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1. Introduction
The science of decision support is foundational for every type of policy, and this work offer a proposal to analyze its role in energy policy.
An example of application of a particular machine learning (ML) technique to an energy policy problem is presented. It is important to understand the role of ML in energy and environmental analysis, for two solid reasons.
The first concerns the need to process large volumes of data and to elaborate and model complex relationships, typical of the energy analysis and of the environmental analysis. In this context, the use of AI (Artificial Intelligence) and machine learning is almost mandatory.
The second concerns the need to a concerted effort to identify how these tools may best be applied to tackle major problems of recent years, like climate change [1]: about this, CO2 emissions is key variable that we must control to achieve the global objective of mitigating damage for humanity.
This work has a specific goal. Using known tools from the scientific literature on energy generation costs, we intend to show how the use of a machine learning technique (the support vector machines, SVM) can produce a more accurate modeling of these costs.
The link with CO2 emissions is provided by the possibility of using the cost model in a cost-effectiveness analysis (C-E A), in which the cost is represented by the Levelised Cost of Energy (LCOE) and the effectiveness is represented by the CO2 emissions of the technologies considered per unit of energy produced.
The CO2 estimation is then obtained by selecting the best generation options according to the C-E A results.
The meaning of this work is the following.
Imagine that you are an energy analyst, in the public or private sector, and you need to use only one or just few variable/s (such as a forecast on the cost of natural gas), to estimate the costs of an electricity generation technology.
This task can be accomplished using a cost model of electricity generation in which a single piece of information can vary, leaving everything else unchanged (or imposing a certain trend on it).
The metric used is the indicator LCOE (Levelised Cost of Energy) provided by IEA (International Energy Agency), using 2020 data.
Once you have obtained a certain level of accuracy in estimate of energy cost, it is possible to move into a context of cost-effectiveness analysis, in which the best energy option in terms of Incremental Cost-Effectiveness Ratio (ICER) was selected to produce energy and, finally, provide a certain level of CO2 emissions for the time horizon in which such a technology is still the “best option”.
In other words, the estimate of energy cost and the cost-effectiveness analysis, allow us to trace the scenarios for electricity generation mix and, finally, calculate a quantitative forecast of the CO2 emitted.
The proposed work just intends to show the application of one of the existing machine learning techniques to the estimation of the LCOE, starting from some explanatory variables.
A linear model (LM) and an SVM are compared in the prediction of the LCOE value for a combined cycle gas plant (CCGT) with a focus on the fuel cost, Operation and Maintenance (O&M) cost and CO2 price using IEA data for Italy in 2020.
The work carried out intends to highlight the possibilities of applying machine learning techniques not only in the purely engineering aspects of energy systems, but also in the statistical-economic ones at a higher level of abstraction.
Some words about why to focus on power generation systems.
As countries work towards a low carbon world, it is crucial that policymakers, modelers, and experts have at their disposal reliable information on the cost of generation.
IEA [2] reports that the levelised costs of electricity generation of low-carbon generation technologies are more and more low the costs of conventional fossil fuel generation. Renewable energy costs continue their descent in recent years and their costs are now competitive with dispatchable fossil fuel-based electricity generation for many countries.
2. Methodology
This section presents the main tools used in this work: the LCOE methodology provided by IEA and the SVM, the used machine learning technique. Just before SVM presentations a very brief remind about ML and its use in energy systems and CO2 emissions estimates will be provided.
2.1 Levelised cost of energy
The Levelised Cost of Energy (LCOE) is the selected tool to measure the cost of an energy unit produced by the considered technologies. LCOE is a methodology described in the joint report by the International Energy Agency and the OECD (Organization for Economic Co-operation and Development) Nuclear Energy Agency (NEA) (now at the ninth edition in a series of studies on electricity generating costs) [1]. This report includes cost data on power generation from natural gas, coal, nuclear, and a broad range of renewable technologies.
The metric for plant-level cost chosen is the well-known levelised cost of electricity (LCOE) (IEA are now considering system effects and system costs with the help of the broader value-adjusted LCOE, or Levelised Cost of Value-Adjusted LCOE, VALCOE metric, here not considered).
The LCOE is widely considered as the principal tool for comparing the plant-level unit costs of different base load technologies over their operating lifetimes since indicates the economic costs of a technology family, not the financial costs of a certain projects in a certain market. Due to the equality between discounted average costs and the stable remuneration over lifetime electricity production LCOE recall the costs of electricity production in regulated electricity markets with stable tariffs than to the variable prices in deregulated markets.
