Features description.
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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:"5938",leadTitle:null,fullTitle:"Contemporary Topics of Pneumonia",title:"Contemporary Topics of Pneumonia",subtitle:null,reviewType:"peer-reviewed",abstract:"Pneumonia is an inflammatory disease of the air sacs and surrounding interstitium caused by infectious agents or by endogenous inflammatory tissue disorder termed interstitial pneumonia. The present book covers contemporary topics of community, hospital, and health care-related bacterial and viral pneumonia in the setting of drug resistance, environmental exposures, climate change, hormonal influences, and gender. The topic of interstitial pneumonia is brought under the lens of an immune-related connective tissue disease.",isbn:"978-953-51-3708-5",printIsbn:"978-953-51-3707-8",pdfIsbn:"978-953-51-3999-7",doi:"10.5772/66055",price:119,priceEur:129,priceUsd:155,slug:"contemporary-topics-of-pneumonia",numberOfPages:222,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"7b8f70c5a40d7270ab454c8a1e9959e8",bookSignature:"Zissis C. Chroneos",publishedDate:"December 20th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5938.jpg",numberOfDownloads:14195,numberOfWosCitations:4,numberOfCrossrefCitations:7,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:12,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:23,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 2nd 2016",dateEndSecondStepPublish:"November 23rd 2016",dateEndThirdStepPublish:"July 29th 2017",dateEndFourthStepPublish:"August 29th 2017",dateEndFifthStepPublish:"October 29th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"80345",title:"Dr.",name:"Zissis",middleName:null,surname:"Chroneos",slug:"zissis-chroneos",fullName:"Zissis Chroneos",profilePictureURL:"https://mts.intechopen.com/storage/users/80345/images/6607_n.jpg",biography:"After completing doctoral studies in Chemistry at the University of South Carolina, Zissis C. Chroneos pursued postdoctoral studies at Vanderbilt University College of Medicine and Cincinnati Children’s Medical Center to elucidate the mechanisms by which surfactant proteins modulate host defense and inflammation in the lung. These studies led to his discovery of the surfactant protein A receptor SP-R210 and its identification as cell-surface isoforms of Myosin 18A that modulate innate receptor dynamics and polarization in macrophages. He is currently an associate professor of Pediatrics, Microbiology and Immunology at Pennsylvania State University College of Medicine investigating host factors and SP-R210–mediated mechanisms that modulate surfactant metabolism, macrophage differentiation, and pathogenesis of viral and bacterial infections in the lung.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Pennsylvania State University",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1047",title:"Pulmonology",slug:"pulmonology"}],chapters:[{id:"55933",title:"Understanding the Intersection of Environmental Pollution, Pneumonia, and Inflammation: Does Gender Play a Role?",doi:"10.5772/intechopen.69627",slug:"understanding-the-intersection-of-environmental-pollution-pneumonia-and-inflammation-does-gender-pla",totalDownloads:1232,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:1,abstract:"Accumulating evidence indicates that exposure to air pollution is associated with increased mortality from respiratory disease. Exposure to ambient pollutants, such as ozone, particulate matter, sulfur dioxide, nitrogen dioxide, and other agents has been associated with decrease in lung function and immunity, and with increased rates of hospitalization for lung disease, including pneumonia. Furthermore, sex differences in frequency and severity of pulmonary disease and infection have been reported, suggesting a role of sex hormones in mediating these differences. Pneumonia, which is commonly caused by bacterial infection and subsequent lung inflammation leading to hospitalization and death, occurs at different rates in men and women. In this context, male and female hormones can have direct effects on the immunity system by binding to receptors in immune cells, and these responses can be modulated by environmental exposures. This chapter summarizes clinical, animal, and epidemiological studies linking exposure to air pollution and pneumonia in both males and females. Understanding sex-specific mechanisms in pneumonia pathogenesis and environmental responses can help in the development of more effective therapeutics and treatment options to reduce negative health outcomes in men and women.",signatures:"Patricia Silveyra, Nathalie Fuentes and Lidys Rivera",downloadPdfUrl:"/chapter/pdf-download/55933",previewPdfUrl:"/chapter/pdf-preview/55933",authors:[{id:"89055",title:"Dr.",name:"Patricia",surname:"Silveyra",slug:"patricia-silveyra",fullName:"Patricia Silveyra"},{id:"202257",title:"BSc.",name:"Nathalie",surname:"Fuentes",slug:"nathalie-fuentes",fullName:"Nathalie Fuentes"},{id:"202258",title:"Dr.",name:"Lidys",surname:"Rivera",slug:"lidys-rivera",fullName:"Lidys Rivera"}],corrections:null},{id:"57427",title:"Pneumonia: A Challenging Health Concern with the Climate Change",doi:"10.5772/intechopen.71609",slug:"pneumonia-a-challenging-health-concern-with-the-climate-change",totalDownloads:1483,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Pneumonia is still a global health concern with high mortality rate, mainly among children under 5 years and adults over 65 years. In addition to pathogen virulence, immunoevasion capacity, and drug resistance ability, risk factors for the patient include aging, comorbidities, malnutrition, and all causes affecting the immune system. The extent to which environmental disorders affect the respiratory health is established for chronic diseases such as asthma, COPD, and cardiovascular diseases, but less is known about the underlying mechanisms of their impact on infectious diseases of the respiratory system. This chapter aims to recall the epidemiology, diagnosis, and treatment of pneumonia, with a focus on the impact of climate change and related risk factors on acute low tract respiratory infections.",signatures:"Jean-Marie Ntumba Kayembe and Harry-César Ntumba Kayembe",downloadPdfUrl:"/chapter/pdf-download/57427",previewPdfUrl:"/chapter/pdf-preview/57427",authors:[{id:"123153",title:"Prof.",name:"Jean-Marie",surname:"Kayembe",slug:"jean-marie-kayembe",fullName:"Jean-Marie Kayembe"},{id:"218877",title:"MSc.",name:"Harry César",surname:"Kayembe Ntumba",slug:"harry-cesar-kayembe-ntumba",fullName:"Harry César Kayembe Ntumba"}],corrections:null},{id:"56193",title:"The Emerging Problems of Carbapenem-Resistant Gram- Negative Bacillary Pneumonia",doi:"10.5772/intechopen.69630",slug:"the-emerging-problems-of-carbapenem-resistant-gram-negative-bacillary-pneumonia",totalDownloads:1271,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Carbapenem-resistant Gram-negative organisms are increasingly isolated from lower respiratory tract infections. Limited treatment options are the main problems for physicians and clinical microbiologists who have to face such clinical cases. Bacteriological diagnosis, starting with accurate Gram smear performed from properly collected specimens and ending with antibiotic susceptibility testing, is essential. Morphological characters of bacterial cells provide important clues about the nature of infection, prior to bacterial isolation and identification. Attempts to find complementary options for the respiratory contamination and treatment of carbapenem-resistant Gram-negative bacillary pneumonia led us to test the susceptibility of 21 essential oils. Among them, Thymus vulgaris, Eugenia caryophyllata, Origanum vulgare, Melaleuca alternifolia and Aniba rosaeodora essential oils proved to be efficient against Acinetobacter baumannii carbapenem-resistant strain and Escherichia coli ATCC 25922. In an attempt to evaluate the magnitude of environmental spreading of the carbapenemase genes, 40 carbapenemase sequences of different organisms were compared. Carbapenemases show striking similarities inside each beta-lactamase class (A, D, and B), no matter their origin—environmental organisms or clinical isolates. Class B carbapenemases are most widely distributed, metallo-beta-lactamases being present in bacteria as well in Archaea.",signatures:"Mihaela Ileana Ionescu",downloadPdfUrl:"/chapter/pdf-download/56193",previewPdfUrl:"/chapter/pdf-preview/56193",authors:[{id:"201932",title:"Dr.",name:"Mihaela",surname:"Ionescu",slug:"mihaela-ionescu",fullName:"Mihaela Ionescu"}],corrections:null},{id:"55737",title:"Multidrug-Resistant Gram-Negative Pneumonia and Infection in Intensive Care Unit",doi:"10.5772/intechopen.69377",slug:"multidrug-resistant-gram-negative-pneumonia-and-infection-in-intensive-care-unit",totalDownloads:1798,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Multidrug-resistant (MDR) pneumonia can be problematic and challenging to treat in an era of increasing resistance and limited treatment armamentarium. Multidrug-resistant pathogens are associated with increased morbidity and mortality, thus early empiric appropriate antibiotics are critical for survival. Many factors play a role in the selection, optimization, and duration of therapy that should be made on an individual basis. New technologies such as “rapid diagnostics” may provide the clinician with early phenotypic or genotypic result, thus improving early appropriate therapy. The increasing antibiotic resistance is a global threat to patients worldwide and is an economic burden. In the United States, drug-resistant bacteria cause approximately 2 million cases of illnesses and contribute to 23,000 deaths each year. The inappropriate use of antibiotics has contributed to the healthcare burden that ranges from $27 to $42 billion annually. As a result, several