Coefficient of prediction of ET0 for Mellegue catchment.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"8465",leadTitle:null,fullTitle:"Recent Trends in Computational Intelligence",title:"Recent Trends in Computational Intelligence",subtitle:null,reviewType:"peer-reviewed",abstract:"Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications.",isbn:"978-1-83880-706-1",printIsbn:"978-1-83880-705-4",pdfIsbn:"978-1-83880-707-8",doi:"10.5772/intechopen.78839",price:119,priceEur:129,priceUsd:155,slug:"recent-trends-in-computational-intelligence",numberOfPages:198,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"ed1a280abdc24c8367170d2aff2d1a68",bookSignature:"Ali Sadollah and Tilendra Shishir Sinha",publishedDate:"May 6th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8465.jpg",numberOfDownloads:8004,numberOfWosCitations:12,numberOfCrossrefCitations:21,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:32,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:65,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 13th 2019",dateEndSecondStepPublish:"September 17th 2019",dateEndThirdStepPublish:"November 16th 2019",dateEndFourthStepPublish:"February 4th 2020",dateEndFifthStepPublish:"April 4th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"147215",title:"Dr.",name:"Ali",middleName:null,surname:"Sadollah",slug:"ali-sadollah",fullName:"Ali Sadollah",profilePictureURL:"https://mts.intechopen.com/storage/users/147215/images/system/147215.jpeg",biography:"Ali Sadollah received his BSc degree in mechanical engineering, solid states, from Azad University, Semnan Branch, Iran in 2007 and MSc degree in mechanical engineering, applied mechanics, from the University of Semnan, Semnan, Iran in 2010. He obtained his Ph.D. at the Faculty of Engineering, the University of Malaya, Kuala Lumpur, Malaysia in 2013. He has also served as a postdoctoral research fellow for more than 2 years at Korea University, Seoul, South Korea. In 2016, for a year, he served as a research staff member at Nanyang Technological University, Singapore. Afterward, he was a guest assistant professor at Sharif University of Technology, Tehran, Iran for 2 years. Currently, he is an assistant professor in the Department of Mechanical Engineering at University of Science and Culture, Tehran, Iran. His research interests include algorithm development, optimization and metaheuristics, applications of soft computing methods in engineering, artificial neural networks, and computational solid mechanics.",institutionString:"University of Science and Culture",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Science and Culture",institutionURL:null,country:{name:"Iran"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"39255",title:"Dr.",name:"Tilendra",middleName:"Shishir",surname:"Sinha",slug:"tilendra-sinha",fullName:"Tilendra Sinha",profilePictureURL:"https://mts.intechopen.com/storage/users/39255/images/system/39255.jpeg",biography:"Dr. Tilendra Shishir Sinha, born in July 1968 in Dhanbad, Jharkhand, India, received his BE in Computer Technology from Nagpur University, Maharashtra, India, in 1992. He completed his MSc(Engineering) in Electrical Engineering (Power Electronics Specialization) from the National Institute of Technology, Jamshedpur, Jharkhand, India, in 2002. He moved on to complete his PhD(Engineering) in Computer Science & Engineering from the National Institute of Technology, Durgapur, West Bengal, India, in 2009. He has more than 25 years of experience in teaching, industry, and research. He is an IEEE member, ISTE member, and fellow member of IEI and Chartered Engineer in CSE. He has co-authored numerous publications in journals and conferences, and also published four book chapters in the area of natural language processing, speech processing, signal and image processing, information security, and cyber security. \nHis research interests include natural language processing, promoting global cyber security, information security through biometrical patterns, underwater objects detection and recognition, digital signal and morphological image processing, and applied engineering mathematics in various applications of core engineering.",institutionString:"Koneru Lakshmaiah Education Foundation",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"520",title:"Machine Learning",slug:"computer-and-information-science-artificial-intelligence-machine-learning"}],chapters:[{id:"71338",title:"Boundary Element Modeling and Optimization of Three Temperature Nonlinear Fractional Generalized Photo-Thermoelastic Interaction in Anisotropic Semiconductor Structures",doi:"10.5772/intechopen.91230",slug:"boundary-element-modeling-and-optimization-of-three-temperature-nonlinear-fractional-generalized-pho",totalDownloads:748,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The main objective of this paper is to introduce a new fractional-order theory called nonlinear fractional generalized photo-thermoelasticity involving three temperatures. Due to strong nonlinearity, it is very difficult to solve the wave problems related to this theory analytically. Therefore, we propose a new boundary element algorithm and technique for simulation and optimization of the considered problems related to this theory. The genetic algorithm (GA) as an optimization method has been applied based on free form deformation (FFD) technique to improve the performance of our proposed technique. In the formulation of the considered problem, the profiles of the considered objects are determined by FFD technique, where the FFD control point positions are treated as genes, and then the chromosome profiles are defined with the gene sequence. The population is established by a number of individuals (chromosomes), where the objective functions of individuals are achieved by the boundary element method (BEM). A nonuniform rational B-spline curve (NURBS) was used to model optimized boundary where it reduces the number of control points and provides the flexibility to design several different shapes for solving the considered photo-thermoelastic wave problems. The numerical results verify the validity and accuracy of our proposed boundary element technique.",signatures:"Mohamed Abdelsabour Fahmy",downloadPdfUrl:"/chapter/pdf-download/71338",previewPdfUrl:"/chapter/pdf-preview/71338",authors:[{id:"233766",title:"Prof.",name:"Mohamed Abdelsabour",surname:"Fahmy",slug:"mohamed-abdelsabour-fahmy",fullName:"Mohamed Abdelsabour Fahmy"}],corrections:null},{id:"70944",title:"Using Discrete Geometric Models in an Automated Layout",doi:"10.5772/intechopen.90941",slug:"using-discrete-geometric-models-in-an-automated-layout",totalDownloads:662,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The application of discrete (voxel) geometric models in computer-aided design problems is shown. In this case, the most difficult formalized task of computer-aided design is considered—computer-aided layout. The solution to this problem is most relevant when designing products with a high density of layout (primarily transport equipment). From a mathematical point of view, these are placement problems; therefore, their solution is based on the use of a geometric modeling apparatus. The basic provisions and features of discrete modeling of geometric objects, their place in the system of geometric modeling, the advantages and disadvantages of discrete geometric models, and their primary use are described. Their practical use in solving some of the practical problems of automated layout is shown. This is the definition of the embeddability of the placed objects and the task of tracing and evaluating the shading. Algorithms and features of their practical implementation are described. A numerical assessment of the accuracy and performance of the developed geometric modeling algorithms shows the possibility of their implementation even on modern computers of medium power. This allows us to hope for the integration of the developed layout algorithms into modern systems of solid-state geometric modeling in the form of plug-ins.",signatures:"Leonid V. Markin",downloadPdfUrl:"/chapter/pdf-download/70944",previewPdfUrl:"/chapter/pdf-preview/70944",authors:[{id:"312671",title:"Prof.",name:"Leonid",surname:"Markin",slug:"leonid-markin",fullName:"Leonid