Other similarities and differences between gravity and magnetic methods
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{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"}]},book:{item:{type:"book",id:"7250",leadTitle:null,fullTitle:"Cancer Survivorship",title:"Cancer Survivorship",subtitle:null,reviewType:"peer-reviewed",abstract:"This book is a marvelous compendium of eight articles that cover a wide range of topics, including breast cancer: management and survivorship, rectal cancer intersphincteric resection, head and neck cancer diagnosis and radiotherapy, synthetic peptides as antitumor agents, and recent advances in thyroid cancer. It has been a wonderful opportunity to co-edit this special edition. We are greatly appreciative of the work of all the contributors to the book, who brought with them tremendous diversity of perspectives and fields truly reflective of the complexity of the topic and who, through coming together in this project, serve as a nidus of the multidisciplinary collaboration in this field. Finally, we must acknowledge the thousands of cancer patients who have participated in the studies that have provided the information that has advanced the field so greatly in recent years.",isbn:"978-1-78984-907-3",printIsbn:"978-1-78984-906-6",pdfIsbn:"978-1-83881-789-3",doi:"10.5772/intechopen.74342",price:119,priceEur:129,priceUsd:155,slug:"cancer-survivorship",numberOfPages:140,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"cf8054394c93ff635e50eb4ac8cc8d3a",bookSignature:"Dil Afroze",publishedDate:"January 3rd 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7250.jpg",numberOfDownloads:8619,numberOfWosCitations:1,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:5,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:11,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 30th 2018",dateEndSecondStepPublish:"February 20th 2018",dateEndThirdStepPublish:"April 21st 2018",dateEndFourthStepPublish:"July 10th 2018",dateEndFifthStepPublish:"September 8th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"244441",title:"Prof.",name:"Dil",middleName:null,surname:"Afroze",slug:"dil-afroze",fullName:"Dil Afroze",profilePictureURL:"https://mts.intechopen.com/storage/users/244441/images/system/244441.jpeg",biography:"Dil Afroze (Ph.D) is a Professor and Chair at the Advanced Centre for Human Genetics, Sher-I-Kashmir Institute of Medical Sciences (SKIMS), India. After obtaining her doctorate from Jamia Millia Islamia, India, she joined the Department of Immunology and Molecular Medicine at SKIMS as faculty in 2005. With over two decades teaching experience in immunology and molecular medicine, Professor Dil Afroze is the Vice President of the Indian Immunology Society. She has been a registered guide for basic and clinical graduate and post graduate programs and has mentored postdoctoral fellows as well. She has a number of awards and honors to her credit at the national and international level. She has publications in high impact journals such as Frontiers, the International Journal of Infectious Disease, and the Journal of Chemical Biochemistry and is an editorial board member of several international books (IntechOpen publications). She has been a Visiting Professor in the Division of Oncology, University of Linköping, Sweden and Department of Anatomical Sciences and Neurobiology, University of Louisville School of Medicine, USA.",institutionString:"Sher-i-Kashmir Institute of Medical Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Sher-i-Kashmir Institute of Medical Sciences",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"428",title:"Cancer Biology",slug:"biochemistry-genetics-and-molecular-biology-oncology-cancer-biology"}],chapters:[{id:"63556",title:"Overview of Important “Organs at Risk” (OAR) in Modern Radiotherapy for Head and Neck Cancer (HNC)",doi:"10.5772/intechopen.80606",slug:"overview-of-important-organs-at-risk-oar-in-modern-radiotherapy-for-head-and-neck-cancer-hnc-",totalDownloads:2078,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"With the advent of highly conformal and adaptive radiotherapy techniques, the significance of accurate delineation of organs at risk (OARs) is becoming more and more important. Techniques such as Intensity modulated radiotherapy (IMRT) and intensity/volumetric modulated arc therapy (VMAT) has allowed for improved dose conformation within the target. It has also allowed for steep dose gradients around the target for better normal tissue sparing. The accurate contouring and delineation of the OARs are thus warranted as variation in delineation has been systematically reported in studies. All these facts have led to the development of contouring guidelines for OARs in various sites. Head and neck cancers (HNC) are a perfect example where outcome and quality of life (QOL) balance remains a therapeutic challenge. There are several OARs and thus the accurate delineation following a standard guideline becomes more important. This chapter looks into the published guidelines for the delineation of such structures.",signatures:"Trinanjan Basu and Nithin Bhaskar",downloadPdfUrl:"/chapter/pdf-download/63556",previewPdfUrl:"/chapter/pdf-preview/63556",authors:[{id:"209078",title:"Dr.",name:"Trinanjan",surname:"Basu",slug:"trinanjan-basu",fullName:"Trinanjan Basu"},{id:"266716",title:"Dr.",name:"Nithin",surname:"Bhaskar",slug:"nithin-bhaskar",fullName:"Nithin Bhaskar"}],corrections:null},{id:"64641",title:"Breast Cancer: Management and Survivorship",doi:"10.5772/intechopen.82297",slug:"breast-cancer-management-and-survivorship",totalDownloads:1137,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Breast cancer is one of the most common in female population worldwide and comprises about 22.9% of all cancers. Despite the prognosis and survival rates of breast cancer patients and survivors are comparatively better than other cancers, but their net outcome can be revealed by other factors like tumor grade, secondary effects of chemotherapy like insomnia and health behaviors, this distressing may decrease patient’s life expectancy. In the backdrop of this, the need of the hour for the breast cancer survivors is to assess multifactoral nonpharmacological interventions and the management that includes physical exercise, psychological and complementary medicine, which could be cost effective, widely accessible and more promising for breast cancer patients and survivors apart from pharmacological interventions.",signatures:"Bilal Rah, Shazia Ali, Mohd Ishaq Dar and Dil Afroze",downloadPdfUrl:"/chapter/pdf-download/64641",previewPdfUrl:"/chapter/pdf-preview/64641",authors:[{id:"244441",title:"Prof.",name:"Dil",surname:"Afroze",slug:"dil-afroze",fullName:"Dil Afroze"},{id:"243176",title:"Dr.",name:"Bilal",surname:"Rah",slug:"bilal-rah",fullName:"Bilal Rah"},{id:"249300",title:"Dr.",name:"Shazia",surname:"Ali",slug:"shazia-ali",fullName:"Shazia Ali"},{id:"256923",title:"Dr.",name:"Mohd",surname:"Ishaq Dar",slug:"mohd-ishaq-dar",fullName:"Mohd Ishaq Dar"}],corrections:null},{id:"60538",title:"Retracted: Multiscale Stochastic Modeling Connects Cancer Drug Resistance Mechanisms to Population Survival Rates",doi:"10.5772/intechopen.76185",slug:"multiscale-stochastic-modeling-connects-cancer-drug-resistance-mechanisms-to-population-survival-rat",totalDownloads:666,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.",signatures:"Xiaoqiang Sun",downloadPdfUrl:"/chapter/pdf-download/60538",previewPdfUrl:"/chapter/pdf-preview/60538",authors:[{id:"243333",title:"Dr.",name:"Xiaoqiang",surname:"Sun",slug:"xiaoqiang-sun",fullName:"Xiaoqiang Sun"}],corrections:null},{id:"62874",title:"Understanding the Anti-Tumor Properties Mediated by the Synthetic Peptide GK-1",doi:"10.5772/intechopen.79833",slug:"understanding-the-anti-tumor-properties-mediated-by-the-synthetic-peptide-gk-1",totalDownloads:983,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Cancer exhibits adaptive features typical of complex systems, like resilience and robustness to environmental challenges through the emergent co-evolution of its components. These events promote carcinogenesis through dynamic interactions among numerous components and subsystems, including the immune system. During the past decade, our research group has provided substantial evidence that the peptide GK-1 has important immunomodulatory properties. In elderly mice, GK-1 acts as a potent adjuvant of the influenza vaccine through a mechanism that involves the activation of antigen-presenting cells (APCs) and an increased production of pro-inflammatory cytokines and chemokines (IFN-γ, TNFα, CCL2). To date, there is solid evidence supporting the antitumoral properties of GK-1 in murine cancer models. First, a lower occurrence and smaller size of spontaneous bronchiolar adenomas were found in elderly GK-1-treated mice compared to paired untreated mice. In two independent studies, GK-1 treatment reduced tumor growth and increased mouse survival in a murine model of melanoma and breast tumor. In the former model, a synergy between GK-1 and anti-PD-L1 treatment was observed, while in the latter, GK-1 alone controlled the metastatic burden. The effective activation of APCs induced by GK-1, restoring the antitumor-specific immunity, may underlie some of its antineoplastic effects.",signatures:"Jacquelynne Cervantes-Torres, Laura Montero, Noé Rodríguez-Rodríguez, Edda Sciutto, Gladis Fragoso and Diana Torres-García",downloadPdfUrl:"/chapter/pdf-download/62874",previewPdfUrl:"/chapter/pdf-preview/62874",authors:[{id:"245785",title:"Ph.D.",name:"Edda",surname:"Sciutto",slug:"edda-sciutto",fullName:"Edda Sciutto"},{id:"260979",title:"MSc.",name:"Jacquelynne",surname:"Cervantes",slug:"jacquelynne-cervantes",fullName:"Jacquelynne Cervantes"},{id:"260980",title:"MSc.",name:"Laura",surname:"Montero-León",slug:"laura-montero-leon",fullName:"Laura Montero-León"},{id:"260981",title:"Dr.",name:"Gladis",surname:"Fragoso",slug:"gladis-fragoso",fullName:"Gladis Fragoso"},{id:"260983",title:"Dr.",name:"Diana",surname:"Torres-García",slug:"diana-torres-garcia",fullName:"Diana Torres-García"},{id:"265340",title:"Dr.",name:"Noé",surname:"Rodriguez-Rodriguez",slug:"noe-rodriguez-rodriguez",fullName:"Noé Rodriguez-Rodriguez"}],corrections:null},{id:"62963",title:"Sentinel Node for Accurate Diagnosis of the Head and Neck Carcinoma",doi:"10.5772/intechopen.79775",slug:"sentinel-node-for-accurate-diagnosis-of-the-head-and-neck-carcinoma",totalDownloads:788,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"When it comes to tumors of the head and neck, there is currently no reliable method for finding all the metastases of the neck. Therefore, we follow the rule of performing an elective dissection in the patients where more than 20% of metastases are expected. Even then, there are some patients with local recurrences. The explanation most likely lies in the incorrect histopathological diagnosis and unrecognized metastases. The ability to recognize smaller metastases can be accomplished by the use of the concept of the sentinel lymph node. This chapter describes the assessment of the neck status in 40 patients. In 18 patients, we have found metastases in the sentinel lymph nodes. It is important to note that in eight patients, metastases were found only after the use of serial cuts and immunohistological staining.",signatures:"Bogdan Cizmarevic",downloadPdfUrl:"/chapter/pdf-download/62963",previewPdfUrl:"/chapter/pdf-preview/62963",authors:[{id:"247771",title:"Ph.D.",name:"Bogdan",surname:"Cizmarevic",slug:"bogdan-cizmarevic",fullName:"Bogdan