Despite many limitations, LCOE has maintained its utility and appeal since it is a uniquely straightforward, transparent, comparable, and well understood metrics remaining a widely used tool for modeling, policy making and public debate.
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted revenues and the present value of the sum of discounted costs. Another way on the left-hand side one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:
PMWh The constant lifetime remuneration to the supplier for electricity;
MWh The amount of electricity produced annually in MWh;
1+r−t The real discount rate corresponding to the cost of capital;
Capitalt Total capital construction costs in year t;
O&Mt Operation and maintenance costs in year t;
Fuelt Fuel costs in year t;
Carbont Carbon costs in year t;
Dt Decommissioning and waste management costs in year t
PMWh is equal to levelised cost of electricity (LCOE).
Eq. (1) is the formula used here to calculate average lifetime levelized costs based on the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning and dismantling provided by OECD countries and selected non-member countries.
2.2 Machine learning
Machine learning (ML) is the field of artificial intelligence (AI) that provide methods to learn from data over time creating algorithms not being programmed to do so.
The literature about ML is relatively recent but is so vast that only some hint to review works can be made here, as an access point to this world1.
Machine learning approaches are normally categorized as in the follows.
Supervised machine learning, that trains itself on a labeled data set; unsupervised machine learning that uses unlabeled data with algorithms to extract the features required to label, sort, and classify the data in real-time, without human intervention; semi-supervised learning (SsL) namely a medium between supervised and unsupervised learning: SsL uses a smaller labeled data set during training and make classification and feature extraction from a larger, unlabeled data set; reinforcement machine learning is like supervised learning, but do not requires sample data for training (since using “trial and error” mode).
About the machine learning algorithms for use with labeled data the regression algorithms (as linear and logistic regression); decision trees (based on a set of decision rules to perform classification); instance-based algorithms: it uses classification to estimate how likely a data point is to be a member of one group, or another based on its proximity to other data points.
Methods based for use with on unlabeled data are: clustering algorithms: (like K-means, TwoStep, and Kohonen clustering); association algorithms: (that find patterns in data by identifying ‘if-then’ relationships namely association rules); neural networks: (that create a layered network of calculations featuring an input layer, when data in; one or more hidden layer, where calculations are performed; and an output layer. Where each conclusion is assigned a probability); deep neural network that uses multiple hidden layers, each of which successively refines the results of the previous layer. Deep learning models are typically unsupervised or semi-supervised. Certain types of deep learning models—including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.
In this work, the machine learning approach used is the SVM one.
SVMs2 are machine learning algorithms built on statistical learning theory for structural risk minimization. In pattern recognition, classification, and analysis of regression, SVMs outperform other methodologies. The significant range of SVM applications in the field of load forecasting is due to its ability to generalize (also, local minima lead to no problems in SVM).
SVM was chosen, in this work, for the sake of simplicity, since the performed Support Vector Regression (SVR) [5], extremely easy to understand in comparing a traditional statistical tool with a competing machine learning based one.
Often, the available applications of SVM in the energy sector are oriented on the engineering side3 while in this work the approach is oriented in support decisions for energy policy field.
Using one of the possibilities offered by SVMs, namely the SVR, the follows show how it is possible to obtain more accurate forecasts of costs per unit of energy produced, using LCOE as a metric.
The best available accuracy is then used in a context of cost-effectiveness analysis.
In the following, a method to select among competing options (options that can be differ even for slight changes in some significant LCOE parameters), the one characterized by the best Incremental Cost-Effectiveness Ratio (ICER) is presented.
The possibility of making this choice during the lifetime of the plant leads to the possibility of identifying the best technology available, year by year, to get the corresponding profile of the associated CO2 emissions.
2.2.1 Machine learning for energy systems and CO2 emission estimation
The growing utilization of data collectors in energy systems has resulted in a massive amount of data accumulated (an increasing mass of mart sensors are now extensively used in energy production and energy consumption) leading to a continuous production of big data and, consequently, to a massive number of opportunities and challenges in decision support science.
Today, ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy, and speed or to provide an understanding on energy system functionality in the context of complex human interactions.
[7] propose a comprehensive review of essential ML to present the state of the art of ML models in energy systems and discuss their likely future trends.
