μs [56]. Zhang et al. demonstrated a compact Q-switched Tm:YAP laser based on multi-layered BP nanoplatelets, which delivered an average output power of 3.1 W and a pulse duration of 181 ns at a repetition rate of 81 kHz [57].
Another group of 2D materials, TIs such as Bi2Te3 and Sb2Te3, have also been proposed and investigated in recent years. In general, bulk TIs materials have a small bandgap but a gapless metallic surface state is generated in layered 2D TIs, which is caused by strong spin-orbit coupling and time-reversal symmetry [58]. With such typical band structure, 2D TIs materials show broadband absorption like CNTs and graphene and have become one of promising optical modulator candidates in generating 2 μm laser pulses.
In conclusion, 2D material based SAs have attracted much attention in generating laser pulses due to its advantage of easy fabrication, compact setup, and broadband optical absorption. Related reports on passively Q-switched 2 μm solid-state lasers based on 2D material SAs are summarized in Table 4. However, the obtained output powers are in the order of several hundred mW and the pulse widths are in the order of several hundred ns, especially the pulse energies are only with tens of microjoules, which limits the pulse peak power and their applications furthermore. In the future, the damage thresholds of the 2D material based SAs should be greatly increased, which would benefit the increase of the output power and pulse energy as well as the long-term stabilities of the Q-switched lasers.
SAs | Gain media | Output power (mw) | Duration (ns) | Frequency (kHz) | Energy (μJ) | Time | Ref. |
---|---|---|---|---|---|---|---|
Graphene | Tm:YAG | 38 | 2250 | 27.9 | 3.8 | 2012 | [50] |
Tm:LSO | 106 | 7800 | 7.6 | 14 | 2013 | [51] | |
Tm:YAP | 362 | 735 | 42.4 | 8.5 | 2014 | [52] | |
Ho:LuAG | 370 | 752.2 | 48.8 | 7.5 | 2016 | [28] | |
Tm,Y:CaF2 | 400 | 1316 | 20.22 | 20.4 | 2018 | [53] | |
MoS2 | Tm,Ho:YGG | 205.62 | 410 | 149 | 1.38 | 2014 | [48] |
Tm:CLNGG | 79.2 | 4840 | 110 | 0.72 | 2015 | [59] | |
Tm:GaVO4 | 100 | 800 | 48.09 | 2.08 | 2015 | [60] | |
Tm,Ho:YAP | 275 | 435 | 55 | 5 | 2017 | [61] | |
Tm:CYA | 490 | 480 | 102.6 | 4.87 | 2017 | [62] | |
BP | Tm:YAP | 3199.5 | 181 | 81 | 39.5 | 2016 | [57] |
Tm:YAP | 150.92 | 1780 | 19.25 | 7.84 | 2016 | [63] | |
Tm:CaYA | 12 | 3100 | 17.7 | 0.68 | 2016 | [64] | |
Tm:YAG | 38.512 | 3120 | 11.6 | 3.32 | 2016 | [56] | |
Bi2Te3 | Tm:LuAG | 2030 | 620 | 118 | 18.4 | 2017 | [65] |
Sb2Te3 | Tm:GdVO4 | 700 | 223 | 200 | 3.5 | 2018 | [66] |
Overview of 2D nanomaterials modulated 2 μm solid-state lasers.
Since Podlipensky et al. demonstrated that Cr2+:ZnSe crystals can be used as SAs for 1.54 μm Er:glass lasers [67], Cr2+ doped II-VI compounds like Cr2+:ZnS and Cr2+:ZnS have been widely used in the spectral range of 1.5–2.1 μm. Figure 7 shows that Cr:ZnS and Cr:ZnSe have broadband absorption spectra form 1.5 to 2.2 μm [68].