Cizmarevic"}],corrections:null},{id:"62706",title:"Thyroidectomy without Ligatures in Differentiated Thyroid Cancer",doi:"10.5772/intechopen.79730",slug:"thyroidectomy-without-ligatures-in-differentiated-thyroid-cancer",totalDownloads:962,totalCrossrefCites:1,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Technical improvements in thyroid surgery are nearly close with the progress of the vessels sealing systems. In all cases, we need to obtain a radical and safe thyroid excision. This chapter is conducted to evaluate the technical key point and the postoperative benefits of our procedure using vessels sealing devices in differentiated thyroid cancers. A prospective study, carried out in First Surgical Clinic, Emergency County Clinical Hospital Tirgu Mureș, Romania from January 01, 2013 to March 01, 2018, based on 100 consecutive patients, divided into two groups: first group without ligatures, using Small Jaw LigaSure™, and the second group operated by conventional procedure. Statistical analysis of some parameters (the thyroid pathology, operative time, hospitalization days, analgesic drugs, immediate postoperative complications and histopathological findings) shows that this procedure provides a total and “complete” removal of the thyroid specimen, with a decreased operative time and fewer hospitalization days.",signatures:"Molnar Călin, Butiurca Vlad Olimpiu, Molnar Varlam Claudiu and Botoncea Marian",downloadPdfUrl:"/chapter/pdf-download/62706",previewPdfUrl:"/chapter/pdf-preview/62706",authors:[{id:"247775",title:"Prof.",name:"Calin",surname:"Molnar",slug:"calin-molnar",fullName:"Calin Molnar"},{id:"257695",title:"Dr.",name:"Marian",surname:"Botoncea",slug:"marian-botoncea",fullName:"Marian Botoncea"},{id:"257696",title:"Dr.",name:"Vlad Olimpiu",surname:"Butiurca",slug:"vlad-olimpiu-butiurca",fullName:"Vlad Olimpiu Butiurca"},{id:"257699",title:"Dr.",name:"Claudiu Varlam",surname:"Molnar",slug:"claudiu-varlam-molnar",fullName:"Claudiu Varlam Molnar"}],corrections:null},{id:"62730",title:"Quality of Life Following Intersphincteric Resections for Low Rectal Cancer: Early Results",doi:"10.5772/intechopen.79727",slug:"quality-of-life-following-intersphincteric-resections-for-low-rectal-cancer-early-results",totalDownloads:1018,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Intersphincteric resections are part of the therapeutic arsenal that preserves the sphincterian apparatus. This chapter analyzes the evolution of rectal surgery leading up to intersphincteric resections, deals with anatomical and oncological aspects in rectal cancer, and finally shows our own personal experience with ISR in a series of 40 cases focusing on oncological outcomes, continence, and defecation. As a conclusion, intersphincteric resection represents a feasible therapeutic option in highly selected cases that exempts the patient from the need of a permanent colostomy bag without compromising oncological principles. The Wexner score system is simple and effective in objectifying continence in patients that undergo this type of surgery.",signatures:"Călin Molnar, Marian Botoncea, Claudiu Varlam Molnar and Vlad-Olimpiu Butiurca",downloadPdfUrl:"/chapter/pdf-download/62730",previewPdfUrl:"/chapter/pdf-preview/62730",authors:[{id:"247775",title:"Prof.",name:"Calin",surname:"Molnar",slug:"calin-molnar",fullName:"Calin Molnar"},{id:"257695",title:"Dr.",name:"Marian",surname:"Botoncea",slug:"marian-botoncea",fullName:"Marian Botoncea"},{id:"257696",title:"Dr.",name:"Vlad Olimpiu",surname:"Butiurca",slug:"vlad-olimpiu-butiurca",fullName:"Vlad Olimpiu Butiurca"},{id:"257699",title:"Dr.",name:"Claudiu Varlam",surname:"Molnar",slug:"claudiu-varlam-molnar",fullName:"Claudiu Varlam Molnar"}],corrections:null},{id:"63915",title:"Actinic Papillary Fibroelastoma of the Left Ventricle",doi:"10.5772/intechopen.81024",slug:"actinic-papillary-fibroelastoma-of-the-left-ventricle",totalDownloads:989,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"We present the case of a 69-year-old woman with a history of endometrial carcinoma in 1996, who underwent a total hysterectomy and bilateral adnexectomy. The patient also received chemotherapy (doxorubicin and cisplatinum) and local radiotherapy (50 Gy) because of a single lung metastasis, with total remission during later follow-up. During follow-up, 10 years later following radiotherapy, a transthoracic echocardiogram (TTE) revealed an image consistent with a primary cardiac tumor (papillary fibroelastoma) or metastatic cardiac tumor on the posteromedial papillary muscle. Cardiac magnetic resonance imaging (MRI) revealed a solid mass on the posteromedial papillary muscle with late enhancement, consistent with a primary cardiac tumor. During surgery, the tumor located in the posteromedial papillary muscle was resected. A pathological examination revealed the presence of a tumor mass with a core of dense connective tissue surrounded by a layer of hyperplastic endocardial cells characteristic of a papillary fibroelastoma. After 8 years of follow-up, the patient remains asymptomatic.",signatures:"Tomás Francisco Cianciulli and María Cristina Saccheri",downloadPdfUrl:"/chapter/pdf-download/63915",previewPdfUrl:"/chapter/pdf-preview/63915",authors:[{id:"256376",title:"Prof.",name:"Tomas",surname:"Cianciulli",slug:"tomas-cianciulli",fullName:"Tomas Cianciulli"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"8207",title:"Breast Cancer Biology",subtitle:null,isOpenForSubmission:!1,hash:"8f11a095792565a9ffb62eb68cc1aff7",slug:"breast-cancer-biology",bookSignature:"Dil Afroze, Bilal Rah, Shazia Ali, Faheem Shehjar, Mohd Ishaq Dar, Shailender S. 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As understood today, right at the heart of the Earth is a solid inner core composed primarily of iron. At 5, 700°C, this iron is as hot as the Sun’s surface, but the crushing pressure caused by gravity prevents it from becoming liquid. Surrounding this is the outer core, a nearly 2, 000 km thick layer of iron, nickel, and small quantities of other metals. Lower pressure than the inner core means the metal here is fluid. Differences in temperature, pressure and composition within the outer core cause convection currents in the molten metal as cool, dense matter sinks while warm, less dense matter rises. This flow of liquid iron generates electric currents, which in turn produce magnetic fields (Earth’s field). These convection processes in the liquid part of core (outer core) give rise to a dipolar geomagnetic field that resembles that of a large bar magnet aligned approximately along the Earth’s rotational axis. The mantle plays little part in the Earth’s magnetism, while interaction of the past and present geomagnetic field with the rocks of the crust produces magnetic anomalies recorded in detailed when surveys are carried out on or above the Earth’s surface.
The magnitude of the Earth’s magnetic field averages to about 5x10-5 T (50, 000 nT). Magnetic anomalies as small as 0.1 nT can be measured in continental magnetic surveys and may be of geological significance.
The magnetic methods, perhaps the oldest of geophysical exploration techniques bloomed after the World War II. Today, with improvements in instrumentation, navigation and platform compensation, it is possible to map the entire crustal section at a variety of scales from strongly magnetic basement at a very large scale to weakly magnetic sedimentary contacts at small scale. Methods of magnetic data treatment, filtering, display and interpretation have also advanced especially with the advent of high performance computers and colour raster graphics.
Internal structure of the Earth (from http://zebu.uoregon.edu)
As is well known today, magnetic methods are used to solve various problems such as:
Mapping the basement surface and sediments in oil/gas exploration
Detecting different types of ore bodies in mining prospecting
Detecting metal objects in engineering geophysics
Mapping basement faults and fractures
Determining zones with different mineralization in logging as well as inspecting casing parameters
Studying the magnetic field of the Earth and its generators and
A variety of other purposes such as natural hazards assessment, mapping impact structures and environmental studies.
Magnetic observations are obtained relatively easily and cheaply and a few corrections are applied to them. This explains why the magnetic methods are one of the most commonly used geophysical tools. Despite these obvious advantages, interpretations of magnetic observations suffer from a lack of uniqueness due to dipolar nature of the field and other various polarization effects. Geologic constraints, however, can considerably reduce the level of ambiguity. Information from magnetic surveys comes from rock units at depth as well as from those at or near the surface. This is the strength of the magnetic method (or any surface geophysical method), making it more powerful than any other remote sensing method which relies on the information from reflections of electromagnetic (EM) waves by materials on the Earth surface. Thus while the natural magnetic field of the Earth is measured in magnetic method, EM radiation normally is used as the information carrier in remote sensing. Electromagnetic radiation is a form of energy with the properties of a wave, and its major source is the sun. Solar energy traveling in the form of waves at the speed of light is known as the electromagnetic spectrum. Passive remote sensing systems record the reflected energy of electromagnetic radiation or the emitted energy from the Earth, such as cameras and thermal infrared detectors. Active remote sensing systems send out their own energy and record the reflected portion of that energy from the Earth’s surface, such as radar imaging systems.
In this chapter, we explore the magnetic methods of geophysical exploration. The first part of the chapter covers the fundamental concepts of magnetic force field, the Earth’s magnetic field and its relationship with gravity field. The second part deals with the measurement procedures and treatment of the magnetic field data, while the third part covers the magnetic effects of simple geometric bodies, processing and interpretation of magnetic data and ending it with treatment, analysis and interpretation of real field data.
Any magnetic grain is a dipole. That is, it has two poles, P1 and P2 of opposite signs diametrically linked. Charles Augustin de Coulomb in 1785 showed that the force of attraction or repulsion between electrically charged bodies and between magnetic poles obeys an inverse square law similar to that derived for gravity by Newton.
The mathematical expression for the magnetic force, Fm experienced between two magnetic monopoles is given by:
Where μ is a constant of proportionality known as the magnetic permeability, P1 and P2 are the ‘strengths’ of the magnetic monopoles and r is the distance between the poles.
We note that the expression in equation (1) is identical to the gravitational force,
We may seem to easily compare the gravitational force between masses m1 and m2 separated by r to that of either the attractive or repulsive magnetic force between two monopoles. However, the magnetic monopoles have never existed! Rather the fundamental magnetic element appears to consist of two magnetic monopoles: one positive and the other negative, separated by a distance. Thus the fundamental magnetic element consisting of two monopoles is called a magnetic dipole. Every magnetic grain is therefore a dipole.