Machine learning was used for estimate CO2 emission from energy systems in several context, using different approach. It is possible to recall, among an increasing number of works in recent years:
[8] about flexibility of the electricity demand, a machine learning algorithm developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions in Danish bidding zone DK2;
[9] an investigation on the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries;
[10] on the linkage between energy resources and economic development the focus of that work is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources with a higher predictive accuracy;
[11] about proposing a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs using a random forest machine learning method);
[12] on the relationship among iron and steel industries, air pollution and economic growth in China (using a Long Short Term Memory, LSTM, approach);
[13] on the forecasting of energy consumption related carbon emissions for the Beijing-Tianjin-Hebei region.
[14] on the uses of gray relational analysis to identify the factors that have a strong correlation with carbon emissions for China to reduce carbon emissions by studying prediction of carbon emissions (using LSTM).
[15] on the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities for the Peruvian Amazon (using deep learning models).
[16] on the use of a random forest machine learning regression workflow to map country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into, to create a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries as a transformative tool to quantify the climate change mitigation services that forests provide.
[17] To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, through a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040.
2.3 Our methodology
The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.
In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.
The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.4
To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.5
This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.
Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant’s lifetime.
First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.
The basic relationships of the model are:
PF=Power∗8760∗AVLF∗AAF100∗1−AuxPE2
ws=1−wdE3
ks=krft+EMRP∗BE4
i=wd∗kd+ws∗ksE5
d=i/1+iE6
dfi=∑j1/1+ijE7
icfinal=icfinal+icCnsT∗dfiE8
df=∑j1/1+djE9
icfinal=icfinal+icCnsT∗dfi1E10
dfi=∑j1/1+ijE11
Pro=Pro+PF∗dfE12
OM=FOM+VOM∗PF∗dfE13
Fue=CFue∗PF∗dfE14
CO2=PCO2∗PF∗dfE15
Cost=∑jOM+Fue+CO2E16
Decom=n∗Decom∗ProE17
LCOE=Power∗icfinal∗1000+Cost+Decom/ProE18
Where:
CC Cost of Capital (USD/MWh)
Power net capacity (MWe)
AVLFmin AVerage Load Factor min value (%)
AVLFmax AVerage Load Factor max value (%)
AAF Average Availability Factor (%)
AuxP Auxiliary Power (%)
Lifetime Time horizon of plant (years).
wdmin min weight of cost of debt on total cost (%)
wdmax max weight of cost of debt on total cost (%)
kdmin min value of debt rate (%)
kdmax max value of debt rate (%)
tmin min value of taxation (%)
tmax max value of taxation (%)
krftmin min value of free risk rate (%)
krftmax max value of free risk rate (%)
EMRPmin min value of Expected Market Risk Premium (%)
EMRPmax max value of Expected Market Risk Premium (%)
Bmin min value of Beta (%)
Bmax max value of Beta (%)
CnsTmin min value of Construction Time (years)
CnsTmax max value of Construction Time (years)
FOMmin Fixed Operation and Maintenance Costs min (USD*MWh)
FOMmax Fixed Operation and Maintenance Costs max (USD*MWh)
VOMmin Variable Operation and Maintenance Costs min (USD*MWh)
VOMmax Variable Operation and Maintenance Costs max (USD*MWh)
Cfuemin min value of Costs of Fuel (USD*MWh)
Cfuemax max value of Costs of Fuel (USD*MWh)
Effmin min value of Efficiency (%)
Effmax max value of Efficiency (%)
PCO2min min value of CO2 price (USD*MWh)
PCO2max max value of CO2 price (USD*MWh)
Decommin min value of Decommissioning (USD*MWh)
Decommax max value of Decommissioning (USD*MWh)
All other parameters are settled using the IEA values.
We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).
Table 1 describes the hypothesis used in this first step of the analysis.
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 1
Linear decreasing of 2% per year except every 6 years
constant
constant
Scenario 2
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Table 1.
Scenarios used for the regression of LCOE on fuel cost and O&M cost
3. Results
Figure 1 shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (Figure 2).
Figure 1.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
Figure 2.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
1,30E-14
8,39E-15
8,39E-17
SVM
5,25E-01
4,01E-01
4,01E-03
Tuned SVM
1,74E-03
1,54E-03
1,54E-05
with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (Figures 1 and 2).
Figure 3.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
Figure 4.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
3.87E+00
2.70E+00
2.70E-02
SVM
2.77E+00
1.59E+00
1.59E-02
Tuned SVM
2.61E+00
1.45E+00
1.45E-02
Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.