Markin"}],corrections:null},{id:"69613",title:"Effective Pitch Value Detection in Noisy Intelligent Environments for Efficient Natural Language Processing",doi:"10.5772/intechopen.89697",slug:"effective-pitch-value-detection-in-noisy-intelligent-environments-for-efficient-natural-language-pro",totalDownloads:582,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The performance of applications based on natural language processing depends primarily on the environment in which these applications are applied. Intelligent environments will be one of the major applications used to process natural language. The methods for speaker’s gender classification can adapt and improve the performance of natural language processing applications. That is why, this chapter will present an effective speaker’s pitch value detection in noisy environments, which then allows more robust speaker’s gender classification. The chapter presents the algorithm for the speaker’s pitch value detection and performs the comparison in various noisy environments. The experiments are carried out on the part of the publically available Aurora 2 speech database. The results showed that the automatically determined pitch values deviate, on average, only by 8.39 Hz from the reference pitch value. A well-defined pitch value allows a functional speaker’s gender classification. In this chapter, presented speaker’s gender classification works well, even at low signal to noise ratios. The experiments show that the speaker’s gender classification performance at SNR 0 dB is higher than 91% when the automatically determined pitch value is used. Speaker’s gender classification can then be used further in the processes of natural language processing.",signatures:"Damjan Vlaj, Andrej Žgank and Marko Kos",downloadPdfUrl:"/chapter/pdf-download/69613",previewPdfUrl:"/chapter/pdf-preview/69613",authors:[{id:"306398",title:"Dr.",name:"Marko",surname:"Kos",slug:"marko-kos",fullName:"Marko Kos"},{id:"306773",title:"Dr.",name:"Damjan",surname:"Vlaj",slug:"damjan-vlaj",fullName:"Damjan Vlaj"},{id:"306776",title:"Dr.",name:"Andrej",surname:"Žgank",slug:"andrej-zgank",fullName:"Andrej Žgank"}],corrections:null},{id:"70393",title:"Classification Problem in Imbalanced Datasets",doi:"10.5772/intechopen.89603",slug:"classification-problem-in-imbalanced-datasets",totalDownloads:949,totalCrossrefCites:4,totalDimensionsCites:7,hasAltmetrics:0,abstract:"Classification is a data mining task. It aims to extract knowledge from large datasets. There are two kinds of classification. The first one is known as complete classification, and it is applied to balanced datasets. However, when it is applied to imbalanced ones, it is called partial classification or a problem of classification in imbalanced datasets, which is a fundamental problem in machine learning, and it has received much attention. Considering the importance of this issue, a large amount of techniques have been proposed trying to address this problem. These proposals can be divided into three levels: the algorithm level, the data level, and the hybrid level. In this chapter, we will present the classification problem in imbalanced datasets, its domains of application, its appropriate measures of performances, and its approaches and techniques.",signatures:"Aouatef Mahani and Ahmed Riad Baba Ali",downloadPdfUrl:"/chapter/pdf-download/70393",previewPdfUrl:"/chapter/pdf-preview/70393",authors:[{id:"300915",title:"Dr.",name:"Aouatef",surname:"Mahani",slug:"aouatef-mahani",fullName:"Aouatef Mahani"},{id:"310335",title:"Dr.",name:"Ahmed Riadh",surname:"BABA ALI",slug:"ahmed-riadh-baba-ali",fullName:"Ahmed Riadh BABA ALI"}],corrections:null},{id:"71118",title:"Applications of Mining Arabic Text: A Review",doi:"10.5772/intechopen.91275",slug:"applications-of-mining-arabic-text-a-review",totalDownloads:717,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches.",signatures:"Qasem Al-Radaideh",downloadPdfUrl:"/chapter/pdf-download/71118",previewPdfUrl:"/chapter/pdf-preview/71118",authors:[{id:"303628",title:"Prof.",name:"Qasem",surname:"AlRadaideh",slug:"qasem-alradaideh",fullName:"Qasem AlRadaideh"}],corrections:null},{id:"71286",title:"Categorizing Patient Disease into ICD-10 with Deep Learning for Semantic Text Classification",doi:"10.5772/intechopen.91292",slug:"categorizing-patient-disease-into-icd-10-with-deep-learning-for-semantic-text-classification",totalDownloads:742,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"How to leverage insights into big electronic health records (EHRs) becomes increasingly important for accomplishing precision medicine to improve the quality of human healthcare. When analyzing big Chinese EHRs, there are a lot of applications that we need to categorize patients’ diseases according to the medical coding standard. In this paper, we develop natural language processing (NLP), deep learning, and machine learning algorithms to automatically categorize each patient’s individual diseases into the ICD-10 standard. Experimental results show that the convolutional neural network (CNN) algorithm outperforms the recurrent neural network (RNN)-based long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms, and it generates much better results than the support vector machine (SVM), one of the most popular conventional machine learning algorithms, demonstrating the great impact of deep learning on medical big data analysis.",signatures:"Junmei Zhong and Xiu Yi",downloadPdfUrl:"/chapter/pdf-download/71286",previewPdfUrl:"/chapter/pdf-preview/71286",authors:[{id:"306304",title:"Ph.D.",name:"Junmei",surname:"Zhong",slug:"junmei-zhong",fullName:"Junmei Zhong"}],corrections:null},{id:"71081",title:"Deep Learning Approach to Key Frame Detection in Human Action Videos",doi:"10.5772/intechopen.91188",slug:"deep-learning-approach-to-key-frame-detection-in-human-action-videos",totalDownloads:814,totalCrossrefCites:5,totalDimensionsCites:6,hasAltmetrics:0,abstract:"A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of constituting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.",signatures:"Ujwalla Gawande, Kamal Hajari and Yogesh Golhar",downloadPdfUrl:"/chapter/pdf-download/71081",previewPdfUrl:"/chapter/pdf-preview/71081",authors:[{id:"312168",title:"Dr.",name:"Ujwalla",surname:"Gawande",slug:"ujwalla-gawande",fullName:"Ujwalla Gawande"},{id:"312350",title:"Mr.",name:"Kamal",surname:"Hajari",slug:"kamal-hajari",fullName:"Kamal Hajari"},{id:"312351",title:"Prof.",name:"Yogesh",surname:"Golhar",slug:"yogesh-golhar",fullName:"Yogesh Golhar"}],corrections:null},{id:"68953",title:"Machine Translation and the Evaluation of Its Quality",doi:"10.5772/intechopen.89063",slug:"machine-translation-and-the-evaluation-of-its-quality",totalDownloads:1482,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Machine translation has already become part of our everyday life. This chapter gives an overview of machine translation approaches. Statistical machine translation was a dominant approach over the past 20 years. It brought many cases of practical use. It is described in more detail in this chapter. Statistical machine translation is not equally successful for all language pairs. Highly inflectional languages are hard to process, especially as target languages. As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future. This chapter also describes the evaluation of machine translation quality. It covers manual and automatic evaluations. Traditional and recently proposed metrics for automatic machine translation evaluation are described. Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive. Integration of human and machine translation is a promising workflow for the future. Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process.",signatures:"Mirjam Sepesy Maučec and Gregor Donaj",downloadPdfUrl:"/chapter/pdf-download/68953",previewPdfUrl:"/chapter/pdf-preview/68953",authors:[{id:"305414",title:"Associate Prof.",name:"Mirjam",surname:"Sepesy Maučec",slug:"mirjam-sepesy-maucec",fullName:"Mirjam Sepesy Maučec"},{id:"309288",title:"Dr.",name:"Gregor",surname:"Donaj",slug:"gregor-donaj",fullName:"Gregor Donaj"}],corrections:null},{id:"70785",title:"Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges",doi:"10.5772/intechopen.90810",slug:"pedestrian-detection-and-tracking-in-video-surveillance-system-issues-comprehensive-review-and-chall",totalDownloads:1309,totalCrossrefCites:7,totalDimensionsCites:10,hasAltmetrics:0,abstract:"Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed.",signatures:"Ujwalla Gawande, Kamal Hajari and Yogesh Golhar",downloadPdfUrl:"/chapter/pdf-download/70785",previewPdfUrl:"/chapter/pdf-preview/70785",authors:[{id:"312168",title:"Dr.",name:"Ujwalla",surname:"Gawande",slug:"ujwalla-gawande",fullName:"Ujwalla Gawande"},{id:"312350",title:"Mr.",name:"Kamal",surname:"Hajari",slug:"kamal-hajari",fullName:"Kamal Hajari"},{id:"312351",title:"Prof.",name:"Yogesh",surname:"Golhar",slug:"yogesh-golhar",fullName:"Yogesh Golhar"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6806",title:"Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications",subtitle:null,isOpenForSubmission:!1,hash:"fedf4479b910cbcee3025e391f073417",slug:"fuzzy-logic-based-in-optimization-methods-and-control-systems-and-its-applications",bookSignature:"Ali Sadollah",coverURL:"https://cdn.intechopen.com/books/images_new/6806.jpg",editedByType:"Edited by",editors:[{id:"147215",title:"Dr.",name:"Ali",surname:"Sadollah",slug:"ali-sadollah",fullName:"Ali Sadollah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7607",title:"Recent Trends in Artificial Neural Networks",subtitle:"from Training to Prediction",isOpenForSubmission:!1,hash:"b7f92b054dddc49df32d4f9c9e71507c",slug:"recent-trends-in-artificial-neural-networks-from-training-to-prediction",bookSignature:"Ali Sadollah and Carlos M. 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\r\n\tThe development of RNA therapeutics has been an intense journey, with numerous stories of success and failure. The potential, and suitability, of recently discovered RNAs (for therapeutics), stemmed from several Nobel Prize-winning discoveries. Currently, novel RNA drugs are entering clinical trials almost daily. RNA therapeutics are chemically synthesized biomolecules with broad clinical applications, ranging from correcting inherited mutations, to treating cancer, and chronic conditions, improving organ transplant outcomes, and infectious disease prophylaxes.
\r\n\tRNA therapies evolved as profitable and widely applicable individualized treatment solutions. Moreover, RNA-based therapeutic vaccines (e.g., against SARS-CoV-2 infection) have been proven to be safe and effective, and several of them are approved by the United States Food and Drug Administration (FDA).
\r\n\tThis book aims to present distinct classes of RNA therapeutics, ranging from single-stranded antisense oligonucleotides (ASOs), and subclasses of RNA interferences (miRNAs and other RNAi), to in vitro transcribed mRNAs and RNA vaccines. Also, it will present some of the challenges in RNA drug engineering, delivery, and specificity. Additionally, the improvement of pharmacological effectiveness will be discussed. Monumental breakthroughs in molecular biology, computational chemistry, bioinformatics, and individualized genomics, which undoubtedly propelled RNA therapeutics through the commercialization stage, will also be examined in this book.
\r\n\tRNA therapeutics have had a significant impact on medicine, the economy, and overall public health; they are becoming prescription drugs, and this holds great promise for modernizing healthcare.
Oxygen radicals are continuously formed in all living organisms, with deleterious effects that lead to cell injury and death. Production of oxidative species occurs under physiological conditions at a controlled rate, but it is dramatically increased in conditions of oxidative stress. Reactive oxygen species (ROS) is a term which encompasses all highly reactive, oxygen-containing molecules, including free radicals [1, 2]. Free radicals are an atom or molecule that bears an unpaired electron and is extremely reactive, capable of engaging in rapid change reaction that destabilize other molecules and generate many more free radicals [3, 4]. Vitamin E compounds (tocopherols and tocotrienols) are well recognized for their effective inhibition of lipid oxidation in food and biological systems [5, 2]. Carotenoids can act as primary antioxidants by trapping free radicals or as secondary antioxidants by quenching singlet oxygen. In foods, carotenoids usually act as a secondary antioxidant. Beta-carotene is found in many foods that are orange in color [6].
Stress plays a significant role in the development of atherosclerotic heart disease (AHD) [7]. A stressful condition leads to the excessive production of free radicals which results in oxidative stress an imbalance in the oxidant per antioxidant system [8]. Under normal conditions, there is a natural defense system provided by several enzymes such as superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px) which performs a vital role for detoxification of free radicals. The use of antioxidant rich food or antioxidant food supplements became immensely popular since many diseases have been associated with oxidative stress [9]. Antioxidant enzyme such as superoxide dismutase (SOD) is an important radical superoxide scavenger and it plays an important role in cell protection [10, 2]. Therefore, this CAT or SOD enzyme are very good biochemical markers of stress and their increased activity may attest to a potential for remediation [11]. Inherent antioxidant defense systems consisting of enzymes, such as catalase (CAT), and superoxide dismutase (SOD), an antioxidant nutrients may participate in coping with oxidative stress. As antioxidant enzymes have an important role in the protection against free radical damage, a decrease in the activities or expression of these enzymes may predispose tissues to free radical damage [12].
Red palm oil (RPO) is extracted from the oil palm (
Corn oil presents a relatively high concentration of polyunsaturated fatty acids (PUFA).Due to the high levels of unsaturation these lipids are highly susceptible to free radical oxidative reactions, giving rise to the formation of lipid peroxides. Many investigations suggest that a large number of polyunsaturated fatty acids produces more lipid peroxides and may have mutagenic activity [18, 19].
Coconut oil is a colorless to pale, brownish yellow oil [20]. It is the major sources of saturated fat apart from palm kernel. They are the only natural sources of lauric oil available to the world market. Coconut oil is the principal cholesterol-raising fat because it contains large amounts of lauric (C: 12: O) and myristic (C: 14: 0) acids [21]. Therefore the objective of this study is to investigate the effect of different concentration of red palm olein and different vegetable oils on antioxidant enzymes in normal and stressed rats.
Due to the importance of the role of antioxidants in protection against the oxidative stress which lead to many dangerous diseases such as heart diseases and cancer thus this study was done to investigate the effect of natural antioxidants particularly vitamin E and beta carotene in red palm olein on antioxidant enzymes and compared the results with four different vegetable oils in normal and stress conditions of rats.
The evaluated red palm olein (RPO) samples consisted of carotenes (576 ppm), vitamin E (>800 ppm) and free fatty acids (0.045%) provided by Carotino SDN BHD company and palm olein (PO) (Seri Murni), corn oil (CO) and coconut oil (COC) were obtained commercially. For the first group the test diet was prepared by mixing RPO with normal commercial rat pellet to contain 5%, 10% and 15% of the red palm olein (RPO). The 5% diet was prepared by adding 5g RPO to 95g rat pellet, and mixed manually and the diets were then left to absorb the RPO at room temperature overnight and stored at 20o C before the feeding trial was conducted. Similar process was conducted with 10%, and 15% RPO. For second group the test diet was prepared by mixing vegetable oils with normal commercial rat pellet to contain 15% of the vegetable oils. The 15% diet was prepared by adding 15g RPO, PO, CO or COC to 85g rat pellet, and mixed manually and the diets were then left to absorb the vegetable oils at room temperature overnight and stored at 20o C before the feeding trial was conducted.