We can therefore determine the force produced by a dipole by considering a force produced by two monopoles. Since the dipole is simply two monopoles, each of strength P1 and P2, we expect that the force generated by a dipole is simply the force generated by one monopole added vectorially to the force generated by the second monopole. Consequently, the force distribution for a dipole is nothing more than the magnetic force distribution observed around a simple bar magnet. Thus a bar magnet can be thought as two magnetic monopoles separated by a length of the magnet. The magnetic force appears to originate out of the North Pole (N) of the magnet and to terminate at the South Pole (S) of the magnet. Some of the field lines pass through the material of the magnet (high concentration because of high μ), some pass through air (low concentration because of low μ). Notice that even in air; the poles have high density of field lines. Also, the lines radiate out from N (vertically outward) and radiate into S (vertically inward). Between the length of the bar in air, the magnetic field directions are variable, but with the middle of the bar having a near horizontal field direction. Again, the field strength and direction at any point around the bar magnet is a vector sum of the force field contributed by each of the monopole (N or S).
When we examine equation (1) in terms of unit of measurement, we see that the magnetic force, Fm retains its fundamental unit of newton (N) and r2 would be in square metre (m2). Permeability, μ by the S. I. unit definition, is a unitless constant. The units of the pole strength, P are defined such that if a force of 1 N is produced by two unit poles separated by a distance of 1 m, then each unit pole has a strength of one ampere-metre (1 Am). Thus a unit pole has an S.I unit of ampere-metre.
We can also define, from equation (1), the force per unit pole strength exerted by a magnetic monopole, P1 or P2. This is called magnetic field strength or magnetizing force, H. Thus
Here again, given the units associated with force (N) and magnetic monopoles (Am), the unit associated with magnetic field strength, H are N/A-m and by definition, 1 N/A-m is referred to as a tesla (T): named after a Croatian inventor, Nikola Tesla. Thus 1 T = 1 N/Am. Indeed from equation (2), the unit of H can be expressed as Am/m2 or Am-1 (ampere per metre). Thus 1 N/Am = 1 Am-1 = 1 T. Similarly, the unit of magnetic flux is weber (Wb) and magnetic flux per unit area is the magnetic strength we have been talking about. Thus the unit of magnetic strength can also be expressed in weber per square metre (Wb/m2). Hence 1 Wb/m2 = 1 T.
When describing the magnetic field of the Earth, it is common to use units of nanotesla (nT), where 1 nT = 10-9 T. The average strength of the Earth’s magnetic field, H is about 50, 000 nT (ranges from 20, 000 to 70, 000 nT). A nanotesla has the value as the old unit of gamma (1 nT = 1 gamma).
When magnetic materials or rocks are placed within a field, T (a magnetizing force such as H given in equation (2)), the magnetic materials or rocks will produce their own magnetizations or polarizations. This phenomenon is called induced magnetization, magnetic polarization or magnetic induction. The strength of the magnetic field induced on the magnetic material due to the inducing field, T is called the intensity of magnetization or magnetic polarization, Ji; where
The constant of proportionality, k is the magnetic susceptibility and is a unitless constant determined by the physical properties of the magnetic material. The susceptibility, k can either be positive or negative in values. Positive values imply that the field, Ji is in the same direction as the inducing field T. Negative k implies that the induced magnetic field is in the opposite direction as the inducing field. Details of the mechanisms of induced magnetization can be further obtained from [1].
In magnetic exploration method, the susceptibility is the fundamental material property whose spatial distribution, we attempt to determine. We see that magnetic susceptibility is analogous to density in gravity surveying. Unlike density, there is a large range of susceptibilities even within materials and rocks of the same type. This definitely will put limit to knowledge of rock type through susceptibility mapping of an area.
Magnetic susceptibility in SI unit is a dimensionless ratio having a magnitude much less than 1 for most rocks. Hence a typical susceptibility value may be expressed (as for example) k = 0.0064 SI. In the old c.g.s. system of electromagnetic units (emu), the numerical value of magnetic susceptibility for a given specimen is smaller by a factor of 4π than the SI value. Thus k (SI) = k (emu) x 4π. Hence for k = 0.0064 SI, k (emu) = k (SI)/4π = 0.00051 emu.
Nearly 90% of the Earth’s magnetic field (geomagnetic field) looks like a magnetic field that would be generated from a dipolar magnetic source located at the centre of the Earth and nearly aligned with the Earth’s rotational axis. This field is believed to originate from convection of liquid iron in the Earth’s outer core [2] and is monitored and studied using global network of magnetic observatories and various satellite magnetic surveys. If this dipolar description of the Earth’s field were complete, then the magnetic equator would nearly correspond to the Earth’s geographic equator and the magnetic poles would also nearly correspond to the geographic poles. The strength of the Earth’s field at the poles is about 60, 000 nT. This is called the Main Field of the Earth. This field changes slowly with time and is believed to go through a decay and collapse, followed by polar reversal on a time scale of the order of 100, 000 years [3], [4]. The construction of a global magnetic reversal timescale is of fundamental importance in deciphering Earth’s history. For details on such discussion, [5] can be consulted.
The remaining 10% of the Earth’s magnetic field cannot be explained in terms of simple dipolar sources. The larger component of this 10% of the Earth’s field originates in iron-bearing rocks near the Earth’s surface where temperatures are sufficiently low (i.e. less than the Curie temperature of the rocks). This region is confined to the upper 30 – 40 km of the crust and is the source of the crustal field which is made up of induced field on magnetically susceptible rocks and remanent magnetism of the rocks. The smaller portion of the 10% comes from the upper atmosphere (external source).
The external source field is believed to be produced by interactions of the Earth’s ionosphere with the solar wind. Hence some temporal variations (usually variable over hours at tens of nT or occasionally variable over a few hours at hundreds of nT: the magnetic storm) are correlated to solar activity. The external component (except for magnetic storm phenomenon) is usually regular and are corrected/removed appropriately from field measurements in a process similar to drift correction in gravity surveys. Where magnetic storm is detected, survey is most often discontinued until after the phenomenon has passed.
The crustal field, its relation to the distribution of magnetic minerals within the crust, and the information this relation provides about exploration targets are the primary subjects of the magnetic method in exploration. In a magnetic survey, the magnetic induction, B whose magnitude is measured at a point is the vector sum of four field components:
The Earth’s
An
A field caused by
Other (usually) less significant fields caused by solar, atmospheric [8] and cultural influences
While we can handle the external features (source 4) component (like drift correction in gravity survey: for the solar/atmospheric sources) and divesting from such features or recognizing their transient effects and removing them (for cultural features), the main field is examined from complex models that have been developed and are available. Our intent here is to characterize the global magnetic field (main field) in order to isolate the magnetic field caused by crustal sources (sources 2 and 3).
Spherical harmonic analysis provides the means with which to determine from measurements of a potential field and its gradient on a sphere whether the sources of the field lie within the sphere or outside the sphere. Carl Friederich Gauss in 1838 was the first to describe the geomagnetic field in this way and concluded that the observed field at the Earth’s surface originates entirely from within the Earth. However, we know today from satellite observations, space probes and vast accumulation of information from field measurements that a small part of the geomagnetic field originates from outside the Earth.
We consider a magnetic induction vector,
Following [9], if no sources exist outside the sphere, then both V and
On the other hand, if all sources lie outside the sphere, then V and
Where in both equations (5) and (6), the superscripts, i and e denote internal and external sources respectively, θ is the co-latitude (latitude = 90o - θ), ϕ is longitude, r is the radial distance from the centre of the sphere,
For example, the normalized surface harmonics for n = 0, m = 0 is 1, for n = 1, m = 0 is cosθ, for n = 1, m = 1 is
Different types of surface harmonics can be deduced from the nature and forms of the normalized term:
If sources exist both inside and outside the sphere, then the potential, V in source-free regions near the surface of the sphere is given by the sum of the equations (5) and (6). Thus it is further convenient to express the combination of equations (5) and (6) in terms of Gauss’ coefficients
It is generally known that n = 1 harmonic from equation (8) gives the first three coefficients (
Excluding the n = 1 harmonic from equation (8) eliminates the dipole term from the geomagnetic field and leaving a remainder of the form called the non-dipole part.
At the point of observation, P, T is the magnitude of the total field intensity, and X, Y, Z and H are the north, east, vertical and horizontal components respectively. The quantity, I is the angle T makes with the horizontal (along which H is directed) and is called the dip or inclination, while D, the declination is the angle the horizontal field, H makes with the true or geographic north. Note that H is not the same here as the one expressed in equation (2).
We note that a simple dipole theory predicts that the magnetic inclination, I is related to the geographic latitude, φ as tan I = 2tan φ.
The vector elements of the Earth’s magnetic field at a point are
The IGRF is essentially a set of Gaussian coefficients,
Practically the IGRF consists of Gauss’ coefficients through degree and order 10 or slightly above as these terms are believed to represent the larger part of the field of the Earth’s core. Subtracting these low-order terms from the measured magnetic fields provides in principle the magnetic field of the crust.
The gravity and magnetic survey methods exploit the fact that variations in the physical properties of rocks in-situ give rise to variations in some physical quantity which may be measured remotely (on are above the ground). In the case of gravity method, the physical rock property is density and so density variations at all depths within the Earth contribute to the broad spectrum of gravity anomalies. For the magnetic method, the rock property is magnetic susceptibility and/or remanent magnetization; both of which can only exist at temperatures cooler than the Curie point and thus restricting the sources of magnetic anomalies to the uppermost 30 – 40 km of the Earth’s interior. In practice, almost all magnetic properties of rocks in bulk reflect the properties and concentrations of oxides of iron and titanium (Fe and Ti): the Fe-Ti-O system, plus one sulphide mineral, pyrrhotite [1]. We also note that the highest density used typically in gravity surveys are about 3.0 g cm-3, and the lowest densities are about 1.0 g cm-3. Thus densities of rocks and soils vary very little from place to place. On the other hand, magnetic susceptibility can vary as much as four to five orders of magnitude from place to place, even within a given rock type.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Passive and is a potential field bearing all the consequences | \n\t\t\tPassive and is a potential field bearing all the consequences | \n\t\t
Mathematical expression for the force field is that of the inverse square law relation | \n\t\t\tMathematical expression for the force field is that of the inverse square law relation | \n\t\t
Force between monopoles can either be attractive or repulsive | \n\t\t\tForce between masses is always attractive | \n\t\t
A monopole cannot be isolated. Monopoles always exist in pairs (dipole) | \n\t\t\tA single point mass can be isolated | \n\t\t
A properly reduced field has variation due to variation in induced magnetization of susceptible rocks and remanent magnetization | \n\t\t\tA properly reduced field has variation due to density variation in rocks | \n\t\t
Field changes significantly over time (secular variation). | \n\t\t\tField does not change significantly over time. | \n\t\t
Other similarities and differences between gravity and magnetic methods
Geologic interpretation of magnetic data requires the knowledge of the magnetic properties of rocks in terms of magnetic susceptibility and remanent magnetization. Factors that influence rock magnetic properties for various rock types have been summarized appropriately [10], [1], [11], [12]. The rocks of the Earth’s crust are in general only weakly magnetic but can exhibit both induced and remanent magnetizations. Magnetic properties of rocks can only exist at temperatures below the Curie point. The Curie temperature is found to vary within rocks but is often in the range 550oC to 600oC [13]. Modern research indicates that this temperature is probably reached by the normal geothermal gradient at depths between 30 and 40 km in the Earth and this so-called ‘Curie point isotherm’ may occur much closer to the Earth’s surface in areas of high heat flow.