The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.
Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 3
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.
For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:
RMSE
MAE
MAPE
Linear Model
4.25878
3.49147
0.03491
SVM
2.70117
1.52912
0.01529
Tuned SVM
2.58541
1.52378
0.01524
In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.
Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).
In addition to this, let us imagine that the second type of plant has an average load factor of 94%.
Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.
The meaning of this operation is as follows:
to use systems with different characteristics (in this case we have changed the O&M costs and the load factor of a single technology family);
to calculate the ICER corresponding to each plant in a defined time interval (in this case, from when the LCOE starts to vary);
to calculate the degree of uncertainty on the value of the ICER thanks to the MAPE of the SVR, defining the variation range for the ICER6;
to select the technology that has the lowest ICER and then we calculate the corresponding emissions over the time horizon considered;
finally, to calculate the emissions profile corresponding to the winning technology, year by year.
CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).
Figure 5 illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.
4. Conclusions
ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).
In our case, the performed step was:
improving LCOE forecasting performance,
comparing multiple competing options by use of the ICER in Cost-Effectiveness Analysis;
consider the uncertainty about ICER using the MAPE (in this case, but is just an option) calculated by SVM;
choosing the best technology and calculating the CO2 emissions for it;
defining the trend of the CO2 emissions in the lifetime of the plant by step 4.
Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.
The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CO2 emissions, energy systems, machine learning, support vector machines, cost-effectiveness analysis, forecasting",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76238.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76238.xml",downloadPdfUrl:"/chapter/pdf-download/76238",previewPdfUrl:"/chapter/pdf-preview/76238",totalDownloads:150,totalViews:0,totalCrossrefCites:1,dateSubmitted:"November 12th 2020",dateReviewed:"March 26th 2021",datePrePublished:"April 12th 2021",datePublished:null,dateFinished:"April 12th 2021",readingETA:"0",abstract:"In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76238",risUrl:"/chapter/ris/76238",signatures:"Marco Rao",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. Abdel Rahman",coverURL:"https://cdn.intechopen.com/books/images_new/10627.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-368-7",printIsbn:"978-1-83969-367-0",pdfIsbn:"978-1-83969-369-4",isAvailableForWebshopOrdering:!0,editors:[{id:"146384",title:"Dr.",name:"Marcos Sales Guerra",middleName:null,surname:"Tsuzuki",slug:"marcos-sales-guerra-tsuzuki",fullName:"Marcos Sales Guerra Tsuzuki"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology",level:"1"},{id:"sec_2_2",title:"2.1 Levelised cost of energy",level:"2"},{id:"sec_3_2",title:"2.2 Machine learning",level:"2"},{id:"sec_3_3",title:"2.2.1 Machine learning for energy systems and CO2 emission estimation",level:"3"},{id:"sec_5_2",title:"2.3 Our methodology",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_8",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Rolnick, D. and Donti, P. and H. Kaack, L.H., and Kochanski, K. and Lacoste, A. and Sankaran, K. and Ross, A. and Milojevic-Dupont, N., and Jaques, N., and Waldman-Brown, A., and Luccioni, A., and Maharaj, T., and Sherwin, E., and Mukkavilli, S.K., and Konrad P. K, and Carla Gomes, K., and Ng, A., and Hassabis, D., and C. Platt, J.C., and Creutzig, F., and Chayes, J. and Bengio, Y., Tackling Climate Change with Machine Learning, 2019'},{id:"B2",body:'IEA, Projected Cost of Generating Electricity 2020, IEA, 2020'},{id:"B3",body:'Saravanan and Sujatha, P., “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 945–949, doi: 10.1109/ICCONS.2018.8663155'},{id:"B4",body:'Wang, L. 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Application of support vector machine models for forecasting solar and wind energy resources: A review, Journal of Cleaner Production, Volume 199, 2018, Pages 272-285, DOI: 10.1016/j.jclepro.2018.07.164'},{id:"B7",body:'Salimi, M., Mosavi, A., Faizollahzadeh, A.S., Amidpour, M., Rabczuk, T., and Shamshirband, S., State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies 2019, 12, 1301; doi:10.3390/en12071301'},{id:"B8",body:'Leerbeck, K., Bacher, P., Grønborg Junker, R., Goranović, G., Corradi, O., Ebrahimy, R., Tveit, A., Madsen, H., Short-term forecasting of CO2 emission intensity in power grids by machine learning, Applied Energy, Volume 277, 2020, DOI:10.1016/j.apenergy.2020.115527'},{id:"B9",body:'Magazzino, C., Mele, M., Schneider, N. 