governmental agencies have placed forth regulatory mandates to enforce antimicrobial stewardship programs in acute care hospitals. Education will be vital across all healthcare disciplines to ultimately ensure optimal prescribing and reduce the emergence of resistance.",signatures:"Mauricio Rodriguez and Salim R. Surani",downloadPdfUrl:"/chapter/pdf-download/55737",previewPdfUrl:"/chapter/pdf-preview/55737",authors:[{id:"15654",title:null,name:"Salim",surname:"Surani",slug:"salim-surani",fullName:"Salim Surani"},{id:"201286",title:"Mr.",name:"Mauricio",surname:"Rodriguez",slug:"mauricio-rodriguez",fullName:"Mauricio Rodriguez"}],corrections:null},{id:"55756",title:"Microbiology of Ventilator-Associated Pneumonia",doi:"10.5772/intechopen.69430",slug:"microbiology-of-ventilator-associated-pneumonia",totalDownloads:1954,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Ventilator-associated pneumonia (VAP) is a pulmonary infection that appears after 2 days of endotracheal intubation and when invasive mechanical ventilation is used. VAP is considered the most common nosocomial infection in the intensive care unit (ICU) and presents high morbidity and mortality rates, principally when caused by multi-resistant bacteria. Several risk factors are associated with VAP, including the microbiota, advanced age, immunocompromising conditions, pulmonary illness, length of mechanical ventilation, the aspiration technique, tracheostomy, supine positioning, enteral feeding, previous antibiotic exposure, among other endogenous and exogenous factors. The main pathogens are Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, and Enterobacteriaceae members, which are considered potentially multidrug-resistant pathogens. Conventional microbiology methods continue to be used for laboratory diagnosis. However, it is necessary to validate rapid and accurate laboratory methods, such as molecular assays that detect multiple gene sequences of a wide range of bacterial species and resistance markers. Therefore, the objective of this chapter is to review and update several aspects related to VAP, including risk factors, etiology, laboratory diagnosis, bacterial virulence and VAP severity, and antibiotic susceptibility.",signatures:"Valério Monteiro-Neto, Lídio G. Lima-Neto, Afonso G. Abreu and\nCinara Regina A. V. Monteiro",downloadPdfUrl:"/chapter/pdf-download/55756",previewPdfUrl:"/chapter/pdf-preview/55756",authors:[{id:"200503",title:"Ph.D.",name:"Valério",surname:"Monteiro-Neto",slug:"valerio-monteiro-neto",fullName:"Valério Monteiro-Neto"},{id:"205884",title:"Dr.",name:"Lídio",surname:"Lima-Neto",slug:"lidio-lima-neto",fullName:"Lídio Lima-Neto"},{id:"205885",title:"Dr.",name:"Afonso",surname:"Abreu",slug:"afonso-abreu",fullName:"Afonso Abreu"},{id:"205886",title:"BSc.",name:"Cinara Regina",surname:"Monteiro",slug:"cinara-regina-monteiro",fullName:"Cinara Regina Monteiro"}],corrections:null},{id:"56280",title:"Advancing in the Direction of Right Solutions: Treating Multidrug-Resistant Pneumonia",doi:"10.5772/intechopen.69979",slug:"advancing-in-the-direction-of-right-solutions-treating-multidrug-resistant-pneumonia",totalDownloads:1420,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Worldwide, antibiotic resistance is a major contemporary public health threat due to rapid emergence of resistant bacteria and endangering the efficacy of antibiotics. There are significant number of reports on clinical failure of β-lactam and β-lactamase inhibitor combination and even carbapenems due to various carbapenem resistance mechanisms. The increasing rate of the antibiotic resistance and its impact on treatment failure encouraged us to study newly reported concept of antibiotic adjuvant entities (AAEs) by which the increasing failure rate of antibiotics can be controlled. These AAEs have been developed for both Gram-positive and Gram-negative multidrug-resistant (MDR) infections. Elores (ceftriaxone + sulbactam with adjuvant ethylenediaminetetraacetic acid (EDTA)) and Potentox (cefepime + amikacin with adjuvant potassium chloride) are the AAEs for Gram-negative MDR pathogens each catering to a different type of resistance and Vancoplus (ceftriaxone + vancomycin with adjuvant L-arginine), another AAE, can help us to last longer in the war against antibiotic-resistant Gram-positive bugs particularly which cause complicated lower respiratory tract infection (LRTI) leading to pneumonia. These new antibiotic additions (Elores, Potentox, and Vancoplus) to the current armamentarium to treat MDR infections, including pneumonia, can help us combat against antimicrobial resistance more efficiently.",signatures:"Manu Chaudhary, Gazalla Ayub and Anurag Payasi",downloadPdfUrl:"/chapter/pdf-download/56280",previewPdfUrl:"/chapter/pdf-preview/56280",authors:[{id:"203391",title:"Dr.",name:"Anurag",surname:"Payasi",slug:"anurag-payasi",fullName:"Anurag Payasi"},{id:"205371",title:"Dr.",name:"Gazalla",surname:"Ayub",slug:"gazalla-ayub",fullName:"Gazalla Ayub"},{id:"205372",title:"Dr.",name:"Manu",surname:"Chaudhary",slug:"manu-chaudhary",fullName:"Manu Chaudhary"}],corrections:null},{id:"57188",title:"Interstitial Pneumonia Associated with Connective Tissue Disease: An Overview and an Insight",doi:"10.5772/intechopen.70864",slug:"interstitial-pneumonia-associated-with-connective-tissue-disease-an-overview-and-an-insight",totalDownloads:1838,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Interstitial pneumonia (IP) refers to involvement of the lung parenchyma by varying degrees of inflammation and fibrosis, in contrast to airspace disease typically seen in bacterial pneumonia. IP lies in the center of a heterogenous group of diffuse interstitial lung diseases (ILDs), either idiopathic or linked to underlying disorders. One of the major categories of disorders frequently associated with IP is a connective tissue disease (CTD), in which autoimmune-mediated tissue injury leads to multiple organ impairment. Today, IP represents the most critical pulmonary complication in CTD, resulting in significant morbidity and mortality. Despite growing understanding of the pathology of IPs, as well as the accumulating knowledge from both basic and clinical studies of CTDs, the pathogenesis of CTD-associated IP remains unclear. This chapter will provide an overview of the general understanding of ILD and illustrate the current state of knowledge on IP associated with CTD, in order to fully comprehend the entirety of its complex pictures. Moreover, we will propose a new insight into the immune pathogenesis of CTD-IP by presenting evidence which robustly indicates that T cells trigger initial development of IP in polymyositis/dermatomyositis, suggesting potential approaches for controlling such particular T cells in therapeutic interventions for IP.",signatures:"Akira Takeda and Yoshiki Ishii",downloadPdfUrl:"/chapter/pdf-download/57188",previewPdfUrl:"/chapter/pdf-preview/57188",authors:[{id:"202405",title:"Dr.",name:"Akira",surname:"Takeda",slug:"akira-takeda",fullName:"Akira Takeda"}],corrections:null},{id:"57668",title:"Pneumonia of Viral Etiologies",doi:"10.5772/intechopen.71608",slug:"pneumonia-of-viral-etiologies",totalDownloads:3208,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Pneumonia is a common illness that continues to cause significant morbidity and mortality in both adults and children. Bacteria such as Streptococcus pneumonia, Staphylococcus aureus and Haemophilus influenzae are generally considered as the main pathogens in community-acquired pneumonia and Legionella species, Chlamydia pneumoniae and Mycoplasma pneumonia in atypical pneumonias. In contrast the proportion of pneumonias due to viruses has been both difficult to detect and quantify with any precision. However, with the advent of powerful molecular techniques and rapidly developing technologies a greater number of viruses are being implicated as pathogens and co-pathogens in pneumonia. In the case of adults, the most commonly detected viruses are influenza virus, RSV and parainfluenza. Other viruses that have recently received considerable attention, are H5N1 influenza virus and coronaviruses. Infectious causes of pneumonia in immunocompromised patients include measles, HSV, CMV, HHV-6 and Influenza viruses. Pneumonias caused by other viruses are more rarely reported and include outbreaks of rhinovirus, adenovirus (particularly serotype 14 in military institutions), coronavirus, and metapneumovirus. A range of promising therapeutic targets have been identified and numerous innovative therapeutic treatments demonstrated to improve lung injury due to viral infections.",signatures:"Al Johani Sameera and Akhter Javed",downloadPdfUrl:"/chapter/pdf-download/57668",previewPdfUrl:"/chapter/pdf-preview/57668",authors:[{id:"76522",title:"Dr.",name:"Javed",surname:"Akhter",slug:"javed-akhter",fullName:"Javed Akhter"},{id:"80162",title:"Dr.",name:"Sameera",surname:"Al Johani",slug:"sameera-al-johani",fullName:"Sameera Al Johani"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"956",title:"Cystic Fibrosis",subtitle:"Renewed Hopes Through Research",isOpenForSubmission:!1,hash:"703f0969078948d82535b7b0c08ab613",slug:"cystic-fibrosis-renewed-hopes-through-research",bookSignature:"Dinesh Sriramulu",coverURL:"https://cdn.intechopen.com/books/images_new/956.jpg",editedByType:"Edited by",editors:[{id:"91317",title:"Dr.",name:"Dinesh",surname:"Sriramulu",slug:"dinesh-sriramulu",fullName:"Dinesh Sriramulu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"969",title:"Lung Diseases",subtitle:"Selected State of the Art Reviews",isOpenForSubmission:!1,hash:"b4344208b8b993d83e0131d23db46343",slug:"lung-diseases-selected-state-of-the-art-reviews",bookSignature:"Elvis Malcolm Irusen",coverURL:"https://cdn.intechopen.com/books/images_new/969.jpg",editedByType:"Edited by",editors:[{id:"87213",title:"Prof.",name:"Elvis",surname:"Irusen",slug:"elvis-irusen",fullName:"Elvis