Indeed rock magnetism is a subject of considerable complexity. Clearly, all crustal rocks find themselves situated within the geomagnetic field described in section 3. These crustal rocks are therefore likely to display induced magnetization given by equation (3), where the magnitude of magnetization, Ji is proportional to the strength of the Earth’s field, T. The magnetic susceptibility, k is actually the magnetic volume susceptibility that is encountered in exploration rather than mass or molar susceptibilities.
Apart from the induced magnetization, many rocks also show a natural remanent magnetization (NRM) that would remain even if the present-day geomagnetic field ceases to exist. The simplest way in which NRM can be acquired is through the process of cooling of rocks in molten state. As the rocks cool past the Curie point (or blocking temperature) a remanent magnetization in the direction of the prevailing geomagnetic field will be acquired. The magnitude and direction of the remanent magnetization can remain unchanged regardless of any subsequent changes in the ambient field.
Measurements can be made of the Earth’s total magnetic field or of components of the field in various directions. The oldest magnetic prospecting instrument is the magnetic compass, which measures the field direction. Other instruments include magnetic balances and fluxgate magnetometers.
The most used instruments in modern magnetic surveys are the proton-precession or optical-pumping magnetometers and these are appreciably more accurate and all of these instruments give absolute values of field. The proton magnetometer measures a radio-frequency voltage induced in a coil by the reorientation (precession) of magnetically polarized protons in a container of ordinary water or paraffin. Its measurement sensitivity is about 1 nT. The optical-pumping magnetometer makes use of the principles of nuclear resonance and cesium or rubidium vapour. It can detect minute magnetic fluctuations by measuring the effects of light-induced (optically pumped) transitions between atomic energy levels that are dependent on the strength of the prevailing magnetic field. The sensitivity of the optical absorption magnetometer is about 0.01 nT and on this premise may be preferred to proton precession magnetometer in air-borne surveys.
Airborne magnetic surveys or aeromagnetic surveys are usually made with magnetometers carried by aircraft flying in parallel lines spaced 2 - 4 km apart at an elevation of about 500 m when exploring for petroleum deposits and in lines 0.5 - 1.0 km apart roughly 200 m or less above the ground when searching for mineral concentrations. Ship-borne magnetic surveys or marine magnetic surveys can also be completed over water by towing a magnetometer behind a ship.
Ground surveys are conducted to follow up magnetic anomaly discoveries made from the air. Such surveys may involve stations spaced from 50 m apart. Survey may be along profiles or gridded network or may be in random pattern. Magnetometers also are towed by research vessels or mounted on the researcher on foot. In some cases, two or more magnetometers displaced a few metres from each other are used in a gradiometer arrangement; differences between their readings indicate the magnetic field gradient. A ground monitor is usually used to measure the natural fluctuations of the Earth’s field over time so that corrections similar to drift correction in gravity can be made. Alternatively, like gravity observations where the temporal variation in field values were accounted for by reoccupying a base station and using the variation in this reading to account for instrument drift and temporal variations of the field, we could also use the same strategy in acquiring magnetic observations. The alternative is not the best as field variation in magnetic may be highly erratic and magnetometers which are electronic instruments do not drift. With these points in mind most investigators conduct magnetic surveys using two magnetometers. One is used to monitor temporal variations of the magnetic field continuously at a chosen “base station”, and the other is used to collect observations related to the survey proper.
Surveying is generally suspended during periods of large magnetic fluctuation (magnetic storms).
Magnetic effects result primarily from the magnetization induced in susceptible rocks by the Earth’s magnetic field. Most sedimentary rocks have very low susceptibility and thus are nearly transparent to magnetism. Accordingly, in petroleum exploration magnetics are used negatively: magnetic anomalies indicate the absence of explorable sedimentary rocks. Magnetics are used for mapping features in igneous and metamorphic rocks, possibly faults, dikes, or other features that are associated with mineral concentrations. Data are usually displayed in the form of a contour map of the magnetic field, but interpretation is often made on profiles.
The first stage in any ground magnetic survey is to check the magnetometers and the operators. Operators can be potent sources of magnetic noise. Errors can also occur when the sensor of the magnetometer is carried on a short pole or on a back rack. Compasses, pocket knives, metal keys, geological hammers, and cultural articles with metal blend (belt, shoes, bungles, etc) are all detectable at distances below about a metre and therefore the use of high-sensitivity magnetometers requires that operators divest themselves of all metallic objects. Attempts must be made to follow the operation manual provided along with a magnetometer!
Diurnal corrections are essential in most field work, unless only gradient data are to be used. If only a single magnetometer is available, diurnal corrections have to rely on repeated visits to a base, ideally at intervals of less than an hour. A more robust diurnal curve can be constructed if a second fixed magnetometer is used to obtain readings at 3 to 5 minute intervals. The second magnetometer need not be the same type as that being used in the field. Thus a proton magnetometer can provide adequate diurnal control for surveys conducted with cesium vapour magnetometer and vice versa. Note that base should be remote from magnetic interferences and must be describable for future use.
In aeromagnetic surveys, great pains must be taken to eliminate spurious magnetic signals that may be expected to arise from the aircraft itself. Airframes of modern aircraft are primarily constructed from aluminum alloys which are non-magnetic and so the potential magnetic sources are the aircraft engines. Thus magnetometer sensors must be mounted far away from these engines.
Aeromagnetic data usually obtained have gone through on-board processing such as magnetic compensation, checking/editing, diurnal removal, tie line and micro leveling. For example, the basis of magnetic compensation is the reduction of motion-induced noise on the selected magnetic elements. These can be from individual sensors or various gradient configurations. The motion noise comes from the complex three-dimensional magnetic signature of the airframe as it changes attitude with respect to the magnetic field vector. The noise comes from permanent, induced and eddy effects of the airframe plus additional heading effects of the individual sensors. Thus the magnetic interference in a geophysical aircraft environment comes from several sources which must be noted and compensated for.
On-board data checking and editing involves the removal of spurious noise and spikes from the data. Such noise can be caused by cultural influences such as power lines, metallic structures, radio transmissions, fences and various other factors. Diurnal removal corrects for temporal variation of the earth’s main field. This is achieved by subtracting the time-synchronized signal, recorded at a stationary base magnetometer, from survey data. Alternatively, points of intersection of tie lines with traverse/profile lines can form loop networks which can be used to correct for the diurnal variation similar to drift correction in gravity survey.
Tie leveling utilizes the additional data recorded on tie lines to further adjust the data by consideration of the observation that, after the above corrections are made, data recorded at intersections (crossover points) of traverse and tie lines should be equal. Several techniques exist for making these adjustments and [14] gives a detailed of the commonly used techniques. The most significant cause of these errors is usually inadequate diurnal removal. Micro-leveling, on the other hand, is used to remove any errors remaining after the above adjustments are applied. These are usually very subtle errors caused by variations in terrain clearance or elevated diurnal activity. Such errors manifest themselves in the data as anomalies elongate in the traverse line direction. Accordingly they can be successfully removed with directional spatial filtering techniques [15].
When all the above considerations to raw magnetic data have been recognized and attended to, the IGRF correction (main field effect) is now carried out to give the \'magnetic anomaly\' defined as the departure of the observed field from the global model.
The potential field, φ (x, y, z) in free space, i.e. without any sources satisfies the Laplace equation
The Fourier transform in one-dimension can be found in most text books of applied mathematics. The Fourier transform in two or three dimensions possess additional properties worth noting [16]. The two-dimensional Fourier transform is given by:
Where u and v are spatial frequency numbers in the x- and y-directions respectively (u = 2π/Lx and v = 2π/Ly), with Lx and Ly as length dimensions in the x- and y-directions respectively. It is important to note that φ (x, y) and Φ (u, v) are simply different ways of looking at the same phenomenon. The Fourier transform maps a function from one domain (space or time) into another domain (wave number or frequency). For details, [17] can be consulted.
Magnetic potential field is caused by the variation in magnetization in the Earth’s crust. This potential field is observed over a plane close to the surface of the Earth. If the magnetization variations are properly modeled consistent with other geological information, it is possible to fit the model to the observed potential field. Note that the magnetic field induction usually observed is the derivative of this potential. The model parameters (body shape factors, susceptibility values, burial depth, and magnetization direction) are then observable. These models may be (1) excess magnetization confined to a well-defined geometrical object, (2) geological entity such as basins (sedimentary or metamorphic with intrusive bodies). Sedimentary basins are of great interest on account of their hydrocarbon potential and since these rocks are generally non-magnetic, the observed magnetic field is probably entirely due to the basement on which sediments are resting and (3) with available resources and technology (as in airborne magnetic surveys) large areas can be covered in a survey and so permitting maps that cover several geological provinces or basins and therefore allow inter basin studies such as delineating of extensive shallow and deep features as faults, basin boundaries, etc. to be extracted.
We shall briefly outline a few examples of the rigors that an interpreter goes through to synthesize information from these potential fields.
We consider two monopoles of opposite sign: one at the origin of the 3-coordinate system and the other positioned below such that their common axis is along the z-axis, with -∆z as the separation between the monopoles (Fig. 2)
The potential at P, V (P) due to both monopoles is the sum of the potentials caused by each monopole. This is given generally for monopoles that are not aligned along any particular axis as [18]:
Where
According to Helmholtz theorem, the magnetic field,
Where m is the magnitude of the dipole moment while
Equation (10) can also be expressed in cylindrical coordinates as [8]:
Where θ is now the angle between
Equation (12) shows that the magnitude
Many magnetic bodies exist that are dipolar in nature to a first approximation. For example, the entire field of the Earth appears nearly dipolar from the perspective of other planets. It is also known that in aeromagnetic survey, the inhomogeneity of a massive pluton at the survey height appears to be a dipole source.
Two monopoles of opposite sign with the monopole of positive sign at the origin and other situated at z = ∆z
We can consider a small element of magnetic material of volume,
A 3-D magnetic body of volume dv and uniform magnetization
Where r again is the distance from P to the dipole. In general, magnetization,
The magnetic induction,
The subscripts in the gradient operator from P to Q are now
For a two-dimensional source, we may start with a body of finite length 2a and so the volume integral in equation (15) is replaced with surface integral over the cross sectional area, dS of the body and a line integral along its length (the z-axis) as:
Where S is the cross sectional area of the body. As
The magnetic field induction,
Note also that
We had noted in section one, and by implication, that the mutual force, F between a particle of mass m1 centred at point Q (x’, y’, z’) and a particle of mass, m2 at P (x, y, z) is given by
Where
Where
If a potential exists, then the gravitational attraction (also known as the gravitational acceleration),
Where here, U (P) can be expressed as
Where the gravitational potential is defined as the work done by the field on the test particle.