Normal (N) group: Rats were maintained under standard laboratory conditions and fed with respective diet till the completion of the experiment.Normal rats
Stressed rats
One hundred and eighty Sprague Dawley male rats each weighing between 170-250g and approximately 80 days old were obtained from the animal house of the Faculty of Science and Technology, Universiti Kebangsaan Malaysia. They were divided into three groups.
The first group contains 78 rats were divided into 13 groups of 6 rats per group. The rats were fed
Procedure for collecting liver from rat
A 0.2 g sample of liver was cut to small pieces. Tissue was suspended in 2 ml of 50 mM phosphate buffer (pH 7.4), and was homogenized using a mixer at top speed for 3 min. Afterwards, the homogenate was centrifuged at 20000 g for 25 min. In this process the temperature was maintained at 4C0 during the homogenization process. Phosphate buffer was prepared based on Aebi’s method [22]. Phosphate buffer 50 mM, PH 7.0: dissolve (a) 6.81 g KH2PO4, and (b) 8.90 g Na2HPO4.2H2O in distilled water and make up to 100 ml each mix solution (a) and (b) in proporation 1:1.5 (v/v).
Enzyme activity of catalase (EC.1.11.1.6) was determined based on Aebi’s method [22]. Catalase activity was measured at 22°C by monitoring the decomposition of hydrogen peroxide. The reaction mixture consisted of 2.0 mL of the liver homogenate suspended in phosphate buffer (50 mM, pH 7.0), and 1.0 mL of hydrogen peroxide solution (30 mM). The absorbance was recorded for 2 minutes at 240 nm immediately after adding hydrogen peroxide solution. Catalase activity was expressed as moles of hydrogen peroxide reduced/min/mg protein.
Activity superoxide dismutase (EC.1.6.4.2) was assayed based on the method of Marklund and Marklund [23]. Superoxide dismutase activity was determined at 22°C by using the pyrogallol. The reaction mixture consisted of 50 mM of cacodylic acid buffer pH 8.2, containing 1mM EDTA, 300 µl of liver homogenate, 300 µl of 0.2 mM pyrogallol. The absorbance was recorded for 3 minutes at 420 nm immediately after adding the pyrogallol solution. Superoxide dismutase activity was expressed as units of SOD/minute/mg protein.
Protein concentrations were determined based on the Lowry method [24]. To 0.1 mL of sample or standard was added 0.1 mL of 2
Protein standard curve
Antioxidant is an important part of a cells defense against free radical damage. Antioxidant enzymes, in particular, constitute a major part of this defense [25]. It is evident from earlier work that different concentrations RPO have differential effects on the activities of antioxidant enzymes [10]. Figures 5, 6 and 7 showed the results of catalase activity at different concentrations of RPO (5%, 10% and 15%) for different times (2, 4 and 8 weeks) of treatment. After 2 weeks there was no significance difference (p≥0.05) between the control group and 10% and 15% concentrations of RPO while at 5% there was an increased in the catalase activity. At 4 weeks there was no significance difference (p≥0.05) between the control group and different concentrations groups (5%, 10%, and 15%) of RPO. At 8 weeks there was no significance difference between the control group and 5% group while at 10% and 15% there was decreasing of the catalase activity but there was no significance difference (p≥0.05).
The catalase activity (u/mg) in liver of rats fed with different type of red palm oil (0%, 5%, 10% and 15%) for 2 weeks. Bars are mean ± SEM (n=6), no significantly different (p>0.05).
The catalase activity (u/mg) in liver of rats fed with different type of red palm oil (0%, 5%, 10% and 15%) for 4 weeks. Bars are mean ± SEM (n=6), no significantly different (p>0.05).
The catalase activity (u/mg) in liver of rats fed with different type of red palm oil (0%, 5%, 10% and 15%) for 8 weeks. Bars are mean ± SEM (n=6), no significantly different (p>0.05).
Figures 8, 9 and 10 showed the results of SOD activity at different concentration of RPO (5%, 10% and 15%) for different times (2w, 4w and 8w) of treatment. After 2 weeks there was an increased in SOD activity at 5% while there was a decreased in SOD activity at 10% and 15% groups. However, there were no significance differences (p≥0.05) among these groups. On the contrary at 4 weeks the SOD activity increased with increasing duration of treatment in all concentrations compared to the control group. There was no significant (p≥0.05) increased in SOD activity at 15% concentration of RPO.
At 8 weeks there was no significant difference (p≥0.05) between the control group and all treatment groups of RPO except there was decreased in SOD activity at 15% of RPO. Therefore, after 4 weeks the activity of SOD was significantly higher (p≤0.05) at 15% of RPO dietary group compared to the control group but the increase in the 10% of RPO dietary group was not statistically significant. On the other hand, there was a significantly decreased (p<0.05) in 15% of RPO dietary group after 2 and 8 weeks.
The results of this study showed that 15% treatment of RPO which contain β-carotene and vitamin E for 4 weeks may enhance the antioxidant enzyme (SOD) defence system. These results thus suggest that a combination of carotenoids and vitamin E (tocopherol and tocotrinol) in the RPO has an important role in the protection against free radical damage. Red palm oil contains the highest concentration of tocotrienols compared to other vegetables or plants and the tocotrienols can be 40-60 times more potent as anti-oxidant than tocopherols [26]. Tocotrienols are free radical scavenging antioxidants, however, only the α-isomer has considerable biological antioxidant activity. It is therefore not surprising that there are relatively very few studies on their antioxidative effects in oils and fats [2, 26].
Although a few of studies explicitly show the effects of vitamin E on the activities of antioxidant enzymes, there is no consensus on what might be the responses of antioxidant enzymes to vitamin E, partly because of different feeding behavior and other ecological conditions [27].
The superoxide dismutase (SOD) activity (u/mg) in rat liver fed with different type of red palm oil (0%, 5%, 10% and 15%) for 2 weeks. Bars are mean ± SEM (n=6), no significantly different (p>0.05) at all treated groups with RPO.
The superoxide dismutase (SOD) activity (u/mg) in rat liver fed with different type of red palm oil (0%, 5%, 10% and 15%) for 4 weeks. Bars are mean ± SEM (n=6), no significantly different (p>0.05) at 5% and 10%, significantly different (p<0.05) at 15%
The superoxide dismutase (SOD) activity (u/mg) in rat liver fed with different type of red palm oil (0%, 5%, 10% and 15%) for 8 weeks. Bars are mean ± SEM (n=6), no significantly different (p≥0.05).
The results of CAT activity at different vegetable oils (RPO, PO, CO and COC) for different times (4 and 8 weeks) of treatment are summarized in Figures 11 and 12. After 4 weeks there was no significance different (p≥0.05) between control group and different vegetable oils treated groups while at 8 weeks there was significance decreased (p≤0.05) in PO,CO and COC groups compared to control group but the CAT liver sample was no significant different (P≥0.05) between control group and RPO group.
Several studies have illustrated that RPO is a rich cocktail of lipid-soluble antioxidants such as carotenoids (α- and β-carotene, lycopenes), vitamin E (in the form of α-, β-, δ- tocotrienols and tocopherol) [27]. Red palm oil has 17,500 mg of β-carotene per 100 g, and 28,000 mg of α-carotene per 100 g for a total of 6,140 retinol equivalents per 100 g. Thus, it has good potential for routine diets with enrichment carotenoids [28]. Red palm fruit oil (RPO) contains about 15 times more carotenes than that present in the same weight of carrots, and 44 times that of leafy vegetables [29]. Palm oil is a rich source of vitamin E, having both tocotrienols and tocopherols [30, 31].