Irusen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"648",title:"Chronic Obstructive Pulmonary Disease",subtitle:"Current Concepts and Practice",isOpenForSubmission:!1,hash:"d52ddc19c473a70b91e5a64f41760a04",slug:"chronic-obstructive-pulmonary-disease-current-concepts-and-practice",bookSignature:"Kian-Chung Ong",coverURL:"https://cdn.intechopen.com/books/images_new/648.jpg",editedByType:"Edited by",editors:[{id:"103585",title:"Dr.",name:"Kian Chung",surname:"Ong",slug:"kian-chung-ong",fullName:"Kian Chung Ong"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5444",title:"Hypoxia and Human Diseases",subtitle:null,isOpenForSubmission:!1,hash:"331b1aa8d399bc404988a8bc5e431582",slug:"hypoxia-and-human-diseases",bookSignature:"Jing Zheng and Chi Zhou",coverURL:"https://cdn.intechopen.com/books/images_new/5444.jpg",editedByType:"Edited by",editors:[{id:"89898",title:"Dr.",name:"Jing",surname:"Zheng",slug:"jing-zheng",fullName:"Jing Zheng"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3843",title:"Lung Inflammation",subtitle:null,isOpenForSubmission:!1,hash:"92938e8752fa3444849d88b776cd7892",slug:"lung-inflammation",bookSignature:"Kian Chung Ong",coverURL:"https://cdn.intechopen.com/books/images_new/3843.jpg",editedByType:"Edited by",editors:[{id:"103585",title:"Dr.",name:"Kian Chung",surname:"Ong",slug:"kian-chung-ong",fullName:"Kian Chung Ong"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3309",title:"Respiratory Disease and Infection",subtitle:"A New Insight",isOpenForSubmission:!1,hash:"2e85d47bf0576f1c2ccf642156ccbda2",slug:"respiratory-disease-and-infection-a-new-insight",bookSignature:"Bassam H. 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Systems",subtitle:null,reviewType:"peer-reviewed",abstract:"\r\n\tCoordinating, orchestrating, and scheduling tasks has been an art practiced by governments, companies, and managers for ages. The rise of Taylorism, standardisation, electrical systems, electronic systems and computing, and now, quantum computing, has given scheduling a whole World of importance.
\r\n\tFrom practice to a mathematical and technological application, scheduling has become another form of art: an algorithmic art, declined in as many OS and hardware constraints, from embedded systems onboard an aircraft or a spacecraft to databases in all financial and Internet servers.
\r\n\tThey have become ubiquitous so that a large part of our civilisational development is supported by their reliability, redundancy, and optimisation capacity. Like all of our civilisational assets, they are benefiting from scientific breakthrough in computational sciences such as evolutionary algorithms, Artificial Intelligence, and quantum computing. If not by using it, by being in need of adapting to the next generation of computing. Space development is also bringing new challenges, especially in redundancy and reliability.
Major issues inhibiting successful patient recovery in intensive care units (ICUs) are the frequent occurrence of clinical alarms and the harsh, shrill noises that generally characterize these sounds. Alarms sound frequently to alert clinicians of physiological aberrancy that exceeds a threshold, yet many alarms have low-positive predictive value [1]. As stated by Edworthy and colleagues, multiparameter auditory warnings can be combined to create varying degrees of urgency [2]. Although the implementation of these results has proven useful to alert clinicians of possible danger, the potential negative consequences from the piercing alarm sounds were not considered from the patient perspective. While clinicians can suffer from alarm fatigue and desensitization, in this project, the patient-specific consequences are of the utmost concern, as patients commonly experience sleep deprivation, post-traumatic stress disorder (PTSD) anchored to critical illness, and delirium after a stay in the ICU [3]. Despite surviving an ICU stay, 88% of ICU patients experience hallucinatory/delusional intrusive memories related to ICU care for up to 8 months after hospital discharge [3], and the incidence of cognitive impairment as a function of ICU stay increases from 6 to 25% of patients [4].
\nWhile the underlying causes of these neuropsychological outcomes are not determined, the frequent, loud noises produced by clinical alarms often disturb patients’ sleep patterns and sound for extended lengths of time with no explanation to the patient, the reason behind the alarm. Compared to other high-consequence industries, health care suffers from poor positive predictive value alarms, as 67.2% of the alarms of the ICUs are false positives [5].
\nOur approach of sheltering the patients from alarms is accomplished by the creation of a wearable frequency-selective silencing device which silences the frequencies corresponding to the alarm noises (primarily patient monitor red/crisis alarm) and will allow the passage of all normal sounds (speech and other environmental stimuli), while maintaining their quality to reduce the likelihood of delirium.
\nResearch is still ongoing to determine the specific sound exposure level of sound and the impact on neuropsychological outcomes in the ICU; specifically, the fractionation of sound into alarm and nonalarm contributions and the psychoacoustic features of sound (e.g., roughness, sharpness, and amplitude envelope) that may be deleterious to the patient. In the case of passive noise cancellation, sound source localization from a point different from the patient’s ears could lead to spatial disorientation. Within the DSM-5 types of PTSD, there is a dissociative subtype of PTSD that is defined by symptoms of derealization and depersonalization [6]. The depersonalization experience could be an “out-of-body” experience, which could exacerbate the PTSD symptomatology. In an effort to not solve one problem and make manifold problems in the process, our approach and design will build from a single microphone passive cancellation process to a microphone array active cancellation process as described below.
\nFor patient-specific needs, the wearable technology must be user-friendly and comfortable to allow for continuous patient wear, especially while the patient is asleep. It must block alarm sounds while allowing the passage of all other environmental noise, such as speech and TV sounds. It is important to note that overstimulation of the auditory sense as well as a complete lack of stimulation of the auditory sense can contribute to PTSD and delirium, which is why noise-canceling headphones and/or simple earplugs that dampen all environmental noise entirely are
Although patients can have full degree-of-freedom head movement, patients and clinicians may be concerned that wearing headphones or earbuds would be uncomfortable to wear for prolonged periods of time; thus, future iterations of our design will incorporate the work by Voix and colleagues to develop comfortable wearable devices [7]. With wearable devices, there is additional concern of microphone placement and sound localization. With respect to the concern of the microphone being overly sensitive and amplifying environmental sounds that would be otherwise filtered by the human ear, we attenuated this difference by the application of an Audio-Technica (Tokyo, Japan) AT8131 windscreen. An additional concern was the quality of the audio path and exacerbation of spatial disorientation. As an initial step, a single microphone was used. However, future iterations of our design will use a microphone array with digital signal processing (DSP) tools for the real-time synthesis of a 3D sound pressure field using Ambisonics technologies to achieve the spatialization of monophonic signal or the reconstruction of natural 3D recorded sound pressure fields as guided by the work of Gauthier and colleagues [8]. As the focus on a “ground-up” ICU design from a multisensory aspect flourishes, ICU rooms are made to be quieter and more anechoic. As that is achieved, wave field synthesis (WFS), an open-loop technology, can be explored in concordance with environmental design. Specifically, adaptive wave field synthesis, combining WFS and active control to reproduce the spatial character of natural hearing, will ameliorate concerns of patient dissociative subtypes of PTSD symptomatology [9].
\nTo remove the alarm sound, MATLAB (MathWorks, Natick MA) digital signal processing was utilized to initially implement and test our digital filters. A spectral analysis was performed on a single alarm sound to obtain its frequency components. Then, an Infinite Impulse Response (IIR) Elliptic bandstop filter was created to block the frequency that specifically dominated in the spectral analysis. The width of the stopband had to be optimized so that the alarm component was completely blocked, yet the effect on environmental noise was minimized. This led to the creation of filters targeting the common red/patient crisis alarm with the most important ones focused at 960, 1920, 2880 and 3840 Hz.
\nThe dynamic digital filter was then generated in Simulink (MathWorks, Natick MA) using the filter specifications determined in MATLAB. The design is two-fold in that it contains both a detector and a series of filters. The detector continuously processes all incoming environmental sounds and determines the power present in the unfiltered environmental noise as compared to the power present in the filtered version. If this difference exceeds a predetermined threshold, this serves to indicate that an alarm is present in the environment. If the alarm sound is detected, the detector switches on the digital filter, and the filtered version of the noise is passed to the patient. This switching mechanism is critical to the design as it ensures that unnecessary processing and potential distortion will not occur for the patient if no alarms are sounding in the environment.