Absorption (black) and emission (red) cross-sections of ZnS and ZnSe doped with Cr2+ ions. [Reprinted/Adapted] With permission from Ref. [
Up to now, Cr:ZnSe/ZnS have been applied in many 2 μm pulsed lasers, as shown in Table 5. In 2015, Sebbag realized a Cr:ZnSe Q-switched Tm:YAP solid-state laser at 1935 nm, which delivered a pulse energy of 1.55 mJ and a pulse duration of 42.2 ns, corresponding to a peak power of 36.7 kW [71]. In 2017, Lan reported a Cr:ZnSe Q-switched Tm:CYA laser with a maximum repetition rate of 21.9 kHz, a minimum pulse width of 42.6 ns and a maximum pulse energy of 60.2 μJ [73]. In 2017, a Cr2+:ZnSe passively Q-switched Nm–cut Tm:KLu(WO4)2 mini-slab laser was reported, where the obtained shortest pulse duration was 35 ns and maximum pulse energy was 0.3 mJ [90]. Due to the low absorption cross-section of Cr:ZnSe crystal around 2 μm, it was mainly used in Tm-doped crystal based lasers. In 2013, A Cr2+:ZnS passively Q-switched Ho:YAG laser pumped by a Tm:YLF laser was demonstrated with a maximum pulse energy of 2.47 mJ, a minimum pulse duration of 36.6 ns at a repetition rate of 10.4 kHz [81]. In 2015, a diode-pumped Cr:ZnS passively Q-switched Tm:KLu(WO4)2 microchip laser generated sub-nanosecond (780 ps) pulses with a pulse repetition frequency of 5.6 kHz, which was the shortest pulse duration among the passively Q-switched 2 μm lasers ever achieved [85]. Besides, in 2017, another novel Cr2+ ions doped Cr:CdSe crystal was firstly demonstrated in a Ho: YAG laser at 2.09 μm. A maximum output pulse energy of 1.766 mJ at a repetition frequency of 685 Hz was obtained with a pulse duration of 15.4 ns, which indicated that a Cr:CdSe crystal could be a promising SA in passively Q-switched 2 μm lasers [89].
SAs | Gain media | Output power (W) | Duration (ns) | Frequency (kHz) | Energy (μJ) | Time | Ref. |
---|---|---|---|---|---|---|---|
Cr:ZnSe | Tm:YAG | 1.6 | 300 | 4 | 400 | 2003 | [69] |
Tm,Ho:YLF | 0.1 | 40 | 4 | 25.6 | 2014 | [70] | |
Tm:YAP | 0.87 | 42.2 | 0.561 | 1550 | 2015 | [71] | |
Tm:YLF | 0.027 | 1200 | 2.1 | 13 | 2014 | [72] | |
Tm:CYA | 1.32 | 42.6 | 21.9 | 60.2 | 2017 | [73] | |
Tm:CYA | 0.25 | 107 | 5.85 | 48.2 | 2017 | [74] | |
Tm:CGA | 0.64 | 44 | 13.9 | 46 | 2017 | [75] | |
Tm:KLuW | 3.2 | 35 | 6.3 | 300 | 2017 | [75] | |
Ho:SSO | 2.4 | 73.5 | 2.65 | 900 | 2017 | [76] | |
Cr:ZnS | Tm:KLW | 0.39 | 25 | 2.7 | 145 | 2012 | [77] |
Tm:YLF | 0.098 | 14 | 0.12 | 850 | 2012 | [78] | |
Tm,Ho:YLF | 0.016 | 1250 | 1.3~2.6 | 4 | 2012 | [79] | |
Tm:LLF | 0.2 | 7.6 | 0.161 | 1260 | 2012 | [80] | |
Ho:YAG | 16.6 | 36.6 | 10.4 | 2470 | 2013 | [81] | |
Tm,Ho:LuLiF4 | 0.074 | 1200 | 5.8 | 13 | 2013 | [82] | |
Tm,Ho:GdVO4 | 3.2 | 354 | 52 | 70.5 | 2013 | [83] | |
Tm,Ho:YVO4 | 0.3 | 500 | 65 | 3.5 | 2014 | [84] | |
Tm:KLW | 0.146 | 0.78 | 5.6 | 25.6 | 2015 | [85] | |
Ho:YAG | 14.8 | 29 | 24.4 | 600 | 2015 | [86] | |
Ho:LuAG | 1.14 | 36 | 0.79 | 1540 | 2015 | [87] | |
Ho:YAP | 6.1 | 93.6 | 7.5 | 830 | 2015 | [88] | |
Tm:YAP | 0.89 | 35.8 | 0.481 | 1850 | 2015 | [71] | |
Tm,Ho:KLuW | 0.131 | 14 | 8.2 | 10.4 | 2016 | [77] | |
Cr:CdSe | Ho:YAG | 1.2 | 15.4 | 0.685 | 1766 | 2017 | [89] |
Overview of Cr2+-doped crystal modulated 2 μm solid-state lasers.