Equations (19) and (12) show that the magnetic scalar potential of an element of magnetic material and the gravitational attraction of mass are identical. Starting from equation (13), and considering a body with uniform magnetization,
The gravitational potential (equation (22)) is written as
Where
Equation (23) is the so-called Poisson’s relation and can be stated as follows: if the boundaries of a gravitational and magnetic source are the same and its magnetization and density distribution being uniform, then the magnetic potential is proportional to the component of gravitational attraction in the direction of magnetization, i.e.
Poisson relation can be used to transform a magnetic anomaly into pseudo-gravity, the gravity anomaly that would be observed if the magnetization were replaced by a density distribution of exact proportions [19]. Pseudo-gravity transformation is a good aid in interpretation of magnetic data. In addition, Poisson’s relation can be used to derive expressions for the magnetic induction of simple bodies when the expression for gravitation attraction is known.
The form of magnetic anomaly from a given body is complex and generally depends on the following factors:
The geometry of the body
The direction of the Earth’s field at a location of the body
The direction of polarization of the rocks forming the body
The orientation of the body with respect to the direction of the Earth’s field
The orientation of the line of observation with respect to the axis of the body.
Thus the computations of models to account for magnetic anomalies are much more complex than those for gravity anomalies. As earlier stated, when the gravity expressions for simple geometrical bodies are given, we can use the Poisson’s relation to find the magnetic expressions over these models [18]. Table 2 is a summary of few of such computations.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Sphere of radius, | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Horizontal cylinder of infinite length of cross sectional radius, r | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Horizontal slab of thickness, t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tZero | \n\t\t
Gravity and magnetic potentials caused by simple sources, along with magnetic induction for bodies of uniform density, ρ and magnetization,
The total-field magnetic anomaly of section 7 which was obtained by subtracting the magnitude of a suitable regional field (the IGRF or DGRF model for the survey date) from the total-field measurement is referred to as the crustal field. As earlier stated, this field is a vector sum of the remanent and the induced fields of the magnetically susceptible rocks of the crust down to the bottom of the Curie depth. The induced field component is usually in the same direction as the ambient field during the survey period.
Magnetic data processing includes everything done to the data between acquisition and the creation of an interpretable profile map or digital data set. Apart from the effect earlier discussed which are ignored or avoided, rather than corrected for, the correction required for ground magnetic data are insignificant especially when compared to gravity. The influence of topography (terrain) on ground magnetics on the other hand can be significant. Magnetic terrain effects can severely mask the signatures of underlying sources as demonstrated by [20]. Many workers have attempted to remove or minimize magnetic terrain effects by using some form of filtering as summarized in [21].
Interpretation of magnetic anomalies has to do with (a) studying the given magnetic map, profile or digital data to have a picture of the probable subsurface causes (qualitative interpretation), (b) separating the effect of individual features on the basis of available geophysical and geological data (further separation of broad-based or long-wavelength anomalies) and (c) estimating the likely parameters of the bodies of interest from the corresponding ‘residual’ or anomalies (quantitative interpretation).
The last two categories of interpretation procedures can be further broken into three parts. Each part has the goal of illuminating the spatial distribution of magnetic sources, but they approach the goal with quite different logical processes.
In summary, any geophysical survey has two domains or spaces of interest: data space (data collected from the field) and model space (earth models) that account for the data space. The task is to establish a link between a data space and a model space (Fig.4).
Connecting link between data space and model space in forward and inverse method
The task of retrieving complete information about model parameters from a complete and precise set of data is inversion. Thus if we have a set of data collected from the field, we try to say about the earth model with those finite data set. How many different ways can one try to travel within a data space and a model space? What difficulties are encountered? How many different ways can one try to overcome those difficulties? How much information can one really gather and what are their limitations? What precautions should be taken as one moves from one space to another? Inverse theory seems to address these questions. Inverse method is a direct method in which source parameters are determined in a direct way from field (e.g. magnetic field) measurements.
Forward method on the other hand entails starting from model space (Fig. 4) by guessing initial model parameters and then calculating the model anomaly (in data space). The model anomaly is compared with observed (data) anomaly. If the match between the two is acceptable, the process stops, otherwise model parameters are adjusted and the process repeated. Forward method is an indirect method.
Various formulae exist for computing magnetic field of regular shapes such as the ones given in Table 2.
Analysis of magnetic data and their various enhancements via a suite of qualitative and quantitative methods as outlined above results in an interpretation of the subsurface geology. Qualitative interpretation relies on the spatial patterns that an interpreter can recognize in the data. Faults, dykes, lineaments and folds are usually identified. Intrusive bodies are often recognized by virtue of the shape and amplitude of their anomalies and so on. For example, detection of a fault in a magnetic map is an important exercise in mineral prospecting. Usually faults and related fractures serve as major channel-ways for the upward migration of ore-bearing fluids. Fault zones containing altered magnetic minerals can be detected from series of closed lows on contour maps, by inflections or by lows on magnetic profiles or by displaced magnetic marker horizons.
Quantitative interpretation on the other hand is meant to compliment the qualitative method and seeks to provide useful estimates of the geometry, depth and magnetization of the magnetic sources. Broadly categorized as curve matching, forward modeling or inversion, quantitative techniques rely on the notion that simple geometric bodies, whose magnetic anomaly can be theoretically calculated, can adequately approximate magnetically more complex bodies. Geometric bodies such as ellipsoids, plates, rectangular prisms, polygonal prisms and thin sheets can all be calculated. Complex bodies can be built by superposing the effects of several simple bodies. Faults are often modeled using thin sheet model.
Like most other geophysical methods, magnetics is ambiguous to the extent that there are an infinite number of models that have the same magnetic anomaly. Acceptable models should be tested for geological plausibility.
The Upper Benue Trough, Nigeria (UBT) is the northern end of the nearly 1000 – km NE-SW trending sediment-filled Benue Trough, Nigeria (Fig. 5). Apart from the adjacent Niger Delta area and the offshore region where oil/gas exploration and production are taking place, the Benue Trough as an inland basin lacks the same full attention of the oil/gas companies. However, with the upbeat in the exploration efforts of the national government towards the search for hydrocarbon prospects of this inland basin, particularly in the light of new oil discoveries in the nearby genetically related basins, attention is directed at the structural setting of this basin.
The Benue Trough as a NE-SW trending sedimentary basin has a Y – shaped northern end (a near E – W trending branch of the Yola – Garoua and north-trending branch of Gongola – Muri) (Fig. 5). The Benue Trough is filled with sediments that range from Late Aptian to Palaeocene in age and whose thickness could reach up to 6000 m at places. The environments of deposition also varied over time such that the sediments vary from continental lacustrine/fluviatile sediments at the bottom through various marine transgressive and regressive beds, to immature reddish continental sands at the top.
For the past 50 years, the published works on the geology of the Benue Trough have substantially increased. The most important regional geological work on the Benue Trough by [49] was a basis for subsequent geological interpretations. [49] interpreted the Benue Trough origin in terms of rift faulting and the folding of the Cretaceous age associated with the basement flexuring. The first geophysical contribution of [50] on the Benue Trough remains to date the unique published reference. These authors have proposed the same rift origin considering that the main boundary rift faults are concealed by the Cretaceous sediments. They observed the existence below the Benue Trough axis, of a central positive gravity anomaly interpreted as a basement high. Field evidence indicates that a set of deep-seated faults is superimposed on the axial high and controlled the tectonic evolution of the trough.
Top is map of Africa showing Nigeria. Below is a simplified geological map of Nigeria (modified from http://nigeriaminers, org). The inset rectangle is the Upper Benue Trough, Nigeria
The rift origin of the Benue Trough supported by numerous authors was interpreted in the plate tectonics concept and from the 1970s several models were proposed to explain the origin of the Benue Trough. For example, seen as a direct consequence of the Atlantic Ocean opening, the Benue Trough was considered to be the third arm of a triple junction located beneath the centre of the present Niger Delta [51] and proposed a Ridge-Ridge-Ridge (RRR) triple junction. This hypothesis has been widely discussed and replaced in the general framework of African Rift System.
The Benue Trough is subdivided into 3 units on the basis of stratigraphic and tectonic considerations. The southern ensemble is called the Lower Benue Trough (LBT) and includes two main units: the Abakaliki Uplift or Anticlinorium and the Anambra Basin. The Abakaliki Anticlinorium (AA) is formed by tightly folded Cretaceous sediments intruded by numerous magmatic rocks. From the Niger Delta, AA extends for about 250 km to the Gboko – Ogoja area in a N50E direction. To the north of AA is a vast synclinorial structure called the Anambra Basin and trends in a N30E direction. This basin comprises a thick undeformed Cretaceous series. On the northern margin of the Anambra Basin, is the Nupe or Middle Niger Basin which stretches along a NW-SE direction. To the south, AA is flanked by the Afikpo Syncline and by a narrow strip of thin, undeformed sediments resting on the Basement Complex (the Mamfe Basin) and to the northwestern border is the Oban Massif. South of the Oban Massif is the Calabar Flank, which belongs to the coastal basins of the Gulf of Guinea.
The Upper Benue Trough (UBT) which is the northern ensemble is the most complex part (Fig. 5). It is characterized by cover tectonics and can be further subdivided into several smaller units. The Gongola – Muri and Yola – Garoua branches are digitations of the Benue Trough and present a similar tectonic style. The Gongola - Muri rift disappears beneath the Tertiary sediments of the Chad Basin and so the margins of this rift are geologically the most difficult to established. The Yola – Garoua rift to some extent is the least known of the West African Rift and strikes E-W into Cameroon. On the western margin of the UBT is the flat-lying Paleocene Kerri Kerri basin resting unconformably upon the folded Cretaceous. The development of the Kerri Kerri basin is said to be controlled by a set of faults between it and the Basement Complex of the Jos Plateau [52]. The basin formation and its tectonic activity seem to be a response of the general uplift of the UBT due to late Cretaceous folding.
The UBT is contiguous with the Nigerian sector of the Chad Basin which extends northwards into the Termit Basin of Chad and Niger and southwestwards into Cameroon and southern Chad as Bongor, Doba, Doseo and Salamat basins. This rift system is closely linked with oil-rich Muglad Basin of Sudan.
Magnetic data over Nigeria have been largely collected above the ground surface in form of systematic surveys on behalf of the national government. These airborne surveys were carried out principally by a consultant, namely:
The total field aeromagnetic field intensity for the UBT comprises 16 half-degree grids acquired from NGSA and is used for the purpose of the present study. These are 131_BAJOGA, 132_GULANI, 133_BIU, 134_CHIBUK; 152_GOMBE, 153_WUYO, 154_SHANI, 155_GARKIDA; 173_KALTUNGO, 174_GUYOK, 175_SHELLEN, 176_ZUMO; 194_LAU, 195_DONG, 196_NUMAN and 197_GIREI total magnetic intensity (TMI) grids.
Fugro Airborne Surveys photo showing a magnetometer in a ‘stinger’ behind the fixed-wing aircraft.
The aeromagnetic data obtained have gone through on-board processing such as magnetic compensation, checking/editing, diurnal removal, tie line and micro leveling.