\nWhen auditory filtering is used, there is a concern of inadvertently filtering desired auditory stimuli. This is especially important when one needs to respond to the auditory stimulus. A relatively understudied source of response failures deals with simultaneous masking, a condition where concurrent sounds interact in ways that make one or more imperceptible due to physical limitations on perception. Bolton and colleagues have developed a novel combination of psychophysical modeling and formal verification with model checking to detect masking in a modeled configuration of medical alarms. This builds on previous work by adding the ability to detect additive masking while concurrently improving method usability and scalability [10]. The psychoacoustics used to describe masking represent frequency on the Bark scale, which maps a frequency (in Hz) to a location on the basilar membrane where the sound stimulates the receptors the strongest. Frequency to Bark conversion is calculated as
The hardware portion of this device continuously completes the digital filtering task during the device’s operation. To do this, the Simulink code for the detector and filter has been uploaded onto a Raspberry Pi (Raspberry Pi Foundation, Cambridge, UK) to allow for alarm filtration. A microphone connected to the Raspberry Pi obtains and passes the environmental sound to the digital detector (Figure 1).
\nDepiction of design prototype.
To prove objectively that the device accomplished our aims, an experiment was performed to prove that the frequency components specific to the alarms were missing from the filtered sound. In the initial stages of the project, a Fast Fourier Transform (FFT) was performed using MATLAB on the unfiltered alarm sound sample and the filtered alarm in order to compare the magnitudes of the frequency components present between the two sounds (Figures 2 and 3).
\nFFT of a single unfiltered alarm (left) and the same alarm filtered by a series of bandstop filters (Note: The Y-axis values are different to display the present spectral waveform after filtering).
Alarm filtered by a series of bandstop filters in Simulink (Note: The Y-axis values are different to display the present spectral waveform after filtering).
In the objective testing using MATLAB, the series of bandstop filters created on MATLAB dampened the magnitudes of the frequencies present in the alarm in the order of 103, as seen in Figure 2.
\nOnce the filtering on MATLAB proved successful, Simulink (Mathworks) was used to compile the software and deploy the data onto a Raspberry Pi device. By inputting a file (.wave) with both the alarm sound and environmental noise present, it was proven that the Simulink software was able to successfully filter the alarm frequencies as shown in Figure 3.
\nAs of now, this device relies on the use of noise-canceling headphones to transmit the filtered sound to the patient. Future designs will incorporate a wireless, in-ear device that can perform all the necessary filtering functions and transmission of the filtered sound in the device itself. With an aggressive goal to reduce cost and time of development, our first model uses passive filtering; however, further developments will incorporate active noise-cancellation which will obviate the need for the passive device to activate (~100–300 ms) and avoid a slight perceived auditory click of activation. With evolving design, this technology-centered initial approach will require FDA exemption status, so it can be studied in the clinical environment.
\nAudible medical alarms are the cause of a number of hazards in hospital and ICU settings. Their shrill acoustic features and the frequency at which they alarm (both in sheer number and frequency spectrum) are responsible for a number of negative consequences, especially for patients. Patients can experience PTSD and delirium secondary to sleep disturbance from alarms and health care providers’ divided and diminished attentional resources allocated to alarms. This frequency-selective silencing device was created to alleviate these problems and create a more comfortable environment for the patients during their length of stay in the ICU and promote patient safety.
\nWe would like to thank the Departments of Anesthesiology and Hearing and Speech Sciences at Vanderbilt University, particularly Dr. Ben Hornsby. We would also like to thank the Departments of Electrical Engineering and Computer Science at Vanderbilt University, especially Dr. A. B. Bonds, Dr. Dean Wilkes, and Garrett Hoffman for their assistance. We would also like to acknowledge Dr. Matthew Walker III and the Department of Biomedical Engineering at Vanderbilt University for their support.
\nAs one of the most significant topics in computer vision and pattern recognition, face recognition attains much attention from both academic and industries over recent decades [1, 2]. With the evolution of neural networks, general face recognition technology emerged as a noteworthy area among researchers [3, 4, 5]. However, identifying face images across widespread range of ages is shortcoming due to human face appearance changes affected by aging process [6, 7]. In order to achieve human face recognition under difference ages, Age-Invariant Face Recognition (AIFR) approach is progressed [8]. AIFR recognizes faces using facial features extracted from human images. AIFR method uses three different models such as generative, discriminative [9], and deep learning methods [10]. Generative approaches are based on the age progression methods in regard to converting the probe image into the same age as that of gallery image [11]. However, generative schemes have several shortcomings [12]. Optimizing the recognition performance in generative model is not easier task. Estimating the accurate results in generative model is highly difficult since it cannot handle aging impact. Discriminative approaches [13] are introduced to resolve discrepancy of generative scheme [14]. It develops feature matching using local descriptors [15] in AIFR. Multiple descriptors-based AIFR is introduced to extract features from periocular region [16]. In this, two descriptors are used to extract features that are Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF).
In order to achieve better result in AIFR, deep learning method is integrated with discriminative approach [17]. In deep learning, Convolutional Neural Network (CNN) algorithm plays vital role in recognizing face with different aging images [18]. Large age gap verification is performed by injecting features in deep networks [19]. Here, deep CNN is used to recognize face where texture features are considered. Aging model-based face recognition with different aging images is introduced under deep learning method [20]. Here, CNN descriptor is utilized to match image with different aging images.
From the aforesaid studies, we determine that there are still many issues present in recognizing face with aging progress. The issues are discussed as follows:
Preprocessing is not effective in most of the chapter that reduces performance of the system.
Pose normalization is not considered in existing AIFR, which is highly significant. Since, AIFR datasets such as MORPH, FG-NET, etc., contain different pose images.
Existing feature extraction procedures lack in extracting features from important regions that tend to reduce recognition rate.
Face recognition algorithms are not up to the level to handle large dataset and thus reduce the result of accuracy.
These problems impose confines on the present AIFR systems and also complicate the recognition and retrieval task especially under different aging images.
In order to tackle abovementioned issues, our work contributes the following processes:
In order to reduce time wastages in preprocessing, we initially execute novel Image Quality Evaluation (IQE) method, which estimates Image Quality Metric (IQM) for each image. If IQM value is below Image Quality Threshold (IQT), then only preprocessing is performed for that image or else directly gone into the pose normalization process.
Preprocessing is performed to reduce uncertainties in upcoming face recognition processes such as feature extraction, recognition, and retrieval. For this purpose, we implement two processes such as illumination normalization and noise removal. Illumination normalization adopts DGC-CLAHE and noise removal adopts ASBF algorithm.
Pose normalization is significant to diminish difficulties present in feature extraction and thus enhances the recognition and retrieval performance.
Our work extracts feature from three regions that are periocular, nose, and mouth in order to increase recognition rate. Here, two descriptors are utilized that are CNN and SIHKS, which perform better than other existing descriptors such as LBP, SIFT, etc.
In order to reduce recognition and retrieval time, we fuse features after extraction using CCA.
Recognition and retrieval are performed through SVM algorithm, which performs well even with unstructured, semistructured data such as text, images, and trees.
Outline of this chapter is summarized as follows: Section 2 deliberates state-of-the-art works existing in AIFR with their limitations. Section 3 exemplifies problems occurring in previous works related to AIFR. Section 4 explains brief study of our proposed work with our proposed algorithms. Section 5 illustrates numerical results obtain from our simulation environment and also compares it with existing methods. Finally, section 6 concludes our contribution and also provides comment on our future work.
This section discusses the state-of-the-art work related to AIFR along with their limitations. In this, we discussed works that comprise preprocessing, feature extraction, recognition, and retrieval processes.
Kishore et al. [21] have suggested Periocular Region-Based AIFR Using Local Binary Pattern. In this, three sequential processes are executed to recognize faces that are preprocessing, feature extraction, and classification. In preprocessing, enhancement and denoising processes are employed in each facial image. Local Binary Pattern (LBP) descriptor [22] was used to extract features from the periocular region [23] of the given face image. Periocular region contains eyes, eye lashes, and eye brow parts of the face. Chi-square distance was used as classifier to recognize face after feature extraction. Chi-square distance doesn’t recognize face accurately since it is highly sensitive to the sample size.
Nanni et al. [24] have introduced ensemble of texture descriptor and preprocessing techniques to recognize image effectually. Four face recognition processes are performed that are preprocessing, feature extraction, feature transform, and classification. Preprocessing executes three techniques that are adaptive single index retinex (AR) in order to enhance scene detail and color enhancement in darker area. Anisotropic smoothing and different of Gaussian (DoG) are algorithms executed to normalize the illumination field. Features are extracted using two descriptors that are Patterns of the Oriented Edge Magnitudes (POEM) and the Monogenic Binary Coding (MBC). At last, different distance functions are used to recognize face. Accuracy of face recognition was very less due to poor feature extraction mechanism. Chi et al. [25] have offered temporal nonvolume preserving approach to facial age progression and AIFR. In preprocessing, face region was detected and aligned based on the fixed position of the eyes and mouth corners. And then it maps the texture features of the test image with the trained image in order to verify images. Here, deep CNN algorithm was utilized to map features. In this, preprocessing step doesn’t perform effective processes such as normalization, noise removal that tend to reduce system performance.