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Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci Rep 9, 17831 (2019). https://doi.org/10.1038/s41598-019-54386-6'},{id:"B17",body:'Niu, D. Wang, K., Wu, J., Sun, L., Yi Liang, Y., Xu, X., Yang, X. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network, Journal of Cleaner Production, Volume 243, 2020, 118558, DOI: 10.1016/j.jclepro.2019.118558'},{id:"B18",body:'Korovkinas, K., Danènas, P., Garsva, G., Support vector machine parameter tuning based on particle swarm optimization metaheuristic, Nonlinear Analysis: Modelling and Control, Vol. 25, No. 2, 266–281, DOI=10.15388/namc.2020.25.16517'},{id:"B19",body:'Botchkarev, A. A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms, Interdisciplinary Journal of Information, Knowledge, and Management, Volume 14, pp. 045–076, 2019, DOI=10.28945/4184'}],footnotes:[{id:"fn1",explanation:"Here we just remind a recent review of the state of art in machine learning techniques [3]."},{id:"fn2",explanation:"For a good introduction to this topic see [4]."},{id:"fn3",explanation:"See, for example [6]."},{id:"fn4",explanation:"Indeed, manual tuning is often considered as one of the most significant choice [18]."},{id:"fn5",explanation:"See [19] for a complete discussion about the used metrics."},{id:"fn6",explanation:"Namely, ICER max/min = ICER +/− ICER*MAPE."}],contributors:[{corresp:"yes",contributorFullName:"Marco Rao",address:"marco.rao@enea.it",affiliation:'
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
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Singh",slug:"a.-k.-singh",email:"singhanil@nitrkl.ac.in"}],book:{id:"7760",title:"Structure Processing Properties Relationships in Stoichiometric and Nonstoichiometric Oxides",slug:"structure-processing-properties-relationships-in-stoichiometric-and-nonstoichiometric-oxides",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"24934",title:"Dr.",name:"Speranta",surname:"Tanasescu",slug:"speranta-tanasescu",fullName:"Speranta Tanasescu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/24934/images/system/24934.jpg",biography:"Speranta Tanasescu, PhD, is Senior Researcher I and head of the Laboratory of Chemical Thermodynamics in the “Ilie Murgulescu” Institute of Physical Chemistry of the Romanian Academy, Bucharest. Dr. Tanasescu obtained a PhD in Physical Chemistry from the Romanian Academy in 1979 and has served as a supervisor in chemistry since 2002. She received the “Gh. Spacu” Award from the Romanian Academy in 1972. Her thematic research focuses on activities with impact in the following domains: materials science, nanoscience and nanotechnologies, new sources of energies, nanosafety, and nanomedicine. Her research is significant for understanding processing-structure relationships as well as for finding key parameters in relation to bio-reactivity of the nanomaterials with impact in both nanosafety and nanomedicine research.",institutionString:"Ilie Murgulescu Institute of Physical Chemistry of the Romanian Academy",institution:{name:"Romanian Academy",institutionURL:null,country:{name:"Romania"}}},{id:"24957",title:"Prof.",name:"Paula",surname:"Vilarinho",slug:"paula-vilarinho",fullName:"Paula Vilarinho",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"277555",title:"Dr.",name:"Netram",surname:"Kaurav",slug:"netram-kaurav",fullName:"Netram Kaurav",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"299545",title:"Mr.",name:"Paras",surname:"Dubey",slug:"paras-dubey",fullName:"Paras Dubey",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"302126",title:"Emeritus Prof.",name:"Jaroslav",surname:"Sestak",slug:"jaroslav-sestak",fullName:"Jaroslav Sestak",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Institute of Physics",institutionURL:null,country:{name:"India"}}},{id:"313769",title:"Dr.",name:"A. 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Gauckler",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"editorial-policies",title:"Editorial Policies",intro:'
All publications on this website are published under the Open Access model, without any subscription, registration, or access fees required from the user or his/her institution. In accordance with the Budapest Open Access Initiative's (BOAI) definition of Open Access, users are allowed to read, download, copy, distribute, print, search, and link to the full text versions of all Chapters. To read more about our Open Access Statement click here.
\n\n
For Editorial Policies for journals please consult individual journal pages.