The composite total field aeromagnetic data for the UBT are displayed in image (Fig. 7). The advantage of images is that they are capable of showing extremely subtle features not apparent in other forms of presentations (such as contour maps). They can also be quickly manipulated in digital form, thereby providing an ideal basis for on-screen GIS-based applications.
The total-field aeromagnetic intensity over UBT. A base value of 26, 000 nT should be added to map value for the total-field
We have further treated the composite total field aeromagnetic data (Fig. 7) for the UBT for the main field effect through the removal of the Definitive Geomagnetic Reference Field (DGRF – 2005) (Fig. 8) resulting in total magnetic field intensity anomaly (Fig. 9). This anomaly field has polarity signs that shows the impact of low geomagnetic inclination values for the study area and also reflects (1) the
Definitive Geomagnetic Reference Model (DGRF2005) over UBT at 100 m above ground level. The Earth’s model used is geodetic and CI is 50 nT.
We have computed for the present study area (UBT) the field, F for the epoch year 2005 and is displayed in contour form (Fig. 8) and because of the relatively small size of the study area, the values of D and I cannot be contoured and imaged. However between these values of Longitudes and Latitudes (11oE – 13oE, 9oN – 11oN), D ranges from -1.4o (11oE, 9oN) to -0.7o (13oE, 11oN) and I ranges from -5.7o (11oE, 9oN) to -0.2o (13oE, 11oN).
The Definitive Geomagnetic Reference Field (DGRF) or the main field map of the study area (Fig. 8) shows a NW-SE trending lines that have increasing values from the SW portion (minimum value of 33688 nT) to the NE portion (maximum value of 34271 nT) having an average value of 33974 nT and standard deviation of 129 nT. The inclination of the field for this epoch period (2005) decreases correspondingly from -5.7o to -0.2o indicating that slightly north of 11o latitude, the inclination of 0o or magnetic equator passes. We are therefore dealing with a low magnetic latitude area. Similarly the geomagnetic declination varies correspondingly from -1.4o to -0.7o which also shows that further north of 11o latitude, the declination would be 0o, indicating that geomagnetic and geographic meridians coincide. Computations of the rate of change of declination, D (in minutes per year) shows a constant value of 6 minutes/year, rate of change of inclination, I shows a northward increase from -4 to -3 minutes/year and also a northward increase of secular variation in the total field, F of between 21 – 24 nT/year. This shows that beginning 2010, Nigeria will be completely in the southern magnetic hemisphere in the next 40 years, where then the 0o latitude or magnetic equator will be passing through Niger Republic.
The image display of the aeromagnetic total field anomaly map (Fig. 9) has negative anomaly values. This is not surprising and in fact it is expected. The study area and generally Nigeria is situated in a magnetically low-latitude area. The polarizing field of the Earth in such areas is the horizontal component, H. Note that the structure of the Earth’ magnetic field resembles that of a bar magnet. At the magnetic poles, the field is essentially vertical (Z), at the centre of the magnetic bar the field is horizontal (H). The horizontal component, H of the total field, F around or at the magnetic equator is therefore the polarizing field. Any magnetically susceptible (non-zero susceptibility) earth materials within this area will be magnetized or polarized by H. When H field induces a polarization field in a susceptible material, the orientation of the field lines describing the magnetic field is rotated 90o. Above this susceptible earth material, the polarization field now points in the opposite direction as the Earth’s main field. Thus the total field measured will be less than the Earth’s main field, and so upon removal of the main field, the resulting anomalous field will be negative. This is not the case in high-latitude areas, for the same susceptible earth material, where the anomalous field over such would be largely positive and/or negative where also the rotation of the polarizing field depends on the value of inclination, I.
The anomaly map in Figure 9 can be broadly characterized into at least 4 colour zones with the following grid values: -2264 to – 982 nT, -982 to – 877 nT, -877 to -731 nT and -731 to -653 nT running from the NE edge and terminating to the SW side. There appears to be a shear zone running NW-SE nearly bisecting the area and passing through Girei, Shellen, Wuyo and disappearing or being interrupted by Gombe grid probably by reason of an offset NE-SW feature occupying the middle of Gombe grid and pinching out on Biu grid (Fig. 9). The Biu basalts and other high-susceptibility rocks around must have been very influential in the recorded low magnetic anomaly values at NE portion of the map area, particularly towards the northern edges of Bajoga, Gulani, Biu and Chibuk grids.
To demonstrate the usefulness of digital tools in the analysis of magnetic data, we shall apply only one digital processing tool to the analysis of the aeromagnetic total-field anomaly over UBT. We shall use the analytic signal technique.
The total-field aeromagnetic anomaly over UBT. A value of 1000 nT should be added to map values.
The analytic signal for magnetic anomalies was initially defined as a ‘complex field deriving from a complex potential’ [30]. A 3-D analytic signal
Where
The field and the analytic signal derivatives are more easily derived in the wave number domain. If F (M) is the Fourier transform of M in the 2-D wave number domain with wave number
Where
The amplitude of the 3-D analytic signal of the total magnetic field anomaly produces maxima over magnetic contacts regardless of the direction of magnetization. The absence of magnetization direction in the shape of analytic signal anomalies is a particularly attractive characteristic for the interpretation of magnetic field near the magnetic latitude like the area under test. It is also known that the depths to sources can be determined from the distance between inflection points of analytic signal anomalies, but have not explored that option and interested readers can refer to [54].
In this method, we have applied the concept of analytic signal to the residual total magnetic field intensity of the UBT. These processes were accomplished by use of Geosoft Oasis Montaj (version 8.0).
Figure 10 shows the output of the analytical signal amplitude calculated from the original total-field magnetic anomaly (Fig. 9). Analytic signal of the total-field magnetic anomaly reduces magnetic data to anomalies whose maxima mark the edges of magnetized bodies and whose shape can be used to determine the depth of these edges (we have not done this second aspect).
Output of the analytic signal amplitude over UBT. The boundary of high amplitude anomaly over the Biu area (basaltic areas) are delineated
The analytic signal amplitude over the UBT ranges from 0.00 nT/m to 7.93 nT/m: having a mean of 0.036 nT/m and standard deviation of 0.073 nT/m. Since amplitude of the analytic signal anomalies combines all vector components of the field into a simple constant, a good way to think of analytic signal is as a map of magnetization in the ground. With this in mind, we can picture strong anomalies to exist over where the magnetization vector intersects magnetic contrasts, even though one cannot know the source of the contrasts from the signal amplitude alone. Consequently, we can easily see the boundaries of the Biu basalts properly demarcated (Fig. 10) shown by the higher analytic signal values. Note also the few scattered imprints of the same basalts tailing to the SW direction from this major anomaly. The Biu area is composed of Tertiary and Quaternary periods (less than 65 Ma ago) basaltic lava flows containing abundant peridotite xenoliths.
In this chapter we have explored the magnetic method for economic exploration of the Earth. The strength of the method lies in the adequate distribution of magnetization within the crustal materials of the Earth in the light of measurable magnetic field over them.
The Earth’s magnetic field, that is central in the remanence and induced processes, is itself complex. Spherical harmonic analysis provides the means of characterizing the Earth’s magnetic field and with such a representation; it is possible to predict the geomagnetic dipolar field and other non-dipolar components. The knowledge of the dipolar field of the Earth enables the magnetic anomaly to be determined over a survey area from measurements of the magnetic field induction.
We have applied the magnetic method to real field measurements of total-field aeromagnetic intensity data over the Upper Benue Trough, Nigeria. The working data were corrected for secular variation using the existing DGRF model. The anomaly field which is the summary of the crustal field was further processed to obtain the amplitude of analytic signal of the anomaly field. The analytic signal transformations combine derivative calculations to produce an attribute that is independent of the main inclination and direction of magnetization as well as having peaks over the edges of wide bodies. Thus a simple relationship between the geometry of the causative bodies and the transformed data is observed such as seen in Figure 10. We note that the borders of the Biu basalt as exposed by the applied analytic signal technique of the magnetic anomaly data goes beyond the outcropping boundary that may be offered by remotely sensed data. We recognize that even though the magnetic data were remotely sensed, the result from such measurements goes beyond what the traditional remote sensing information can offer.
The greatest limitation of the magnetic method is the fact that it only responds to variations in the magnetic properties of the earth materials and so many other characteristics of the subsurface (e.g. regolith characteristics) are not resolvable. The inherent ambiguity in magnetic interpretation (for quantitative techniques) is problematic where several geologically plausible models can be attained from the data. Interpreters of magnetic data must therefore be aware of such limitations and be prepared to obtain confirmatory facts from other databases to decrease the level of ambiguity.
I acknowledge the National Centre for Petroleum Research and Development (NCPRD), of the Energy Commission of Nigeria (ECN), Abubakar Tafawa Balewa Balewa University, Bauchi, Nigeria for supporting this research.
Agriculture is the main occupation in India and no doubt experienced farmers are the expert at doing everything. They can take the decisions; some decisions may fail so need to do some analysis for good results. One of the decisions we are considering here is “which crop to be adopted to have better yield?”. It needs to analyze all the features on which yield is dependent and predict the suitable crop/crops. The basic feature i.e. agriculture land is the natural feature, which cannot prepare artificially and that too increase in the soil degradation is one of the serious issues farmers are facing [1]. There are multiple reasons affecting on soil quality one of them is, use of fertilizers without knowledge. For increasing yield farmers are using unbalanced quantity of fertilizers [2], without knowing the effect of it.
Even young farmers cannot take decision about which crop is suitable for current situation (soil quality, environment etc.). Farmers are adopting some crop and using chemicals for better yield, we have a good example for this from case study at Kolhapur district [3] which was resulted in causing cancer. To avoid such worst impacts we are thinking about recommending the suitable crop/crops. To get the genuine decision we are using historical data. Once the pattern of historical results identified successfully, predictions can be done appropriately.
There are many algorithmic approaches available for predictive analysis, machine learning is the branch which makes computer to learn from the dataset available. We are discussing about the 1) Role of machine learning in precise decision making about agriculture, 2) How to classify suitability level of crop/crops and feature values.
Since old days farmers are taking farming decisions about agriculture by experience. Here we are discussing about the decision of choosing the crop to be adopted. Though agriculture is a major sector in India, same trend is going on. Automated and advanced systems in the form of software and physical machines are invented and available for use in developed countries, some of them are available in developing countries like India as well. Available facilities are either unaffordable or not easily approachable. Some systems are developed for some particular geographical area, it cannot be adopted for India as it is. Software techniques available in developed countries are not applicable due to fragmented land, because those systems are developed considering unfragmented land.
In India Krishi Vigyan Kendra (KVK) are the centers made available for farmer’s guidance. It is responsible to spread technical knowledge among farmers. They conduct training, awareness programs to achieve some goals set as below:
Develop advisory services for farmers.
Conduct training program on different trends.
Conduct training programs for different level of people working in agriculture.