Bor et al. [26] have introduced Cross Age Reference Coding (CARC) for AIFR. Initially, it executes face detection algorithm in order to detect face region in image. And it extracts features from the detected region for which it utilizes high-dimensional LBP algorithm. LBP extracts 59 local features from the detection regions. In this, Principal Component Analysis (PCA) algorithm was used to reduce dimensionality of extracted feature. After that, CARC recognizes face using local features transformation. More analysis is required on feature extraction since it plays vital role in AIFR. Yali et al. [27] have pointed out distance metric optimization driven CNN for AIFR. Here, two models are integrated that are feature learning and distance metric learning. This integration is achieved through CNN algorithm with parameters optimized using network propagation algorithm. CNN learns features using the convolution layer and recognizes face using the distance metric. Finally, recognized images are retrieved effectually. Herein, recognition rate was very less due to ineffective feature extraction.
Pournami et al. [28] have offered deep learning and multiclass SVM algorithm to recognize face. Here, preprocessing was performed to increase the accuracy of the face recognition where image resizing was performed. CNN feature descriptor was used to extract features from the given image. Here, fully connected layer extracts features from the image and then features are given as input to the multiclass SVM classifier. Resizing only performed in preprocessing thus introduced more noise in extracted feature. Garima et al. [29] have suggested techniques for face verification across different age progression with large age gap. Initially, image normalization was performed where RGB image was converted into the grayscale image and the image is rotate as the eyes are aligned horizontally. In this, face features are extracted using Center Symmetric Local binary Pattern (CSLBP) algorithm. And also weighted K-nearest Neighbor (K-NN) algorithm was used to recognize face from extracted features. K-NN doesn’t perform well for large dataset and thus reduces the accuracy of face recognition. Saroj et al. [30] have pointed out pyramid binary pattern for age-invariant face verification. In this, pyramid binary pattern was used to extract texture feature. Texture features are given as input to the PCA in order to reduce dimensionality of the extracted features. And then, classification was performed through SVM algorithm. Here, texture feature was only extracted to classify the face with age invariant. Thus it reduces accuracy in face recognition since dataset contains different images with large age gap.
Mrudula et al. [31] have offered face recognition across aging using GLBP features. Preprocessing performs three sequential processes that are image resizing, RGB to gray, and illumination normalization. Here, combined feature descriptor was used to extract features from the given image. LBP and Gabor descriptors are combined, which was known as GLBP descriptor. During classification, PCA was used to reduce feature dimensionality and K-NN algorithm was used to recognize face across aging. Herein, GLBP descriptor introduces high false-positive rate in age-invariant face recognition. Zhen et al. [32] have pointed out local polynomial contrast binary patterns for face recognition. Polynomial filters are used to extract the attributes from the given image. In this, LBP descriptor was used to extract texture from the given image. Fisher Linear Discriminant (FLD) algorithm is used to reduce dimension of extracted features. Here, extracted features are classified using nearest neighbor classifier to recognize given image in training set. Nearest neighbor classifier consumes more time to classify image since all the work is performed in testing stages only.
Mohanraj et al. [33] have suggested ensemble of CNN for face recognition in order to resolve aging, pose variation, and low-resolution problem. Preprocessing was established to resize the given image. After that, features are extracted using three different CNN algorithms. Features are concatenated and given to the classifier in order to predict the person. Here, random forest classifier is used to recognize the face. Noise removal was not performed in preprocessing and thus reduces the accuracy of face recognition. Rupali et al. [34] have introduced component-based face recognition. Here, three face components are considered that are nose, lips, and ears. Preprocessing is performed to resize the image and features are extracted using CNN algorithm. Features are extracted from nose and face regions that are given to the FLD algorithm to reduce the dimensions. These features are given to KNN classifier in order to predict the image. In KNN, initial K value prediction is complex that leads to ineffective results. Venkata et al. [35] have pointed out real-time face recognition using deep learning and LBP. During preprocessing, it resizes the given image. In this, LBP was used to extract features from the given images. Extracted features are given to the CNN in order to provide weight to each feature. CNN provides weight in order to estimate the matched face with the training images. Here, texture feature only extracted to recognize face across aging that tends to reduce recognition rate.
Mohsen et al. [36] have offered age-based human face image retrieval using zernike moments. In this, Zernike moment was used to extract features from the images. Here, Zernike moment utilizes Zernike Basis Function (ZBF), which captures both local and global featured fro face image. And, Multi-Layer Perceptron (MLP) algorithm was used to recognize age in training image. Accurate result was not obtained in MLP classifier, thus reducing the recognition rate. Danbei et al. [37] have offered face aging synthesis application based on feature fusion. Initially, face detection was performed and feature points are positioned. For this purpose, triangulation and affine transformations are used, which position the feature points. Here, facial texture features are extracted to recognize face across aging. Extracted features are fused in order to recognize face with the training images effectually. More analysis is required on facial recognition since it describes up to feature fusion process.
Kishore et al. [38] have offered Hybrid Local Descriptor (HLD) and LDA-assisted K-Nearest Neighbor classification in AIFR. Here, Gaussian filter was used to reduce noise that results in information degradation, since it removes fine details of the image and resultant image is blurred. WLD-based feature extraction loses more information due to lack of pixel consideration. K-NN-based classification requires more time due to absence of training phase and finding good similarity measure is also difficult. Muhammad et al. [39] have introduced Demographic Features (DF)-assisted AIFR and retrieval. In this, feature extraction takes more time, since each feature was extracted in three individual CNNs. Position and orientation of the object were ignored in hidden layer of CNN that result in less accuracy in feature extraction and recognition. Chenfei et al. [40] have pointed out Coupled Auto Encoder (CAN) algorithm based feature extraction in AIFR. Herein, feature extraction was not effective due to lack of texture and shape-oriented features. In CAN, data relationships are not considered that affect classification results and weight computation is also very difficult. Huiling et al. [41] have introduced Identity Inference Model (IIM)-based age subspace learning to recognize image in AIFR. Herein, wLBP-based feature extraction was used that results in less accuracy, since it contains more noise in extracted features due to absence of noise removal process. Fahad et al. [42] have introduced Composite Temporal Spatio (CTS) modeling in order to recognize image in AIFR. Here, preprocessing was required to improve the accuracy in age-invariant face recognition, since image database contains illumination, pose variation, etc. Naïve Bayes–based classification results are always biased one, since it doesn’t rely on class conditional dependency.
This section briefly describes our proposed method in detail along with the description of utilized algorithm.
Our Multi-Feature-assisted AIFR (MF-AIFR) method tackles problems that are present in the previous AIFR works. For this purpose, MF-AIFR establishes the five consecutive processes that are IQE, Preprocessing, Pose Normalization, Feature Extraction and Fusion, Feature Recognition and Retrieval as depicted in Figure 1. Our work novelty is present in the IQE method, since previous AIFR method doesn’t concentrate on the quality evaluation. In order to save time, MF-AIFR performs IQE where images that are not satisfied IQT only given to the preprocessing step or else it is directly given to the pose normalization process. During preprocessing, MF-AIFR performs two processes that are illumination normalization using DGC-CLAHE and noise removal using ASBF algorithm. Pose normalization is executed to enhance feature extraction performance where EA-AT algorithm is utilized. Multiple features are extracted from the three different regions of face image that are periocular, mouth, and nose in regard to enhancing accuracy result. Here, two descriptors are executed that are CNN for texture feature and SIHKS for demographic and shape features extraction. Here, demographic features comprise age, gender, and race. Extracted features are fused using CCA in accord to reduce the complex recognition process. For recognition and retrieval, MF-AIFR pursues SVM algorithm, which has high scalability compared with other machine learning algorithm.
Architecture for proposed work.
Figure 1 illustrates the architecture for our proposed work. The process depicted in architecture is described briefly in upcoming sections.
Reducing computation time in AIFR and retrieval is noteworthy in order to achieve efficient performance. For this purpose, MF-AIFR performs novel IQE, which estimates IQM for each image. IQM comprises subsequent metrics that are Brightness
These metrics are designated as follows:
Where
Where
Where
Where
Where
Where
Using above parameters, we estimate IQM for each image. It can be measured as follows:
After computing IQM, this value is compared with the IQT in order to select whether next process is preprocessing or pose normalization for given image.
Where
MF-AIFR performs preprocessing in order to enhance the recognition rate in simulation results. For this purpose, we perform two processes in preprocessing that are illumination normalization and Noise removal.