The catalase activity (CAT) in rat liver fed with different vegetable oils for 4 weeks. Bars are mean ± SEM (n=6), no significantly different (p≥0.05).
The catalase activity (CAT) in rat liver fed with different vegetable oils for 8 weeks. Bars are mean ± SEM (n=6), different alphabet an each bar indicate significant different (P≤0.05).
The results of SOD activity at different vegetable oils (RPO, PO, CO and COC) for different times (4 and 8 weeks) of treatment are summarized in Figures 13 and 14. After 4 and 8 weeks there was no significance different (p≥0.05) between control group and different vegetable oils treated groups.
The superoxide dismutase (SOD) activity in rat liver fed with different vegetable oils for 4 weeks. Bars are mean ± SEM (n=6), no significantly different (p≥0.05).
Mean superoxide dismutase (SOD) activity in rat liver fed with different vegetable oils for 8 weeks. Bars are mean ± SEM (n=6), no significantly different (p≥0.05).
The results from the present study, after different times, showed that under sedentary conditions,
This study finding was similar to that of Rathnagiri et al. [33] who reported that there were no statistically significant differences between control fed rats with respect to SOD activity in the corn oil. The effect of COC on antioxidant enzyme in this study was not in agreement with Anitha and Lokesh [34] who found that COC increased significantly of SOD activity in the liver but Anitha and Lokesh [34] used coconut oil with groundnut oil or olive oil instead of COC. Vitamins directly scavenge ROS and regulate the activities of antioxidant enzymes. Among them, vitamin E has been recognized as one of the most important antioxidants [27].
This probably involves their actions as antioxidants, reducing the level of free radicals and hence free radical damage. Antioxidant enzymes, such as superoxide dismutase (SOD) play a major role in removing the Reactive Oxygen Species (ROS) [11]. At this time point, it is suggested that different experimental period might lead to different result about the effect of dietary vitamin E on the activities of antioxidant enzymes [27]. In the present study, the 2 weeks period in which this experiment was carried out may be insufficient to witness any change in the activity of this enzyme.
In addition to this, Yazar and Tras [35] reported that prior induction of ROS could cause an increase intracellular SOD activity. Hence first induction of ROS may cause changes in SOD activity and then SOD activity may return to the normal level. SOD enzyme, together with CAT, protects cells against damage caused by free radicals and hydrorlipoperoxides [36]. According to Catherine et al. [37] vitamin E work synergistically to decrease the multiplication of free radicals. Vitamin E inhibits the production of lipid hydroperoxide. However, reduced SOD activities may also indicate increased lipid peroxidation end-products like acid thiobarbituric [38]. Intricately linked to lipid peroxidation are antioxidant enzymes such as SOD and catalase. As a defense against reactive free radicals, the body produces antioxidant enzymes which help to mop them up [39].
As red palm olein was shown to reduce MDA production and increase SOD in 15% group for 4 weeks, it would spare the retina from damage. This effect of palm oil may be related to the ability of β-carotene to quench free radicals and prevent tissue damage [40]. The relatively lower cholesterol level in treated rats and higher antioxidant enzyme activity could be viewed as potentially beneficial for the health of the user population in humans [41].
Presence of high amount of unsaturated fatty acids may be the reason for the low antioxidant enzyme activities of some vegetable oils fed rat since polyunsaturated fatty acids (PUFA) deteriorates the antioxidant status due to their liability to become highly oxidized. Feeding oils high in polyunsaturated fatty acids (PUFA) results and increase the oxidative stress since PUFA are highly susceptible to peroxidation than monounsaturated and saturated fatty acid [42].
A) Stressed control group, (B) Stressed red palm olein group, (C) Stressed palm olein group
Figure 16 shows the results of CAT activity in liver samples of normal and stressed rats that were treated with 15% of RPO and PO for 4 weeks of treatment. After 4 weeks, there was no significant difference (P≥0.05) between control group and 15% RPO and PO normal groups whereas there was significant decreased (P≤0.05) between control group and 15% RPO stressed group and there was significantly higher (P≤0.05) in 15% PO stressed group than the control group. This study finding were similar to that of Benson and Kshama [7] who reported that the CAT activity in RPO group has shown significant decrease compared to PO and RPO groups under stress conditions.
Many recent studies emphasize the important role of reactive oxygen species (ROS) in the pathogenesis of various liver diseases. Stress known to increase oxidative stress in the major organs including the liver [42].
The catalase (CAT) activity in normal and stressed rats fed with red palm olein and palm olein for 4 weeks. Bars are mean ±SEM (n=6), different alphabet an each bar indicate significant different (P≤0.05).
Figure 17 shows the results of SOD activity in liver samples of normal and stressed rats that were treated with 15% of RPO and PO for 4 weeks of treatment. After 4 weeks, there was significantly lower (P≤0.05) in 15% RPO and PO normal and stressed groups than the control group. Vitamin E is a major antioxidant vitamins found in the cell and can prevent cell damage through its activity as a free radical chain breaker [43]. Free radicals have been implicated in the etiology of large number of major diseases. They can adversely alter many crucial biological molecules leading to loss of form and function. Such undesirable changes in the body can lead to diseased conditions. Antioxidants can protect against the damage induced by free radicals acting at various levels [43].
β-Carotene has received considerable attention in recent times as a putative chain-breaking biological antioxidant and its ability to interact with free radicals such as peroxyl radicals and to scavenge and quench singlet oxygen is well documented [44]. Defense mechanisms against free radical-induced oxidative damage include the catalytic removal of free radicals and reactive species by factors such as catalase (CAT), superoxide dismutase (SOD) and reduction of free radicals by electron donors, Such as vitamine E (tocopherol and tocotrienol) [45].
The superoxide dismutase (SOD) in normal and stressed rats fed with red palm olein and palm olein for 4 weeks. Bars are mean ±SEM (n=6), different alphabet an each bar indicate significant different (P≤0.05).
In conclusion, palm oil may offer some protection to liver of the treated rats by reducing free radicals damage, as well as increasing SOD. The present study shows no significant difference in level of catalase in control group and different concentration groups of RPO treatment but after 4 weeks 15% of RPO was enhanced the SOD activity level in rat liver. It can be concluded that the effect of different concentrations of RPO appear to depend on the different period of treatment. The current study shows no significant difference in level of catalase in control group and RPO group but the treated rat liver with PO, CO and COC groups were the lowest and it were significantly lower than control group. After 4 weeks of treatment, 15% of RPO enhances the SOD activity level in rat liver.These results could be due to the high content of vitamin E (tocopherols and tocotrienols) and β-carotene in red palm olein. Treatment with 15% RPO and PO diets did not affect the CAT activity after 4 weeks of treatment under normal condition while there was decreased in CAT activity with RPO and increased with PO under stress conditions. Additionally, the results in RPO group showed that higher SOD activity compared to PO and control groups under normal conditions while there were no significant difference (P≤0.05) in SOD between the control group and treated groups under stress conditions.