All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
With the purpose of protecting our Authors' copyright and the transparent reuse of Open Access content, IntechOpen has developed an Attribution Policy for works published under Creative Commons licenses.
IntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
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Conflicts of Interest Policy
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In line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
IntechOpen's Authorship Policy is based on ICMJE criteria for authorship. In order to be identified as an Author, the following requirements must be met:
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A substantial contribution to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work
\\n\\t
Participation in drafting or revising the work
\\n\\t
Approval of the manuscript version to be published
All scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
The Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
To identify instances of fraud and misconduct during the publishing process, IntechOpen implements a robust policy governing such occurrences. In line with our general commitment to openness, and in order to maintain the highest scientific standards, we are committed to transparency about our editorial policy regarding retractions and corrections.
When faced with potential misconduct, IntechOpen accepts its responsibility to maintain the integrity of the academic record. For particularly complex cases, IntechOpen might ask for the assistance of formal industry bodies or seek advice from an appropriate team of advisors.
\\n\\n
IntechOpen's advisors are professionals and scholars with broad knowledge and understanding of different aspects of the scientific publishing process: editorial, authorship, and reviewing roles; publication ethics, copyright, and general legal issues; as well as bibliographic and technical standards.
\\n\\n
In order to provide us with unbiased insights, without compromising the privacy of third parties, IntechOpen presents problematic cases to its advisors in an anonymized format.
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Translation Policy
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IntechOpen publishes books in the English language. If you are interested in the translation of Book Chapters, please check IntechOpen's Translation Policy.
In line with the Principles of Transparency and Best Practice in Scholarly Publishing, you can access a more detailed description of IntechOpen's Advertising Policy.
At IntechOpen we realize that exceptional circumstances can occur, resulting in a request for a refund. We will honor all justified requests in the specific instances outlined in our Refund Policy.
All chapters will be published via IntechOpen's 'Online First' service meaning chapters will be published individually, immediately after review and before the entire book is ready for publication, allowing content to be shared, searched and cited straightaway, thereby generating early stage interest and momentum for your research
\\n\\n
Online First Chapters are considered published on the day they are posted and are citable from that date.
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Chapters will remain listed as Online First until the final versions of the books are published online. Following publication of the full monograph, Chapters will be redirected from the Online First version and will be available only through the final link of the official published page.
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You are invited to download, use, reproduce, make derivative works of, display, distribute and cite the Online First works. You can find "How to Cite and Reference" by following the link at the end of each online book chapter. Please be aware that it is possible that further editing and changes might be made before the final release of the book.
\\n\\n
If there are supplemental materials to the chapter, these will be published at the time the final book is published online.
\\n\\n
Readers and Authors can notify us if they find any errors in the works published under Online First. All major errors will be accompanied by a separate correction notice, erratum or corrigendum (Retraction and Correction Policy.)
\\n\\n
Access policy
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IntechOpen books are available online by accessing all published content on a chapter level.
All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
With the purpose of protecting our Authors' copyright and the transparent reuse of Open Access content, IntechOpen has developed an Attribution Policy for works published under Creative Commons licenses.
IntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
\n\n
Conflicts of Interest Policy
\n\n
In line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
IntechOpen's Authorship Policy is based on ICMJE criteria for authorship. In order to be identified as an Author, the following requirements must be met:
\n\n
\n\t
A substantial contribution to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work
\n\t
Participation in drafting or revising the work
\n\t
Approval of the manuscript version to be published
All scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
The Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
To identify instances of fraud and misconduct during the publishing process, IntechOpen implements a robust policy governing such occurrences. In line with our general commitment to openness, and in order to maintain the highest scientific standards, we are committed to transparency about our editorial policy regarding retractions and corrections.
When faced with potential misconduct, IntechOpen accepts its responsibility to maintain the integrity of the academic record. For particularly complex cases, IntechOpen might ask for the assistance of formal industry bodies or seek advice from an appropriate team of advisors.
\n\n
IntechOpen's advisors are professionals and scholars with broad knowledge and understanding of different aspects of the scientific publishing process: editorial, authorship, and reviewing roles; publication ethics, copyright, and general legal issues; as well as bibliographic and technical standards.
\n\n
In order to provide us with unbiased insights, without compromising the privacy of third parties, IntechOpen presents problematic cases to its advisors in an anonymized format.
\n\n
Translation Policy
\n\n
IntechOpen publishes books in the English language. If you are interested in the translation of Book Chapters, please check IntechOpen's Translation Policy.