Illumination normalization is performed in order to enhance the image quality and also avoid negative effects of the image. MF-AIFR adopts DGC-CLAHE algorithm for illumination normalization. Proposed DGC-CLAHE performs better than existing CLAHE method. It enhances both luminance and contrast of the image adaptively. Our DGC-CLAHE algorithm performs dual gamma correction, which enhances the dark areas of the image. This algorithm adaptively sets the clip points of each image, which depends on the dynamic range of each block of the image. In this, first gamma correction is executed to boost the entire luminance present in the image block. Second gamma correction is executed to adjust the contrast in very dark region in order to avoid overenhancement in bright regions.
Initially, DGC-CLAHE sets clip point adaptively based on the dynamic range, which can be expressed as follows:
Where
DGC-CLAHE defines enhancement weight for the global gray levels of the blocks by first gamma correction (
Where
Where
Noise removal is substantial process in face recognition in regard to enhancing recognition accuracy. For this purpose, our MF-AIFR utilizes ASBF algorithm to remove noise from given image. Proposed ASBF algorithm preserves fine details of the image while removing noise and also sharpens the image. ASBF algorithm is used to remove universal noises such as impulse and Gaussian.
In ASBF algorithm, noisy pixel is detected using Sorted Quadrant Median Vector (SQMV), which incorporates significant features such as edge or texture information. Our ASBF algorithm executes three sequential processes as depicted in Figure 2. Initially, Adaptive Median Filter (AMF) is used to identify the corrupted pixels in the image. Secondly, the edge of the image is preserved using edge detector, which accurately predicts the edge existence in the current window. Noise detector is used to classify the noise into impulse and Gaussian. Switching Bilateral Filter (SBF) contains ranging filter, which switches the modes between impulse and Gaussian based on noise detector result.
ASBF function blocks.
Existing noise filtering algorithm utilizes constant window size such as 3*3, which may fail to distinguish noisy and noise-free pixel accurately and thus results in blur output image. In order to avoid this drawback, our AMF adaptively changes the window size based on the number of noisy pixels present in given image.
Noise detector is used to predict whether pixel is filtered by SBF Gaussian (
At last, pixel with Gaussian and impulse noises are classified based on the above discussed conditions. These outputs are given as input to the SBF with SQMV.
The output from the SBF filter is expressed as follows:
Where
From the above discussions, we conclude that our proposed ASBF removes not only Gaussian noise but also impulse noise while keeping the image fine details and images. This way of performing preprocessing increases the accuracy in AIFR.
Pose normalization is substantial process to increase accuracy in face recognition. Since, our database FG-NET contains different pose images and thus requires pose normalization before entering into feature extraction and retrieval. Our MF-AIFR carried out EA-AT algorithm in order to correct the different poses into the frontal view and thus increases the feature extraction efficiency. EA-AT algorithm initially estimates pose angle of given image using Euler Angle. Then, estimated angle is provided to the Affine Transformation to get frontal view of the given image. Euler angles are three angles in order to describe the orientation of the face with respect to the fixed coordinate.
Figure 3 illustrates the Euler angle with their coordinates in Z vector. Three angles are describes as follows: Yaw, Pitch, and Roll. In this, yaw angle (
Euler angles representation.
Where
Roll angle (
Pitch angle (
These three angles are given as input to the affine transformation algorithm in order to rotate into the correct view. There exist four basic affine transformations that are illustrated as follows:
Translate—It moves a set of point in fixed distance in x and y.
Scale—It scales the set of points in up or down directions.
Rotate—It rotates the set of points about the origin.
Shear—It offsets a set of points in distance proportional to their x and y coordinates.
In mathematical form, an affine transformation of
Where,
Where
Feature extraction and fusion are a major part of this work in order to produce optimum results in AIFR. Our MF-AIFR extracts multiple features from three set of regions. We extract images from three regions that are periocular, nose, and mouth. Since, these three regions are significant to recognize the image across aging. From these regions, we extract three type of features that are texture, shape, and demographic, which are briefed in Table 1. Here, texture feature is extracted using the CNN descriptor, and SIHKS descriptor is used to extract the shape and demographic-related features.
Features | Feature description | Types of features |
---|---|---|
Texture | Texture feature represents the surface characteristics of the image | Contrast, Dissimilarity, Entropy, Homogeneity, Correlation, and Angular Second Moment |
Shape | Shape features represents the physiological identity of given image | Boundary of the periocular, nose, and mouth regions, Convexity, and Solidity |
Demographic | Demographic features represent the individual uniqueness of the given image. | Race, Age, and gender |
Features description.
Our MF-AIFR utilizes CNN descriptor for texture feature extraction since it provides robust performance in learning features layer by layer. CNN applies multiple filters on the raw input image in order to extract high-level features. Here, we extract six texture features in given image such as contrast, dissimilarity, entropy, homogeneity, correlation, and angular second moment. These features are described as follows: In CNN, three different types of layers are present that are Convolutional layer, Polling layer, and Fully connected layer.
It gathers image from the input layer, which is made up of a set of learnable filters. In our work, convolutional layer comprises six filters in order to generate feature map. Six filters in the convolutional layer generate six feature maps. The feature map is the consequence of the every filter that convolved through whole image. Convolution operation can be described as follows:
Where
It is used to perform downsampling operation in order to reduce the spatial size of the convolutional layers. Polling operation is implemented on the pixel values captured by the pooling mask. The pooling operation is described as follows:
Where
Fully connected layer is used to extract the features that are obtained in the preceding layers. The results obtained in the last convolutional and pooling layer are given as input to the fully connected layer in order extract features.
Shape and demographic features are extracted using SIHKS algorithm. Shape features are boundary of the eye, nose and mouth, Convexity, and Solidity. Demographic features comprise age, race, and gender information. Here, race feature represents the skin tone of the face image. These features plays key role in recognizing face across aging.
Proposed SIHKS descriptor performs better than HKS algorithm since conventional method has drawback such as sensitivity to scale especially to the global scale. Hence, we proposed SIHKS algorithm, which performs better in scale invariance, and it is able perform at any point even at scale selection is impossible. In addition to it, it also performs well extracting shape and demographic-oriented features compared with other shape feature descriptor. SIHKS extracts features using three steps that are listed as follows:
Logarithmical sampling in time t. It can be expressed using below equation.
Where
Taking logarithm of heat signature with time variations. It can be described as the below equation,
Where
Taking discrete time Fourier transform of heat signature. It can be expressed as below equation,
With the above steps, our SIKHS estimates scale-invariant quantity
Figure 4 illustrates the texture feature extraction in CNN with their significant layers such as convolutional layer, pool layer, and fully connected layer.
Feature extraction in CNN.
Feature fusion is estimated to reduce extracted feature dimension of extracted features such as shape, texture, and demographic features. This dimensionality reduction will result in better performance in face recognition, which the process of recognition and retrieval is easier. For this purpose, our MF-AIFR algorithm utilizes CCA algorithm, which performs effectively in feature fusion. Feature fusion is defined as the combination of multiple feature vectors into single feature vector. Proposed CCA is a statistical tool for recognizing linear relationship among sets of features vectors in order to determine the inter subject covariances. Canonical covariates of the given feature vectors are obtained using below expression,
Where
Recognition and retrieval are final process in our MF-AIFR, which is performed by utilizing SVM algorithm. Here, we select SVM algorithm to correctly recognize the face cross aging and also retrieve the recognized image for given input image. Figure 5 illustrates the input and output space models of the SVM algorithm.
SVM input and feature space representation.
Proposed SVM algorithm performs well in even unstructured and semistructured data. It addition to it, SVM also scales relatively well to high dimensionality of database. SVM gets input as fused features from previous process obtained using CCA algorithm. SVM is the binary classification method that discovers the optimal linear decision surface based on the concept of structural risk minimization. The decision surface represents the weighted combination of the elements present in the training set. These elements are illustrated as the support vectors and characterize the boundary between two different classes. The output of the SVM algorithm is a set of support vectors
The linear surface is represented as follows:
Where k represents the weight factor and b represents the bias term and z represents the training or testing data. These two parameters are used separate the hyperplane position and orientation. The weight factor k is calculated using below expression,
Kernel function plays vital role in SVM, which classifies features effectually. In MF-AIFR, we use Radial Basis Function (RBF) kernel. RBF performs well compared with other kernel functions. It doesn’t require any prior knowledge about data. It can be expressed as follows:
Here,
To characterize the performance of the proposed MF-AIFR, this section is divided into four aspects such as dataset description, simulation setup, application scenario, results, and discussion.
This section deliberates dataset information used in this chapter. Here, we utilize FG-NET database to perform face recognition and retrieval. Face and gesture recognition NETwork (FG-NET) aging database was released in the year of 2004 in an attempt to support research activities regarding the changes in the facial appearance caused by aging. FG-NET database comprises 1002 images from 82 different subjects. Each subject comprises 6–18 images with the age ranging between the newborns to the 69-year-old subjects. Our FG-NET database contains considerable variations such as poses and illuminations.
Table 2 illustrates the details of the FG-NET dataset briefly. Dataset contains 34 male subjects and 48 female subjects’ images. Each subject has 1–12 images across their age progression.
Parameters | Values | # Images |
---|---|---|
# subjects | 82 | 1002 |
#Males | 34 | Max (1–12) per subject |
#Females | 48 | Max (1–12) per subject |
Dataset description.