This work was supported by the Organization for Women in Science for the Developing World (OWSDW) and the research was funded by UKM-GUP-NBT-27-103, UKM-HEJIM-Industri-16-2010 and UKM OUP-NBT-29-139/2011 We grateful thank to caroteno sdn bhd Malaysia for providing red palm olein sample
Weather forecast is playing a vital role in society, environment, and sociable development. Their utilities have exceeded the simple mission of providing information for the users about the weather behavior during the upcoming period. Indeed, the whole concept was changing where it has been applied recently for politics and deciders to reduce socioeconomic losses that could be potentially generated by climate, which plays a substantial role in assuring life quality and economic prosperity. For example, the United States has cited 96 natural disasters that occurred between 1980 and 2006, with total losses that exceed 700 billion dollars. Up to now, around 629 fatalities per year are directly caused by weather disasters. Moreover, more than 60,000 premature deaths are recorded annually and are originally caused by poor air quality. Additionally, more than 1.5 million road crashes are originally caused by weather causing 7400 deaths, 700,000 injuries, and around 42 billion losses. Economically, more than 42 billion dollars are estimated to be lost due to weather traffic delays [1]. On the other hand, researchers are using weather forecasting to set new strategies that benefit the most from weather changes. Environmentalists are rather focusing on protecting the ecosystem from any possible climate change threats.
The services of weather are rising sharply. Gained profits from weather forecasts and warning disasters have reached over 31 billion dollars. Eventually, good forecasting will automatically lead to earlier warming and thus to good precautions, which will eventually contribute to reduce weather fatalities and economic loss. Technics of observing and studying weather behavior are continuously progressing, but understanding how the climate reacted in the future is still often too hard to simulate. Related to this point, the diversity of machine learning algorithms has led to predict various climate responses, providing more and more accurate forecasts. Since then, details that deal with the numerical and spatial resolution are being continuously developing, acquiring more challenging computing capabilities.
In the meanwhile, the climate is still too sensitive and chaotic. Even so, it is often too hard to obtain the perfect forecast, but ideally, every weather forecast model needs to get a certain measure of confidence, depending on many external parameters (type of data, coordinates, altitude, etc.). Generally, the best forecast model is the one that implements voluminous data and various methods/models. We must therefore limit the potential parameters that influence mostly the behavior of climate. As a consequence, machine learning algorithms can generate multiple forecast scenarios with slightly different initial inputs and/or changes in some stochastic parametrizations or model formulations. The forecast uncertainty is dependent mostly on the initial states of the atmosphere where it was observed, plus a certain random factor. Any changes in the format of weather data can modify the real observed status of the atmosphere at a certain point. Consequentially, a generated model needs much time between processing and delivering to the users.
Machine learning is a field of computer science based on artificial intelligence. The whole concept is based on provide learning capability to the users (computers or other devices) without being explicitly operated. It aims to conceive a suitable models and algorithms to learn and forecast based on input data [2, 3]. In addition, machine learning algorithms are efficiently used to describe the behavior of the dataset, dealing with noisy and nonstationary data. It uses a model input features by producing an expected output and forecasts suitable output features established on its historical records. Given the wide availability of weather data, fast and accurate decision-making is becoming a vital and more important than ever. Therefore, machine learning algorithms are one of the best alternatives of forecasting weather behavior. Besides, they can easily adapt themselves to changing trends inside datasets and can thus generate models based on input data instead of applying a conventional generalized model.
Many research studies have spoken about weather forecasting using several analysis methods. Preliminary studies about weather forecasting were based on persistence and statistical methods such as regression models [4]. These models are the most common approaches that use statistical technique for weather prediction. One of the most famous regression models is polynomial approach, which provides an effective way to describe complex and voluminous dataset in a nonlinear form. Additionally, polynomial regression models are based on the observed relationship between the dependent and independent variables to find out the most suitable polynomial equation order.
Therefore, the present chapter lays out the outcome of using the polynomial regression models for weather forecasting and to expose all the factors and/or parameters that can potentially affect the efficiency of the prediction. We will reveal the risk of dealing with big data volumes and how the format of the input data will affect the accuracy of the model. We will review the application of the Pearson correlation in retrieving or homogenizing the data and how it can affect the prediction climate behavior. We will discuss the best polynomial algorithm that fits mostly with the type of the data and its ability to generate a valid, concrete and unquestionable weather forecast model.
Meteorological data are derived originally from two sources. In the first case, daily climate data were collected and processed in AquaCrop to calculate a yearly average for every one of the following parameters: maximum and minimum temperature (
The main algorithm used in this study is polynomial regression. It has been widely applied, and its statistical tools are famous [5]. Generally speaking, it is a form of linear regression that is why we do call it sometimes “polynomial linear regression model.” It is a form of regression analysis that links the independent variable (
Hence, we can find in some polynomial equations a certain factor (θ) called residual error.
In another way, we can define polynomial regression as a form of linear regression between dependent and independent variables where we add some terms or factors (based on a curvilinear relationship) to convert it into a polynomial regression. Nonetheless, we can simply return to the linear regression model. In that case, the previous equation will be:
The general behavior of any climate data is set to be a nonlinear way. As a result, the linear regression model will be too difficult to visualize or predict entirely the data. For that reason, it will be too hard to draw the best line that fits mostly the weather data. The performance of the model will be too far from reality, and the projection of the weather forecast will be, consequently, too doubtful. In this case, we opt for the polynomial regression to fit the data graph with a low value of error (Figure 1).
Difference between linear and polynomial regression.
Python has several methods for finding the curvilinear relationship between data points. Based mainly on mathematic equations, the algorithm will succeed in drawing the best polynomial regression line that fits the most with the original input data. Later on, we can use the collected information to predict future values with a specific timescale. In Python, many packages were imported to operate this algorithm, but the main needed libraries are: “pandas,” “matplotlib,” “numpy,” and “sklearn.”
The process is quite easy with Python (Figure 2). The first step consists of importing major libraries especially “Pandas,” “numpy,” and “sklearn.” You need to import the CSV file, which must have at least two columns; the column A contains the date (you have the right to put the conventional format of date you want, but be aware that it must be understood by Python!), and the second column that contains the observed values (which corresponds to the climate stats in our cases). Next, you have to specify the corresponding column to every variable and order the value gradually, by using the imported library “numpy.” Going this far, all you have to do next is to apply the corresponded polynomial order that ideally fits with the observed data. You can assess the behavior of the polynomial regression graphically (using the libraries matplotlib) or statically (
Script of polynomial regression model in python.
Statically: based on
By definition,
Graphically: based on back-forecasting
When we say predict, it is often related to anticipate the future response based on past or actual values. Instead, we want to compare the forecast equation with real data. So, here we do invent the conception of back-forecasting. Generally, any time series model looks the same by going forward or backward and can, therefore, predict the future as well as the past. Meaning instead of going forward, we use the model to go backward in time and predict the past. The model uses two backwards passes. The first one is used to calculate early data by the estimating parameters while the second one is made to establish the forecasting equation to calculate the forward values (future). In Figure 3, we used the back-forecasting method to squeeze most information out of the 468 monthly data (1981 to 2019). The more back-forecasted graph is close to the real data plot, the more accurate the forecasting equation will be.
Figure assimilation between back-forecasting plot (based on ARIMA (1,1,1)) and real rainfall values of Zaghouan.