In line with the Principles of Transparency and Best Practice in Scholarly Publishing, you can access a more detailed description of IntechOpen's Advertising Policy.
At IntechOpen we realize that exceptional circumstances can occur, resulting in a request for a refund. We will honor all justified requests in the specific instances outlined in our Refund Policy.
All chapters will be published via IntechOpen's 'Online First' service meaning chapters will be published individually, immediately after review and before the entire book is ready for publication, allowing content to be shared, searched and cited straightaway, thereby generating early stage interest and momentum for your research
\n\n
Online First Chapters are considered published on the day they are posted and are citable from that date.
\n\n
Chapters will remain listed as Online First until the final versions of the books are published online. Following publication of the full monograph, Chapters will be redirected from the Online First version and will be available only through the final link of the official published page.
\n\n
You are invited to download, use, reproduce, make derivative works of, display, distribute and cite the Online First works. You can find "How to Cite and Reference" by following the link at the end of each online book chapter. Please be aware that it is possible that further editing and changes might be made before the final release of the book.
\n\n
If there are supplemental materials to the chapter, these will be published at the time the final book is published online.
\n\n
Readers and Authors can notify us if they find any errors in the works published under Online First. All major errors will be accompanied by a separate correction notice, erratum or corrigendum (Retraction and Correction Policy.)
\n\n
Access policy
\n\n
IntechOpen books are available online by accessing all published content on a chapter level.
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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. 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It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.",book:{id:"10372",slug:"biomimetics",title:"Biomimetics",fullTitle:"Biomimetics"},signatures:"Khadeer Ahmed",authors:[{id:"320026",title:"Dr.",name:"Khadeer",middleName:null,surname:"Ahmed",slug:"khadeer-ahmed",fullName:"Khadeer Ahmed"}]},{id:"65418",title:"Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing",slug:"opening-the-black-box-of-silicon-chip-design-in-neuromorphic-computing",totalDownloads:1616,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"Neuromorphic computing, a bio-inspired computing architecture that transfers neuroscience to silicon chip, has potential to achieve the same level of computation and energy efficiency as mammalian brains. Meanwhile, three-dimensional (3D) integrated circuit (IC) design with non-volatile memory crossbar array uniquely unveils its intrinsic vector-matrix computation with parallel computing capability in neuromorphic computing designs. In this chapter, the state-of-the-art research trend on electronic circuit designs of neuromorphic computing will be introduced. Furthermore, a practical bio-inspired spiking neural network with delay-feedback topology will be discussed. In the endeavor to imitate how human beings process information, our fabricated spiking neural network chip has capability to process analog signal directly, resulting in high energy efficiency with small hardware implementation cost. Mimicking the neurological structure of mammalian brains, the potential of 3D-IC implementation technique with memristive synapses is investigated. Finally, applications on the chaotic time series prediction and the video frame recognition will be demonstrated.",book:{id:"6875",slug:"bio-inspired-technology",title:"Bio-Inspired Technology",fullTitle:"Bio-Inspired Technology"},signatures:"Kangjun Bai and Yang Yi",authors:[{id:"239041",title:"Dr.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi"},{id:"245542",title:"Mr.",name:"Kangjun",middleName:null,surname:"Bai",slug:"kangjun-bai",fullName:"Kangjun Bai"}]},{id:"58622",title:"Bio-inspired Adaptable Facade Control Reflecting User's Behavior",slug:"bio-inspired-adaptable-facade-control-reflecting-user-s-behavior",totalDownloads:1670,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"The purpose of this research is to develop the process of methodology in designing adaptable façade. This study focuses on the processes of façade operation control for each resident’s unit according to the user’s lifestyle. This study aims to develop the design methods that are applicable to the adaptable facade, which is inspired by the design inspiration of the biomimicry. The ideal façade to increase comfort in internal space is an adaptable façade that can constantly respond to changes in the environments. This chapter attempts in active adoption of adaptable facade that makes it possible to respond to changing requirements and environments, eventually enabling the creation of customized services for users. This chapter explores the processes of designing an adaptable façade controlled by three rules inspired by the behaviors of flocks of birds. This chapter shows how adopted bird intelligence can produce various façade controls. Also, this chapter demonstrates biomimetic façade control that has been implemented by behavior-based design. Through this demonstration, this chapter identifies the potentials of biomimetic design in facade using rules of bird flocking as source of design inspiration. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. 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Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. 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