Different age bands present in the FG-NET dataset are represented in Table 3. FG-NET dataset comprises subjects from the age of 0 to 69 years old.
Factors | Ages | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
0–5 | 6–10 | 11–15 | 16–20 | 21–25 | 26–30 | 31–35 | 36–40 | 41–45 | 46–69 | |
#Subjects | 75 | 70 | 71 | 68 | 46 | 38 | 30 | 24 | 19 | 10 |
#Images | 233 | 178 | 164 | 155 | 81 | 62 | 38 | 31 | 26 | 34 |
Different age bands of FG-NET dataset.
Our proposed MF-AIFR is implemented in MATLAB R2017b tool with C programming language. Our MATLABR2017b is executed in windows operating system. MATLAB is a multi-paradigm statistical computing environment developed by MathWorks. MATLAB permits matrix manipulations, implementation of algorithms plotting of functions and data, creation of user interfaces, and interfacing with programs written in other languages, which include C, C++, C#, JAVA, and Python.
To evaluate performance of the MF-AIFR, we consider following metrics that are described as follows:
Where
Where
This compares the simulation results of the MF-AIFR with existing methods such as HLD, DF, and CAN. Here, we compare results using six performance metrics that are Accuracy, Recall, Precision, Recognition Rate, Rank-1 Score, and F-Score. Table 4 illustrates the comparisons of previous methods with their strength, weakness, and research statements.
Accuracy metric is one of the significant metrics to evaluate the performance of the proposed work. This metric defines the how accurate our MF-AIFR in terms of correct classification of images. The performance of this metric is evaluated by alternating the number of images.
Figure 6 demonstrates that comparisons on accuracy of the MF-AIFR with respect to the existing methods such as CAN, DF, and HLD. These comparisons show that our MF-AIFR achieves better performance compared with the existing methods. Since, our method utilizes better feature descriptors such as CNN and SIHKS. Both algorithms extract features effectually from three regions that are periocular, nose, and mouth. This selected region plays a key role in recognizing face across aging. And CNN and SIHKS provide robust performance even in high-dimensional dataset. As a result, our method achieves high accuracy as 95%. By contrast, CAN and DF method attain less accuracy compared with our method due to its poor feature extraction procedures since it doesn’t concentrate on the vital regions such as periocular, nose, and mouth. Meanwhile, HLD obtains high accuracy compared with both CAN and DF method due to its feature extraction from periocular region, which plays significant role in face recognition across aging. Though, it achieves less accuracy compared with our method due to its poor descriptor algorithm since it loses large amount of information during feature extraction.
Comparisons on accuracy.
Table 5 illustrates the average simulation results comparison of accuracy with the existing and proposed methods.
Reference | Key concentration | Strength | Weakness | Research statements | |||||
---|---|---|---|---|---|---|---|---|---|
Accuracy | Recall | Precision | Recognition rate | F-Score | Rank 1-score | ||||
Kishore et al. [41] | HLD-AIFR & Retrieval | Adopts large datasets | It removes fine details of the image and resultant image is blurred. Feature extraction loses more information due to lack of pixel consideration. | Low | Medium | Low | Low | Medium | Low |
Muhammad et al. [42] | DF-AIFR & Retrieval | Better demographic Estimation | Takes more time in feature extraction | Very Low | Low | Low | Very Low | Low | Very Low |
Chenfei et al. [13] | CAN-AIFR | Complexity is less | Data relationships are not considered that affects the recognition results. | Low | Medium | Very Low | Low | Low | Low |
Huiling et al. [15] | IIM-AIFR | Flexible to large dataset | More noise in extracted features due to absence of noise removal | Medium | Low | Very Low | Medium | Very Low | Low |
Fahad et al. [9] | CTS-AIFR | Recognition time is less | Naïve Bayes based recognition results are always biased one, since it doesn’t rely on class conditional dependency. | Very Low | Medium | Very Low | Very Low | Very Low | Low |
Comparisons on previous methods in AIFR.
Methods | Accuracy (%) |
---|---|
HLD | 80.2 |
DF | 73.2 |
CAN | 67.6 |
MF-AIFR | 90.2 |
Accuracy comparisons [average].
From the above comparison, it is noticed that our method achieves better accuracy percentage as 90.2% compared with the existing methods.
Recall is used to evaluate the performance of the MF-AIFR in terms of the correct recognition of face image. Recall performance is evaluated by changing the number of images.
Figure 7 shows that our MF-AIFR achieves less recall percentage compared with other methods.
Comparisons on recall.
Since, our MF-AIFR correctly recognizes the face as per given test image, thus reduces false detection of face images. Reason for this is that our method executes pose normalization before entering into the feature extraction process. Pose normalization enhances the feature extraction efficiency. Thus it leads to correct identification and retrieval of the test image. As a result, our MF-AIFR achieves less recall percentages compared with existing methods. Whereas existing methods such as DF and CAN achieves high recall percentages due to lack of pose normalization and complex feature extraction procedures. In the meantime, HLD method reduces recall percentage compared with DF and CAN methods since it doesn’t follow complex feature extraction procedures. Still, recall of HLD is high compared with MF-AIFR due to lack of pose normalization and information degradation in noise removal process. Table 6 designates the average simulation results comparison of recall with the existing and proposed methods.
Methods | Recall (%) |
---|---|
HLD | 75.6 |
DF | 87 |
CAN | 80 |
MF-AIFR | 70 |
Recall comparisons [average].
From the above comparison results, it is seen that our MF-AIFR method achieves less recall percentage as 70% compared with the existing methods.
Precision is used to measure performance of our work in terms of relevance instances retrieved compared with the total images. Precision performance is measured via altering the number of image.
Figure 8 depicts that MF-AIFR achieves high precision percentages compared with existing methods. MF-AIFR performs preprocessing process before entering into the feature extraction and recognition process. Preprocessing performs illumination normalization and noise filtering since our FG-NET dataset contains illumination and noises in images. These two processes enhance the quality of the image that tends to easy the feature extraction and recognition process. CAN and DF methods achieves less precision due to lack of preprocessing such as noise removal and illumination normalization. Likewise, HLD also obtains less precision owing to fine detail removal in Gaussian-based noise filtering. Since Gaussian filter doesn’t concentrate on fine details of the image, which results in blur image.
Comparisons on precision.
Table 7 designates the average simulation results comparison of precision with the existing and proposed methods. From the above comparison, we conclude that MF-AIFR achieves better precision percentage as 90.6% compared with existing methods.
Methods | Precision (%) |
---|---|
HLD | 81.6 |
DF | 71.6 |
CAN | 65 |
MF-AIFR | 90.6 |
Precision comparisons [average].
F-Score metric considers both false positive and false negative values in account to estimate performance of this work. The performance of this metric is simulated by varying the number of images.
Figure 9 illustrates that comparison on F-Score result of MF-AIFR with existing methods such as DF, CAN, and HLD. From this figure, it is noticed that our method achieves high F-Score compared with existing methods. Our MF-AIFR uses two descriptors such as CNN and SIHKS to extract texture, shape, and demographic features. Here, SIHKS descriptor performs very well in scale invariance and also provides better extraction results even when scale selection is impossible. It extracts shape and demographic features effectually, which plays substantial role in face recognition across aging. At the same time, CAN and DF methods attain less F-Score owing to the absence of significant feature extraction such as texture and shape features. Meanwhile, HLD also attains less F-Score since it doesn’t concentrate on shape features extraction and thus reduces the face recognition and retrieval efficiency.
Comparisons on F-score.
Table 8 describes the average simulation results comparison of F-Score with the existing and proposed methods. From the above comparison, we observed that MF-AIFR method achieves high F-Score percentage as 87.2% compared with existing methods.
Methods | F-Score (%) |
---|---|
HLD | 78.6 |
DF | 71.6 |
CAN | 59.6 |
MF-AIFR | 87.2 |
F-score comparisons [average].
Recognition rate is used to measure the ability of MF-AIFR in terms of the face recognition. It can be measured through changing the number of features.
Figure 10 designates the comparisons on recognition rate of MF-AIFR with respect to the existing methods CAN, DF, and HLD methods. From this figure, it is observed that our MF-AIFR attains high recognition rate compared with existing method. We propose SVM algorithm for recognition and retrieval. It performs well in recognition even in high dimensionality of dataset. In addition to it, we also perform feature fusion before entering into the recognition and retrieval process.
Comparisons on recognition rate.
Feature fusion reduces the dimension of feature vectors and thus tends to enhance the performance of SVM algorithm. Therefore, our method achieves better recognition rate compared with existing method. Meanwhile, DF method has less recognition rate compared with other methods due to lack of effective recognition and retrieval processes since it simply ranks the images. Likewise, CAN also attains less recognition rate compared with our method since it isn’t able to establish data relationship between different features. Meantime, HLD method attains less recognition rate due to usage of KNN for recognition. KNN takes more time, and discovering similarity measure is tedious.
Table 9 defines the average simulation results comparison of recognition rate with the existing and proposed methods. Above comparison illustrates that recognition rate of MF-AIFR is higher than that of other existing methods.