In conclusion, Cr2+ doped II-VI compounds have been successfully used as SAs in passively Q-switched 2 μm lasers. In this way, the obtained pulse energies and the pulse widths can reach hundreds of μJ and ns or even sub-ns level. The maximum pulse energy of 2.47 mJ, and the shortest pulses with sub-nanosecond (780 ps) duration were both realized with a kind of Cr:ZnS SA. Therefore, Cr2+ doped II-VI crystals are still irreplaceable SAs in generating passively Q-switched 2 μm lasers with pulse energies over mJ level and pulse width in order of several nanoseconds to sub-nanoseconds.
As mentioned above, the popular SAs used in the 2 μm Q-switched solid-state lasers are mainly low-dimensional nanomaterials and Cr-doped crystals. In addition, the absorption effect of Ho3+ doped gain crystals can also be used for SAs, which can be classified as a slow-relaxing solid-state SA due to possessing a long emission lifetime. However, the study of passively Q-switched solid-state lasers based on gain crystals is relatively rare. In 1994, Kuo et al. reported a flash-lamp pumped Tm,Cr:Y3Al5O12 laser using a Ho:YLiF4 as SA at room temperature [91]. A maximum single pulse energy of 11 mJ and a shortest pulse duration of 45 ns were obtained. It paved the way for Ho-doped crystals as SAs for passively Q-switched 2 μm lasers. Additionally, Ho:SSO has also been employed as an efficient SA. In 2018, a Ho:SSO crystal was used as SA for the Ho:YAG laser [92]. At a repetition rate of 42.1 kHz, a minimum pulsed duration of 48 ns and a maximum pulse energy of 2 mJ were obtained.
2 μm laser is an ideal light source for the medical application, which has less damage to the rest of the tissue, and less bleeding during the operation. 2 μm lasers used in laser medical treatment are mainly YAG-based two pulsed lasers [93, 94, 95, 96]. Ho:YAG has been successfully used in urology and orthopedics [96]. However, little is known about hard dental tissue ablation with Ho:YAG laser. For example, non-contact laser surgery offers several potential advantages in dental treatment, such as reduced pain and vibration, more precise control by electronic device and haemostatic effect. Figure 8 shows cross-sectional images of ablation crater created on the urinary calculi surface by pulsed Ho:YAG laser (Wuhan National Laboratory for Optoelectronics) in air (dry condition) and underwater with various thickness [93]. The pulsed Ho:YAG laser emitted at 2.12 μm with the pulse energy of 2000 mJ and the pulse duration of 300/450 μs at the repetition rate of 20 Hz.