Agriculture field testing
Demonstration for recent innovations
It is playing an important role for needy farmers. Multiple KVK centers are there, so they can work according to the location to develop location specific solutions. It also has contribution in providing quality products like planting material, seed, organic material, livestock related products etc. As above mentioned, all agriculture related centers are available to facilitate people working in agriculture sector.
Despite all the knowledge centers, testing facilities available, farmers are continuing with traditional methods. The major change they have adopted is after Green revolution. Green revolution has shown drastic increase on yield [4, 5]. Other than using high-yielding varieties of seeds and the improved quality of fertilizers farmers are not interested to adopt new technics. There are many reasons behind it, as discussed further.
The one who work in farm are not well educated, not aware about the ongoing innovations. Even if they become aware, they cannot afford it. Economic condition of most of the farmers in India is not sufficient to purchase the automated systems available in the market. At another end, the educated, economically well, and aware people not much involved in actual farming and related occupations. The traditional farming methods are dependent on natural parameters. Uncertainty in the nature directly effects on agriculture production, so not getting yield as expected. Thus, since long ago there is no improvement in the economic condition of small/marginal scale farmers in India.
Decision making system available in other countries. One of the popular decision making system is Agriculture Land Suitability Evaluator (ALSE), is the crop specific evaluator at Peninsular Malaysia [6]. Here crop specific means, it works for mango, citrus, guava, papaya and banana as well. Base for decision making is cultivation history, cultivation knowledge, land characteristics, climate features such as annual precipitation, dry season length per month, land slope, nutrient availability, and retention. Some other features are used for land availability and suitability evaluation in Tuban Regency, Java Island [7]. Spatial multi-criteria analysis has been done based on parameters like, land elevation, slope, slope direction, land use/land cover, land capability, integrating soil order, climate, and accessibility. Outcome prepared was land use plan by the spatial pattern. This is the area having most fertile land in the country. The weights are assigned to above mentioned criteria with the help of eight experts involved for sub-criterions. Criteria wise scores are assigned in the range of 0–10, according to involvement of sub criterions under each criterion. Weighted sum overlay method used those weights to prepare suitability map. Suitability analysis for crop Soybean in Indonesia [8], to satisfy the local need. Regular domestic consumption of soybean was more so the, need were not fulfilled. The research was conducted in Karawang Regency, West Java, Indonesia to identify suitable area for soyabean plantations in paddy fields and prepared plan for it. The suitability classes defined according to FAO categories from suitable (S2) to not suitable (N). For wheat crop suitability analysis conducted in North Carolina [9]. The case study under taken was rain-fed wheat. Five criterions considered were soil-fertility, climate, soil-features, soil-organic-matter, soil-quality, soil-chemistry and seventeen sub-criteria under that. This system also considers geographic information systems (GIS) as base and the square root method is used, called multi-criteria analysis. Percentage of land suitability for organic wheat was highly suitable- 18.6% and moderately suitable- 76.8% in Duplin country. Existing yield simulation method was also based on Moderate Resolution Imaging Spectroradiometer [10]. A case study for corn and soybean yield simulated within a certain scope of area, predictions are given by the United States Department of Agriculture (USAD)- National Agricultural Statistics Service (NASS). All above discussed systems are suitable for unfragmented land not for fragmented lands I India.
As discussed above still the decision making in India mainly for small/marginal scale fields is by traditional way. So, farmers are not getting expected yield due to many reasons like decreasing quality of soil, uncertainty in nature. Till the moment automated machines are not used by small/marginal scale Indian farmers and are dependent on labors. There are many reasons which makes them to be dependent on labors as below:
Poor economic condition, so unable to purchase machines.
In fragmented lands, it is difficult to use the big machineries like tractor and tractor accessories.
Unaffordable cost of automated systems.
Unaware due to lack of interest.
Though the farming is the main occupation in India, due to urbanization people are moving to urban areas in search of jobs and it causes labor deficiency. So, labor cost is increasing which add on to farming expenses and again it is lowers the economic condition of farmers.
Decision making is in the context of choosing the appropriate crop which give good outcome in available natural conditions like soil, environment. The reason behind it is soil and environment cannot be controlled. So, first we need to study about the technologies available to analyze the data collected through monitoring and recording the soil and environmental features. There are existing prediction techniques we can study to choose the suitable method and we can do the crop suitability analysis based on historical data. Suitability analysis will give the level of suitability for crop/crops and then user can take the decision accordingly. The crop/crops having fair suitability level can be adopted for better yield.
We have divided this advisory system into sub parts as below:
The first important task is to identify the features affecting on crop yield. As per the guidelines available at country level [11], state-wise [12], local [13] through variety of sources. We have listed the fallowing recurrent features from variety of sources and suitable ranges of the possible features.
Topography
Temperature
Soil type
Soil Quality
Rainfall
Soil moisture
pH
EC
Soil nutrients: Nitrogen, Phosphorus, Potassium
Soil micronutrients
All above features and some features can be added or removed as per requirement and availability of data like humidity, soil texture etc. Some of the features remain constant for the period of a year or more than that ex. Topography of land does not change for years. Some features need to measure for the period of a season or less than that ex. rainfall required at the beginning that is seeding phase of the crop is different than the growing phase of the crop. As usual we are considering two seasons of cropping i.e. Kharif and Rabi. There are three main phases of the crop seeding, growing and maturity. Each phase has different requirement for rainfall, soil-moisture, temperature, nitrogen level, phosphorus level and potassium level. We will discuss about the influence of features on crop growth one by one. To consider these values for analysis purpose we need to normalize the feature values. Some of the feature values are considered in the numeric form and some need to convert into categorical form. Textual values cannot be considered as it is, it needs to map to the numeric categories. For better understanding of the technique let us take an example of the crop wheat.
Topography
Topography is nothing but the slope of the crop land. The field having slope less than 10 degree is good to use for cropping. It is a natural feature which can be controlled artificially. With or without analysis we can say this is vital feature contributing towards decision making for crop suitability. Sloppy fields are not good at holding the water at higher ends and may have clayey soil at lower end this uneven nature is not suitable for crop growth. Some of the crops with less water requirement can be adopted provided the slope is less. To use the topography values for algorithm purpose we have categorized it. Plane surface is always most suitable for every crop so, the categorization is done as 1-Plane, 2- slope less than 10 degree and 3-slope more than 10 degree.
Temperature
It is again a natural parameter. Geographical location of India is such that enough solar energy is available. This feature values need to consider phase wise. Suitable ranges of temperature is discussed in Table 1. If temperature goes below or above the range crop yield get reduced. The values of temperature can be considered as it is in the numeric form with unit °C (degree Celsius).
Soil type
Type of the soil plays a key role in crop yield. Water holding capacity is dependent on the soil type. For some of the crops clayey soil is good and for some loamy. Few crops can be grown in sandy soil as well. Soil type is textual value, cannot be considered as it is. Here we are categorizing it according to water holding capacity. First category is 1- Loamy, 2- Clayey, 3- Slity, 4- Sandy.
Soil quality
Quality of the soil is dependent on multiple features like soil nutrients, micronutrients, texture, water holding capacity and it may varies according to the chemical used, erosion occurred etc. Krishi Vigyan Kendra helps to know the quality of soil in terms of levels. With reference to that we are considering the soil quality levels as 1- Good, 2- Moderate, 3- Marginal 4- Low.
Rainfall
Rainfall is the natural feature on which other features dependent. Water requirement for different crop is different. Precipitation has some annual pattern, sometimes it may vary. Here we can directly consider the numeric values or range of it for analysis purpose. This feature needs to measure according to phases of the crop.
Soil moisture
Water holding capacity of the soil, supplied water decides the soil moisture level. Good quality and type of soils has enough moisture content. Not only rainfall but irrigation sources also contribute to decide the soil moisture values. Here we are taking its numerical values as it is phase wise.
pH
It shows acidity or alkalinity of soil and is measured in pH units. Different crops can bear different level of acidity in soil and water. This numerical value is considered in the range of 0 to 14.
EC
Soil electrical conductivity is measure of amount of salinity, one of the indicators of soil health. Excess salinity levels occur in arid and semiarid regions. For this feature as well numeric values considered directly. The range of it is from 0.611 to 25.9 dS m − 1.
Soil nutrients: Nitrogen, Phosphorus, Potassium
Major soil nutrients are nitrogen, phosphorus, potassium. This feature is artificially manageable. Deficiency can be resolved by adding the fertilizers available in the market but excess amount available can not reduced in any way. If farmer get their soil tested, they come to know the existing level of nutrients and amount of fertilizers to be added. But they do not approach for it and add the fertilizers without knowing the requirement. This is leading to soil degradation. Once we know the suitable crop and existing amount of nutrients, it is easy to recommend the appropriate amount of fertilizers to add and avoid the soil degradation up to certain extent.
Soil micronutrients
There are subcategories of soil micronutrients. Soil cab be tested into laboratory to know the availability of micronutrients, so that we can understand one aspect of soil quality and take future decision.
Sr. No. | Temperature | Soil quality | Suitability |
---|---|---|---|
1 | 24°C | Good | Level1 |
2 | 22°C | Good | Level1 |
3 | 20 °C | Good | Level1 |
Sample tuple in dataset.
These are some basic feature we have considered here. We need to keep all the information, while recording these feature values: 1) what was the location? 2) what were the date and time? 3) what was the cropping season? 4) what was the crop/crops adopted? and 5) what was the final yield? There are some other features affecting on yield but either has less effect or values are not available easily. If more factors are considered, results get quite improved.
There are two different sources from which dataset can be collected. Either we can have sensor-based device which will sense actual field features and record the dataset. According to features we want to monitor; device can be integrated with respective sensors (available in market) and microcontroller to control the data recording [14] and storing process. This method can be referred as monitoring feature values. Another method is to prepare dataset from available online or offline sources called as gathering feature values.
Environmental data is available with the meteorological department since long ago. Parameters required for agriculture are with the different scope ex. Rainfall measured by meteorological department is area wise, city wise whereas for agriculture purpose rainfall need to measure at specific location. Soil features also vary one farm to another farm as per the crop adopted and fertilizers used. So the global reports prepared cannot be referred as it is for deciding suitable crop. Crop specific monitoring system can be used as described below to measure the field specific features.
A monitoring system with microcontroller and accessories [14] to sense the nearby features like.
Rainfall sensor
Moisture sensor
pH sensor
EC sensor
Environmental sensor
NPK measuring kit
All the accessories like above can be used to monitor the actual field. There is lot of variety in the accessories available in the market. So according to the device used, scope of monitoring that feature get varies. Depending on the area of field one or more than one system needs to plant in the same field, so that whole farm will get monitored to get more accuracy in the feature value. According to components used cost of this monitoring system get varies. The frequency of feature monitoring can be every second, minute or hour as per the code written, thus it is always editable as per user need. Data can be gathered in the tabular form, so that it can easily converted to SQL database for further analysis. Backup of data can be fetched from device as an when required. As we know microcontroller like raspberry-pi has own memory in the form SD card from which data can be fetched or we can make the provision for data transfer by using some network protocol, as raspberry-pi has default support for Wi-Fi, we just need to do the configuration and coding accordingly. This kind of data backup need only once in a season. It is better to maintain the data along with date and time (time is optional in some cases) and exact place of the data recording.