Also known as the bivariate correlation, it is used to measure the strength of two sets of data based on the linear relationship. Mathematically speaking, it refers to the ratio between the covariance and the standard deviation of two variables (Eq. (3)). As such formula, the resultant value will always set between −1 and 1 where values close to 1 are referring to good correlation as the non-correlated values are close to zero. Generally speaking, the absolute values of the given correlation will be automatically linked to the strength of the values (negative values that are close to −1 are indicating that the two variables are inversely correlated, meaning that the rise of the first variable will lead to a decrease of the other).
The general equation of
Where
Cov(
σ: Standard deviation.
We have to test the relationship between the data based on the Pearson coefficient. By proceeding this correlation, we have neglected all data from the stations that have a correlation less than 0.6 as it is recommended in some studies [6]. Lately, we have to rerun the polynomial regression for the validated data that has only a strong correlation. The program is too simple with Python, you can find here (Figure 4) the script of the operating Pearson correlation and how to export the results into a CSV file.
Script of the application of Pearson correlation in python.
The watershed of Mellegue is situated between 36° 25′ 50.43″, 35° 12′ 20.74″ north and 7° 11′ 30.98″, 8° 55′ 7.99″ east (Figure 5). The area covers 10,500 km2 where approximately 60% of the surface belongs to Algeria, whereas the remaining surface, as well as the outlet, is situated in Tunisia. The local climate is known as arid to semiarid with a slight change in sub-humid in the north. As a consequence, the area is covered with low vegetation where few forests are situated mainly in the northeast of the basin. The average yearly temperature is around 17°c with a slight change of 1–2°C. The winter is responsible for providing 50% of the annual rainfall. The so-called Oued Mellegue represents the main river of the catchment. It is 290 km long, cuts the watershed in a northeastern southwestern direction. Due to its large area, we assembled weather data from 23 stations scattered all around the catchment. Later on, the large amount of data was combined to compute a yearly average value for each weather variable. By homogenizing all the weather data together, it will be very helpful in understanding and visualizing the response of the watershed due to climate change and relieving voluminous data processing by the model. Hence, dealing with each station separately will lead to a lack in predicting the climate behavior of the area [7].
Geographic location of the watershed of Mellegue.
The city of Zaghouan is a Tunisian metropolitan located under the coordinates 36° 24′ north/10° 09′ east (Figure 6). The city is situated in the northeast of Tunisia, specifically on the hill of the Jbel Zaghouan (1295 m altitude). The region is known for the abundance of high reliefs and a large number of water sources because of the active seismicity of the zone, mainly manifested in the fault of Zaghouan. This mechanic structure extends for approximately 80 km along with the northeast-southwest Atlasic trend where total vertical displacement exceeds 5 km. The climate of the study area does belong to the semiarid where annual temperature goes on the average of 18°C, whereas total rainfalls are approximately 500 mm. The estimated population of the city is around 20,837. Local activities rely in a big part on agricultural activities; around 300,000 ha are dedicated to agriculture where about 1.4 million quintals are gained yearly [8, 9].
Geographic location of the city of Zaghouan.
The results of polynomial regression (Figure 7 and Table 1) are showing a general decrease in ET0 compared with the initial states, which recorded 1250 mm/year in 2019. The overall decrease is estimated at the average of 11 mm/year as it will reach, by the end of 2030, 1115 mm /year. The polynomial regression based on Pearson correlation has intensely decreasing the results, where forecasting ET0 is estimated to reach 1060 mm in 2030 with an average loss of 16.2 mm/year. Many causes have been mentioned earlier to link the decrease of reference evapotranspiration. But two of the most influenced factors are revealed to be the temperature and winds. In fact, many studies have predicted the increase in temperature, which may go up to 5°C (depending on the coordinates, local climate, etc.). Inversely, the rise of the surface temperature will lead to a general decrease in ET0. This phenomenon happened to be known as the “Evaporation Paradox” [10, 11]. On the other hand, the expansion of urbanized areas will also affect ET0 where it will undoubtedly generate more polluted air, which has a severe negative impact on the behavior of ET0 [12]. Additionally, the general decrease of water bodies could lead to an indirect decrease in the annual value of ET0 [13].
Quadratic polynomial regression forecast.
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.442 | 71982.188 | −67.603 | 0.016 | Valid |
2 | −135589.162 | 140.694 | −0.036 | Valid | |
3 | −898739.862 | 903.865 | −0.227 | Valid | |
4 | −1260271.181 | 1264.865 | −0.317 | Valid | |
5 | −1060164.556 | 1063.572 | −0.266 | Valid | |
6 | −182580.374 | 185.367 | −0.047 | Valid | |
7 | −355144.733 | 358.103 | −0.090 | Valid | |
8 | −333614.344 | 336.200 | −0.084 | Valid | |
9 | −306578.982 | 308.705 | −0.077 | Valid | |
10 | −856433.461 | 858.485 | −0.215 | Valid | |
11 | −1394177.144 | 1402.250 | −0.352 | Valid | |
12 | −1436810.518 | 1445.436 | −0.363 | Valid | |
13 | −1245118.628 | 1252.851 | −0.315 | Valid | |
14 | −878430.329 | 882.910 | −0.222 | Valid | |
15 | −520073.866 | 522.075 | −0.131 | Valid | |
16 | −2341253.349 | 2342.237 | −0.585 | Valid | |
17 | −2708961.470 | 2715.734 | −0.680 | Valid | |
18 | −856517.732 | 873.275 | −0.222 | Valid | |
19 | −36331.810 | 37.729 | −0.010 | Valid | |
20 | −1060164.556 | 1063.572 | −0.266 | Valid | |
21 | 2746547.110 | −2734.734 | 0.681 | Invalid | |
22 | 316197.814 | −327.556 | 0.085 | Invalid |
Coefficient of prediction of ET0 for Mellegue catchment.
Based on the following results (Figure 8 and Table 2), the forecast of rainfalls is generally stabilized with a slightly increasing tendency, where it predicts a value of precipitation that reaches 575 mm/year in 2030. Inversely, the application of Pearson polynomial regression has shown a decreasing in
Quadratic polynomial regression forecast of
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.45 | 1955249.056 | −1944.213 | 0.483 | Invalid |
2 | 613267.850 | −612.659 | 0.153 | Invalid | |
3 | 715018.701 | −716.287 | 0.179 | Invalid | |
4 | 2811409.072 | −2808.597 | 0.702 | Invalid | |
5 | −362350.042 | 383.213 | −0.101 | Valid | |
6 | 2694179.641 | −2670.272 | 0.662 | Valid | |
7 | 2680735.502 | −2657.038 | 0.658 | Valid | |
8 | 2108146.344 | −2084.830 | 0.515 | Valid | |
9 | 572407.085 | −558.133 | 0.136 | Valid | |
10 | 315977.725 | −312.109 | 0.077 | Valid | |
11 | 2445104.816 | −2433.165 | 0.605 | Invalid | |
12 | 3188164.611 | −3170.534 | 0.788 | Valid | |
13 | 2497308.704 | −2475.376 | 0.614 | Valid | |
14 | 1209763.715 | −1185.469 | 0.290 | Valid | |
15 | 1464559.714 | −1439.674 | 0.354 | Valid | |
16 | −60450.995 | 72.904 | −0.021 | Valid | |
17 | −1642843.790 | 1643.988 | −0.411 | Valid | |
18 | 6646561.474 | −6631.849 | 1.654 | Invalid | |
19 | 8793045.164 | −8760.092 | 2.182 | Invalid | |
20 | −1108225.764 | 1126.116 | −0.286 | Valid | |
21 | 44922.783 | −33.671 | 0.006 | Valid | |
22 | 4539127.186 | −4520.044 | 1.125 | Invalid |
Coefficient of prediction of P for Mellegue catchment.