(Color online) Cross-sectional topography of holmium laser-induced (300 μs, 300 mJ, 1 Hz) craters acquired with OCM system in air and in water. The 600 μm fiber is vertically in contact with the urinary calculi surface. w = width; h = height. [Reprinted/Adapted] With permission from Ref. [
Lidar technology is an optical remote sensing technology that acquires the physical information of a target object by detecting the scattered light characteristics of a distant target object. Compared with the traditional radar technology, the laser radar technology realizes the information loading by modulating the amplitude and frequency of the laser beam, thereby having the advantages of high resolution and good anti-interference. In recent years, laser radar technology with 2 μm band laser as coherent light source has been proposed and made some progress. In 2006, the NICT agency in Japan reported on the airborne coherent wind lidar system. The system used a Tm:YAG laser with a center wavelength of 2.01 μm as the coherent light source, and the maximum single pulse energy was 7 mJ, and it was successfully applied to the test of atmospheric wind profile [97]. In 2007, NASA agencies in the United States reported on the vehicle’s coherent wind Lidar system. The system utilized Tm,Ho:LuLiF4 laser at 2.05 μm and a pulse energy of 100 mJ as the coherent light source, and successfully detected the wind field information of the boundary layer and the troposphere [98]. In 2013, the NICT facility in Japan used a differential absorption Lidar system to successfully measure CO2 concentrations and mountain targets in the 7 km range. The system used a Ho:YLF laser as the light source with a pulse energy of 50–80 mJ and a pulse width of 150 ns at the repetition frequency of 30 Hz [99]. In 2014, the French Polytechnic University reported a Ho:YLF multi-frequency single longitudinal mode laser with a center wavelength of 2.051 μm. The laser operated at a repetition rate of 2 kHz with a pulse energy of 13 mJ and a pulse width of 42 ns. The laser could be used as a source of differential absorption Lidar for the measurement of atmospheric CO2 concentrations, as shown in Figure 9 [100].
Experimental setup diagram of differential absorption Lidar for the measurement of atmospheric CO2 concentrations based on 2 μm laser. (a) Offset locking between On and Off distributed-feedback laser diode (DFB), (b) seeding architecture, (c) cavity length locking using Pound-Drever-Hall technique (PDH), (d) 2-μm pulsed oscillator, and (e) characterization: power meter, beam profiler, heterodyne detection [
A pulsed 2 μm laser with high peak power is also a promising pump source for OPOs in the wavebands such as 3~5 and 8~12 μm. The laser in this waveband plays a key role in the directed infrared countermeasures. Compared with other mid-far infrared lasers (such as CO lasers [101, 102], quantum cascade lasers [103, 104]), OPOs have the advantages of wideband tunability of output spectrum and narrow laser line width. The typical system is Directed Infrared Countermeasures (DIRCM) system, which can protect airborne platforms from infrared guided missile threats. The layout of the Ho:YAG master oscillator power amplifier (MOPA) in DIRCM system [105]. A Tm:YLF fiber laser pumped Ho:YAG laser system with pulse energies up to 90 mJ and the pulse width of 20 ns at 100 Hz. Then, the wavelength was converted into the 3 to 5-micron region using a zinc germanium phosphide (ZGP) crystal in a linear or ring resonator.
In conclusion, 2 μm wave is located in the weak absorption band of the atmosphere and the safe region of the human eye. Therefore, it is of great significance to study 2 μm Q-switched lasers in the areas such as laser medicine and laser lidar. Moreover, the 2 μm pulse laser with high peak power can also be used as an efficient pump source for OPOs and OPA to obtain the output of mid- and far- infrared laser. With the development of 2 μm pulsed laser, it will have more broad applying foreground in industrial processing, medicine, and military field.