Metrological department already has the system to monitor the environmental features accurately. Also, Krishi Vigyan Kendra available at different places providing the values of soil and water features after soil and water testing. The drawback of this method is we do not get the data belongs to same location for which we want to do the suitability analysis. Metrological department has their setup at certain places only not at every place we might use for suitability analysis. KVK reports are also from the different fields, we cannot guarantee that all the soil samples collected are belongs to same location for which we want to do the suitability analysis. Here definitely we can filter out and choose the dataset belongs to same region which will give appropriate prediction. So, we collect the available and authorized data from metrology department, Krishi Vigyan Kendra and the offices working under agriculture universities and government maintaining data related to farming. This data is compiled further to fetch the values of identified features under Section 4.1.1 and crop yield value. Data need to maintain in tabular format so it can easily map to SQL database for further analysis. It is mandatory to maintain the data along with date and time (time is optional in some cases) and exact place of the data recording.
The frequency of monitored features can be changed by averaging the value for required interval. Same data can be customized for different purposes like weather prediction, crop suitability analysis, to understand the pattern of field features etc. Here we are talking about crop suitability analysis. Cropping seasons play important role in the crop specific suitability analysis. So, the interval for data analysis is also a season. Under season every crop goes through the three main phases seeding(s), growing(g), maturity(m). Each phase needs some basic requirement like in the seeding phase favorable temperature for wheat is 20°C -25°C, in the growing phase it is 15–30°C and in maturity phase it is 14°C -15°C [12] at state Punjab in India whereas the it can tolerate the temperature in the range of 3.5–35°C. It means if the crop gets that feature values in expected range in all the phases it will lead to better yield (i.e. high suitability level S1 or S2) otherwise yield may get reduced than expected (i.e. low suitability level S3 to N2). As per the crop season and the phase, interval of data collected is decided. Some of the features need to consider phase wise (ex. Rainfall) and for some features single value need to consider (ex. nitrogen, phosphorus, potassium (NPK). Along with the above identified features under Section 4.1.1 we also need to get to know the season, yield and crop/crops adopted for the fields considered through data gathering.
Collected data is further analyzed to understand the crop specific suitability for variety of crop for particular season, the one which is more suitable is advised to adopt. Here the logic is- based on historical data collected. From environmental data we understand the pattern of environmental features for current season and current phase of crop and predict that aggregate/common values in the respective season. Some of the features like NPK, pH are available as the current value of that feature, so it can be taken as it is not any prediction required here. Standard feature values/ranges required for crop is already well known to the farmers who are in farming business itself. For new farmers, the guidelines are available for cropping, feature values suitable are mentioned crop wise and phase wise under the guidelines available [12, 13]. Guidelines are available in the form of books, reports, online resources, also offline centers like KVK are always available to guide. The information provided by such resources is static information.
Here we are discussing about analyzing the suitability level of crop based on current situation dynamically. For such decision making, historical data with few current feature values can make available as discussed under Section 4.1. The suitable methodologies need to choose and customize for agriculture application. Suitability results need to compare with the existing results available, that is called as testing in machine learning. Variety of methods can be applied and then the methodology which gives more accuracy can be used further. Once the data is available, the methods based on data analysis are more suitable. Extracting the required information from available dataset is called as Data mining.
Data analysis means, identifying something based on current and historical data. Broadly it is categorized into two categories qualitative and quantitative analysis. If you want to get the answer for why, what, or how we need to go for qualitative analysis. If you want to know some statistical or categorical value, then go for quantitative analysis. Here we are discussing about suitability level analysis. According to Food and Agriculture Organization of the United Nations, suitability level is a categorical value [15], so quantitative approach has been used. Quantitative approaches further categorized as text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Here we want to predict the suitability based on historical and some current values, so need predictive analysis. If we have the historical database of the farm features along with the yield, yield of the crop/crops can decide respective suitability level of the crop for storing into dataset.
Ex. For crop C in particular farm area for season S, expected highest yield is YH Tones/Hector and lowest expected yield is YL Tones/Hector (it can be zero Tone in worst condition).
Then, interval between two adjacent levels Yi calculated as,
Five ranges of yield from higher to lower, are computed as,
Example,
Expected high yield YH = 20 Tones/Hector and lowest yield YL = 3 Tones/Hector.
So,
Interval.
There are five suitability levels defined by FAO [15] as below,
S1- suitable.
S2- moderately suitable.
S3- marginally suitable.
N1- not suitable due to physical reasons.
N2- not suitable due to economic reasons.
Now if we know actual yield Y then, suitability level decided as,
Thus, we can get the values for suitability level for historical dataset.
Machine learning is the analysis technique which enables computer to learn from experience. Here experience is in the form of dataset. More the accuracy in dataset more accurate the results are. Here accuracy has the different dimensions. It depends on multiple things like 1)Duration of the dataset captured (ex. Data gathered from last 10 years is better than the data captured for last 1–2 years), 2)Features considered (It’s better to consider maximum characteristics on which crop yield is dependent), 3)Frequency of the data recording (ex. The values of the features in field varies after certain time, so it’s better if we could record every variation in dataset), 4)Duration of the data considered for analysis (ex. To do predictive analysis for the crop in kharif season we need to consider data recorded in kharif season only, the data recorded in rabi season will work as noise). 5)Missing data (if missing data is more in proportion then accuracy in data decreased). We can prepare the dataset from historical data available and the monitoring device. Learning from the historical data and comprehending for current situation is known as supervised machine learning, one of the best predictive analysis techniques. Supervised machine learning is further categorized into regression, classification, naive Bayesian model, random forest model, neural networks, support vector machines.
Classification is the method where we divide dataset into defined classes. Class is nothing but category of the instance. Ex. In a school student are categorized into different classes. The class is decided based on features of the student like age, result of the previous year, date of birth etc. Based on marks of the students, further they can be categorized into pass class and fail class.
Some of methods are already used in agriculture, those are decision making methods based on some mathematical computations. More or less those are based on basic features of machine learning. Few examples we will discuss here. A land evaluation is done based on features belong to climate and site-soil [16]. It is developed using statistics and neural network model techniques. This is bit static method not user friendly, so not used widely. Decision support system [6] is based on soil features like topography, nutrients, history of cultivation, precipitation etc. Static categorization of the feature values is done to evaluate suitability level. The drawback of this system is also a static nature. Konstantinos G Liakos has discussed in detail how machine learning played role in agriculture precision through crop management, yield prediction, disease detection, soil and water management [17]. Patricio and Rieder [18] mentioned that artificial intelligence plays important role in improving accuracy in agriculture. During 2013–2017 data captured by camera was analyzed using support vector machine classifier. Uddin, Mohammad Shorif has discussed contribution of machine learning and computer vision in agriculture [19]. Machine learning helps in decision making, for better productivity and more precise systems. In developed countries machine learning is introduced in agriculture too early for different purpose like farming prediction is done using classification [20, 21], Artificial Neural Network technique used for crop yield prediction [22]. Even for study regarding plant disease statistical and machine learning approaches has been used [23, 24, 25, 26, 27].
As we have discussed under Section 4, we can get the feature values for a particular crop in a particular season as per the list of features identified under Section 4.1.1 and also, we can compute the level of suitability of the same crop using the methodology discussed under Section 4.2. Here, crops specific suitability is considered as output class. Suitability level is further divided into five classes [15], so classification method of machine learning is chosen. According to the discussion under Section 5, we can say that supervised technique classification is suitable for crop specific suitability analysis. We can treat the computed suitability levels based on yield as class-value for that particular record/tuple and all other feature-values as input-feature-values of the same tuple. Any classification technique, which can classify the records into more than one classes based on input features called as multi-class classification technique. Any multi-class classification technique can be used and further customized [28] to get the appropriate suitability analysis. Similar to output classes computed using Eqs. (7)–(11) input classes can be computed as below.
If input feature is categorical value then no need to compute the levels, it can be directly mapped. Example soil quality is one of the features need to consider having three different having categorical values good, moderate and average. Then it will be mapped to input levels as below:
Level1 Soil_quality = Good
Level2 Soil_quality = Moderate
Level3 Soil_quality = Average
Let us consider the input Xi is environment feature temperature at Punjab state in India. Lowest temperature is considered as 0°C and highest temperature observed is 50°C. We know in the growing phase of the crop wheat for high growth rate the favorable temperature is 20–25°C. Wheat cannot tolerate the temperature below 3.5°C so, Level5 is less than or equal to 3.5°C. It cannot tolerate the temperature above 35°C so, Level1 is above 35°C.
Thus,
Highest value is 35°C
Lowest value is 3.5°C
So, as per Eq. (1) interval is calculated as,
Xi = (35–3.5)/4 = 7.75.
X1 is 35
X2 is X1 - Xi = 35–7.75 = 19.5
X3 is X2 - Xi = 27–8 = 11.75
X4 is X3 - Xi = 21–8 = 4
To convert into input class levels, the dynamic levels will be computed. If the available dataset has tuples as shown in Table 1.
To simplify we will round up the values. The levels will be converted as below.
Level1 > =35°C.
Level2 < 35°C and > = 20°C.
Level3 < 20°C and > =12°C.
Level4 < 12°C and > =4°C.
Level5 < 4°C.
Now, as per tuples in database for suitability level 1 of output the mapping input level in Level2, thus for decision tree classification intermediate result based on above data belongs Table 1, partial decision tree will be as shown in Figure 1.
Decision making tree.
Based on simple dataset available as per Table 1, simple decision tree has been formed. If more features will be considered levels of the tree will be increased. Always the last level of the tree will output class i.e. suitability/yield level. For n number of input features tree will have n + 1 level [29].
We can conclude that machine learning can be applied in agriculture decision making. More the balanced and appropriate dataset is available better the decision can be taken. Here the machine learning approach we used, called as decision tree classification. So, we can say quality of decision tree formation is dependent on quality of input dataset. Advanced decision tree approaches work on variety of data values like categorical, constant and discrete (numerical as well as text values with some preprocessing) values. We can consider all these variety of agriculture features for processing and decision making.
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\\n\\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\\n\\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\\n\\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\\n\\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\\n\\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\\n\\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\\n\\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\\n\\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\\n\\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Pristupom na stranicu www.intechopen.com slažete se s ovim odredbama, sa svim primjenjivim zakonskim odredbama, te se slažete s poštovanjem svih lokalnih zakona. Korištenje i/ili pristup ovoj stranici temelji se na potpunom prihvaćanju ovih odredbi. Svi materijali na ovoj stranici zaštićeni su primjenjivim zakonima o autorskim pravima i žigu.
\n\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\n\nKlijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\n\nKompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\n\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\n\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\n\nOsim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
\n\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\n\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\n\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\n\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\n\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\n\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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