According to the following result, polynomial forecasts have announced a slight decrease in maximum temperature (
Quadratic polynomial regression forecast of
Station | Poly ( | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.467 | −4980.173 | 5.042 | −0.001 | Invalid |
2 | −9726.882 | 9.776 | −0.002 | Invalid | |
3 | −14511.587 | 14.568 | −0.004 | Invalid | |
4 | −23027.041 | 23.057 | −0.006 | Invalid | |
5 | −2610.073 | 2.607 | −0.001 | Valid | |
6 | −1249.637 | 1.281 | 0.000 | Invalid | |
7 | 31420.577 | −31.295 | 0.008 | Valid | |
8 | 5659.772 | −5.656 | 0.001 | Valid | |
9 | 4127.832 | −4.116 | 0.001 | Valid | |
10 | −29400.171 | 29.353 | −0.007 | Invalid | |
11 | −20620.933 | 20.694 | −0.005 | Invalid | |
12 | −18063.359 | 18.138 | −0.005 | Invalid | |
13 | −12779.390 | 12.826 | −0.003 | Invalid | |
14 | 35242.347 | −35.089 | 0.009 | Valid | |
15 | −499.410 | 0.506 | 0.000 | Valid | |
16 | −15299.754 | 15.350 | −0.004 | Invalid | |
17 | −21567.206 | 21.631 | −0.005 | Invalid | |
18 | −17724.813 | 17.840 | −0.004 | Invalid | |
19 | −17130.249 | 17.181 | −0.004 | Invalid | |
20 | −2610.073 | 2.607 | −0.001 | Valid | |
21 | 63652.590 | −63.429 | 0.016 | Valid | |
22 | −20955.317 | 20.796 | −0.005 | Valid |
Coefficient of prediction of Tmax for Mellegue catchment.
Quadratic polynomial regression forecast of
Station | Poly (° = 2) | Pear Corr | |||
---|---|---|---|---|---|
Alpha | Beta 1 | Beta 2 | |||
1 | 0.648 | −15061.536 | 15.056 | −0.0038 | Valid |
2 | −15104.129 | 15.116 | −0.0038 | Valid | |
3 | −16539.754 | 16.569 | −0.0041 | Valid | |
4 | −19188.385 | 19.222 | −0.0048 | Valid | |
5 | −17771.726 | 17.806 | −0.0045 | Valid | |
6 | −1198.441 | 1.210 | −0.0003 | Invalid | |
7 | 1745.406 | −1.719 | 0.0004 | Invalid | |
8 | −15167.497 | 15.148 | −0.0038 | Valid | |
9 | −21793.646 | 21.777 | −0.0054 | Valid | |
10 | −11902.214 | 11.955 | −0.0030 | Valid | |
11 | −13972.995 | 13.983 | −0.0035 | Valid | |
12 | −11936.938 | 11.972 | −0.0030 | Valid | |
13 | −11066.352 | 11.111 | −0.0028 | Valid | |
14 | −22346.007 | 22.350 | −0.0056 | Valid | |
15 | −20194.330 | 20.203 | −0.0051 | Valid | |
16 | −31769.096 | 31.718 | −0.0079 | Valid | |
17 | −19493.627 | 19.517 | −0.0049 | Valid | |
18 | −17911.162 | 17.991 | −0.0045 | Valid | |
19 | −15827.268 | 15.904 | −0.0040 | Valid | |
20 | −17771.726 | 17.806 | −0.0045 | Valid | |
21 | 55076.342 | −54.831 | 0.0136 | Invalid | |
22 | 16150.051 | −15.964 | 0.0039 | Valid |
Coefficient of prediction of
According to the following results of Figure 11, it is found out that quartic polynomial regression (order (
Quartic polynomial regression forecast of P for the case of Zaghouan.
Considering the application of polynomial regression for the case of Mellegue watershed, it seems that the best polynomial equation is quadratic (
It seems that polynomial regression cannot deal with big data. The catchment is a very large basin. So, trying to understand/study a specific phenomenon needs to collect a large amount of data that has to be taken from different stations, in such a way to cover the entire area. Homogenizing the data for a resultant average response is very helpful for the machine learning algorithm to reduce error uncertainties from voluminous data, and so polynomial regression can fit the generated weather curvature [20]. Unfortunately, these data are not efficient at all for decision-makers and environmentalists because it can generate a misinterpretation as a response for treating/dealing with the potential causes for such a phenomenon.
As we see in the previous example, choosing the right order that fits mostly with the data type is often challenging to find. This is considered as one of the major handicaps in working with polynomial regression [19]. To deal with such drawbacks, choosing the right order must not be arbitrary and needs some visualization and expert of dealing with similar data (Plot,
Another important drawback that should be reported is the incapacity of the polynomial regression to deal with very sensitive data such as weather. Even high orders cannot keep up with original data curves. Therefore, many samplers (points) will not be taken into account by the polynomial model, which will affect the projection of forecasted weather results. Additionally, as seen in most of the previous examples, we can say that the prediction of the weather shows a linear trend that does not conform to the general feature of the weather as it behaves seasonally, and so it must have a certain sinusoidal tendency. In the two previous study areas, most of the polynomial orders are quadratic equations (
Referring to the case of Zaghouan, statistics of polynomial regression have revealed a pessimistic result. Regardless of the order (
Difference in the behavior of polynomial regression for monthly and yearly data (case of Zaghouan).
There are many advantages from using polynomial regression. Besides their simplicity in processing, they operate well in giving the best relationship between independent and dependent variables. Another good aspect regarding polynomial functions is their aptitude of fitting numerous curvatures, which depend mainly on the type and trend of the data. To conclude with, a polynomial function may not be so accurate, but it can generate an acceptable weather forecast. Let us say that it could be helpful to forecast the general behavior of climate at a certain specific range of time without the need of being too precise.
The Pearson correlation is revealed to be effective in evaluating the statistical relationship or the dependencies between two variables. Even though the method is based on the covariance and mathematical equation, it still not scientifically reliable to determine the strength and direction of the association based on Pearson correlation only. We see in the previous example of Mellegue catchment how the r coefficient has showed the strength of correlation between the stations. Every climate parameter has responded differently from the other. We can see, from Tables 1
Polynomial regression algorithm was outperformed by Python for weather forecasting. We want to see the performance of the model by taking into account the type of input data where it was operated on two concrete case studies. Eventually, we want to test the relationship between stations based on Pearson correlation as a way to retrieve the data and homogenizing it. The results have opened a wide debate to discuss; convenient polynomial order was revealed to be quadratic, which agreed with the general idea that most applied polynomial order is first and second degree [21]. Additionally, the model found a good capacity to fit various complex data [22]. On the other hand, the performance of polynomial regression based on Pearson correlation has altered significantly the weather prognosis accuracy. The behavior of the model has shown a drastic change by going from monthly to yearly data. Depending on the plot observation and coefficients variables, polynomial regression happened to fit more with yearly data. This is due to the simple fact that polynomial regression will operate more efficiently with moderate to low input data. The second fact is that this type of regression is too sensitive to mutable data such as seasonal climate. That circumstance was discovered in the case of Zaghouan (monthly data), where the model has not succeeded in finding the best polynomial order (
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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. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. 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. 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.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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