2 μm pulsed laser with stable, compact and cost-effective characteristics has been a hot topic in recent years. To achieve pulsed laser sources with large pulse energy and high peak power, the traditional method is based on actively AO or EO Q-switching technology. For the flash-pumping operation regime, it has great advantages in generating high energy laser output with hundreds of mJ level due to the larger mode area than that of diode-pumping case. However, diode-pumped solid-state 2 μm lasers have achieved rapid development because of the advantages such as high efficiency, small size, and high beam quality. Furthermore, in cased of the AO Q-switching, many excellent works have been done and the maximum single pulse energy ever achieved is 128 mJ. For diode-pumped EO Q-switched solid lasers, 550-mJ single pulse energy has set a record in the case of using a RTP crystal as EO modulator, to the best of our knowledge. Although the EO modulator has relatively good switching effect, suitable EO crystal with low driven voltage and high repetition rate is rare. Recently, a LGS crystal based EO Q-switched Tm:YAP laser with a maximum repetition rate of 200 kHz was reported, which indicated that the LGS crystal with high damage threshold could be a promising EO Q-switch. Compared with active Q-switching method, passive Q-switching technology has shown promising advantages, such as simplicity, compactness, and high repetition rate, although it usually works in low-energy regime. The most mature SAs are Cr2+ doped II-VI crystals which are still irreplaceable in generating passively Q-switched 2 μm laser with pulse energy over mJ level and pulse width in an order of several nanoseconds to sub-nanoseconds. Besides, the Ho3+ doped gain crystals can also be used as SAs for 2 μm solid-state lasers, however, this rarely employed method can only deliver tens of nanoseconds pulses with tens of kHz repetition rate. At present, the 2D nanomaterials based SAs have become hot spots in generating pulsed lasers due to its advantages of easy fabrication, compact setup, and broadband optical absorptions. However, the pulse energy was limited to μJ level determined by the low damage thresholds of the 2D material. At the same time, there is also a problem in fabricating high quality thin films with large area and uniform thickness. In the near future, active Q-switching technology would still be the main method in obtaining high pulse energy or short pulse width with excellent stability and controllability. Of course, more suitable EO crystals would be further explored. For SAs suitable for passively Q-switched lasers, novel kinds of 2D nanomaterials are emerging endlessly, and the preparation method for growing high quality 2D-SAs are coming to maturity.
As for the applications of Q-switched 2 μm lasers, the application fields are becoming broader and broader. When the 2 μm pulsed laser is applied on the tissue, it can generate the effects such as the vaporization, cutting, solidification, hemostasis and so on, so it plays an important role in medical surgery. Q-switched 2 μm lasers can also be effectively used in environmental monitoring and measurement of carbon dioxide, aerosol concentration, cloud layer and water vapor distribution in the atmosphere. Besides, it should be particularly stated that 2 μm pulsed laser with high peak power is an efficient pump source for OPOs or OPA to realize mid- and far- infrared laser sources, which have important applications in the fields such as Lidar, optoelectronic countermeasures, laser ranging and infrared guidance technology. With the continuous enhancement of Q-switched 2 μm laser performance, more complicated application fields would be further explored and developed in the future.
This work was partially supported by National Natural Science Foundation of China (NSFC) (61475088), Key research and development program of Shandong Province (2018GGX101006), Shenzhen Science and Technology Research and Development Funds (JCYJ20180305163932273), the Program of State Key Laboratory of Quantum Optics and Quantum Optics Devices (KF201908) and Young Scholars Program of Shandong University (2015WLJH38).
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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. 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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. 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He completed a one-year Post-Doctoral Fellowship awarded by the DFAIT (Foreign Affairs and International Trade Canada) at the Institute of Biomedical Engineering of the University of New Brunswick (Canada) in 2010. Currently, he is Professor in the Faculty of Electrical Engineering (UFU). He has authored and co-authored more than 200 peer-reviewed publications in Biomedical Engineering. He has been a researcher of The National Council for Scientific and Technological Development (CNPq-Brazil) since 2009. He has served as an ad-hoc consultant for CNPq, CAPES (Coordination for the Improvement of Higher Education Personnel), FINEP (Brazilian Innovation Agency), and other funding bodies on several occasions. He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for 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:"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:"Beijing University of Technology",institution:null},{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:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{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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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 15th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:286,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/257099",hash:"",query:{},params:{id:"257099"},fullPath:"/profiles/257099",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()