Methods | Recognition rate (%) |
---|---|
HLD | 87 |
DF | 69.2 |
CAN | 79.2 |
MF-AIFR | 92.2 |
Recognition rate comparisons [average].
Rank-1 Score considers the performance of cumulative match for given images in proposed work. It represents the efficacy of our work in terms of recognition and retrieval.
Figure 11 exhibits comparisons on rank-1 score results with respect to the existing methods. From this figure, it is seen that our MF-AIFR attains high rank-1 score compared with the existing methods. Our proposed DGC-CLAHE algorithm based illumination normalization performs well compared with existing CLAHE; it enhances the fine details of the image. ASBF-based noise filtering also provides better performance in noise removal, which sharpens the image. This way of preprocessing results in high matching results in face recognition. At the same time, existing methods such as DF and CAN attain less rank 1 score since it doesn’t use effective algorithm for preprocessing and thus reduce the quality of given image drastically. Likewise, HLD also attains less rank 1 score compared with our method. Since, it doesn’t perform illumination normalization and noise filtering also not effective. From this analysis, we conclude that our MF-AIFR attains better results in rank 1-score compared with other methods.
Comparisons on rank 1-score.
Table 10 signifies average simulation results comparison of rank 1-score with the existing and proposed methods. From the above comparison, we prove that our MF-AIFR method achieves higher rank 1 score percentage as 89.8% compared with existing methods.
Methods | Rank 1 score |
---|---|
HLD | 79.6 |
DF | 73 |
CAN | 65 |
MF-AIFR | 89.8 |
Rank 1 score comparisons [average].
Performance of the computation time is evaluated by varying the number of images. This metric must be low in order to attain better performance in image retrieval across aging.
Figure 12 depicts the comparisons on computation time results with respect to the existing methods. It is noticed that our MF-AIFR method achieves less computation time compared with the existing methods such as CAN, DF, and HLD. MF-AIFR performs IQE process before entering into the preprocessing step. The images that are not satisfying IQT only undergone preprocessing; otherwise it is directly given to the pose normalization step. Thus it reduces the time wastages in performing preprocessing for all input images. In addition to it, our work also reduces time in feature extraction and classification by using effective algorithms such as CNN, SIHKS, and SVM. These algorithms require less time to process the given inputs. As a result, MF-AIFR achieves less computation time. In the meantime, existing methods such as CAN and DF attain high computation time compared with other methods. Since it performs preprocessing for all images and also doesn’t utilize effective algorithm to process the given input image and thus leads to increase in computation time. Likewise, HLD also attains high computation time compared with MF-AIFR since it performs preprocessing for all images regardless of their quality.
Comparisons on computation time.
Table 11 deliberates the comparisons of computation time and thus shows that our method attains less computation time as 12.4ms compared with other methods including HLD, DF, and CAN.
Methods | Computation time (ms) |
---|---|
HLD | 60 |
DF | 63 |
CAN | 73 |
MF-AIFR | 12.4 |
Computation time comparisons [average].
This section signifies highlights of this research regarding face recognition across aging. In order to achieve better performance in AIFR, our work establishes five consequent processes. Table 12 describes the benefits of proposed algorithms along with their functionalities. This table illustrates each algorithm with their benefits in performance metrics such as precision, recall, accuracy, recognition rate, and rank 1 score.
Algorithms | Main functionality | Benefits related to performance |
---|---|---|
DGC-CLAHE | Illumination Normalization | Enhances the recognition rate and accuracy |
ASBF | Noise removal | Enhances recognition rate and feature extraction efficiency |
EA-AT | Pose Normalization | Easier the feature extraction process and Increases the precision level |
CNN | Texture Feature Extraction | Enhances the accuracy in face recognition across aging and perform well in large scale data set |
SIHKS | Shape & Demographic Feature extraction | Increases the rank 1-score and adapts large scale data set. |
SVM | Recognition and Interval | Simple processing, increases the accuracy and reduces the recall |
Benefits of proposed algorithms.
Face recognition across aging becomes challenging due to changes in the human faces with age progressions. In order to address this bottleneck, this chapter proposes MF-AIFR method where four successive processes performed that are listed as follows: IQE is performed to reduce time spend in preprocessing and thus enhances performance of our system drastically. An image that doesn’t satisfy the IQT is given as input to the preprocessing step. Here, illumination normalization and noise removal are performed, which enhances the accuracy in face recognition and retrieval. Illumination normalization adopts DGC-CLAHE, and noise removal adopts ASBF algorithm. In order to normalize the pose, we adopt EA-AT algorithm, which is performed to enhance the feature extraction efficacy. Two types of descriptors are utilized for features extractions that are CNN and SIHKS. Here, we extract multiple features such as texture, shape, and demographic features. We extract features from three types of regions that are periocular, nose, and mouth. CNN extracts texture features, and SIHKS extracts shape and demographic features. This way extracting features increases our recognition rate. In recognition and retrieval, we execute SVM algorithm, which follows the simple procedure and provides better results. At last, we evaluate the performance of MF-AIFR system using seven metrics that are Accuracy, Recall, Precision, Rank-1 Score, F-Score, Recognition rate, and Computation time. Thus it shows that our work performs better than existing methods such as HLD, DF, and CAN.
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The proposed bridge design has a total span of 4440 m with two 330-m end spans and a central span of 3780 m. The height of the two pylons is 702 m, and the deck width is 40 m. The features of this structure include the combination of a suspension bridge and cable-stayed bridge, application of carbon fibre materials, extension of deck width and pretension techniques. Linear static analysis, dynamic analysis and theoretical analysis are conducted under different loading cases. In linear static analysis, the stresses under critical load combinations are smaller than the ultimate strength of the materials. However, the maximum deflection under the dead and wind load combination exceeds the specified serviceability limit.",book:{id:"6395",slug:"bridge-engineering",title:"Bridge Engineering",fullTitle:"Bridge Engineering"},signatures:"Faham Tahmasebinia, Samad Mohammad Ebrahimzadeh\nSepasgozar, Hannah Blum, Kakarla Raghava Reddy, Fernando\nAlonso-Marroquin, Qile Gao, Yang Hu, Xu Wang and Zhongzheng\nWang",authors:[{id:"211659",title:"Dr.",name:"Faham",middleName:null,surname:"Tahmasebinia",slug:"faham-tahmasebinia",fullName:"Faham Tahmasebinia"},{id:"221172",title:"Dr.",name:"Samad M.E.",middleName:null,surname:"Sepasgozar",slug:"samad-m.e.-sepasgozar",fullName:"Samad M.E. Sepasgozar"}]},{id:"61896",title:"Children’s Playgrounds in Slovak Mass Housing Estates: History and Current Trends",slug:"children-s-playgrounds-in-slovak-mass-housing-estates-history-and-current-trends",totalDownloads:1349,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Children’s playgrounds represent an important amenity in the concepts of mass housing, The study chapter presents the unique concepts of children’s playgrounds that have been applied in the Slovak mass housing estates of the second half of the twentieth century, designed by architects and artist, and inspired by the best European experiences, for example, by the landscape design of the Stockholm School. The early inhabitants of the Slovak mass housing estates were predominantly young families with children. The residential aging of this homogenous social structure caused that during the lifespan of housing estates, the demand for playgrounds decreased, they became underused and fell into decay. Today, the social structure of mass housing estates becomes more heterogeneous, what puts new requirements on the design of open public spaces and, as well as, on the regeneration and design of children’s playgrounds, to serve the rising demands of the inhabitants and to enhance the livability of the housing estates. The study examines the current examples of the children’s playgrounds from Slovak mass housing estates, which show that nowadays the typified design of the standardized catalog type elements is used and preferred.",book:{id:"7205",slug:"housing",title:"Housing",fullTitle:"Housing"},signatures:"Katarína Kristiánová",authors:[{id:"224853",title:"Dr.",name:"Katarina",middleName:null,surname:"Kristianova",slug:"katarina-kristianova",fullName:"Katarina Kristianova"}]},{id:"66232",title:"Geotechnical Engineering Applied on Earth and Rock-Fill Dams",slug:"geotechnical-engineering-applied-on-earth-and-rock-fill-dams",totalDownloads:2359,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter presents the importance of geotechnical engineering on the site selection, design, construction, operation, and maintenance of earth-rock dams and earth structures; it emphasizes the geotechnical engineering work related to dam safety during the operation stage. Preliminary geological studies required to select the best dam site are described first. Next, the field and laboratory studies related to the curtain design and dam foundation treatment, as well as geotechnical studies required for the construction, operation, and maintenance of the dam, are discussed. Recent developments in the following three areas are also included: (a) seismic considerations for the design, construction, and maintenance of earth dams; (b) importance of water flow control through the dam embankment and dam foundation, required to avoid internal soil erosion and excessive pore pressure; and (c) dam safety in Mexico and around the world. A case history of a recent failure is used for illustration purposes. In this example, design and construction shortcomings resulted in serious damages on an earth dam. Conclusions and recommendations related to this topic are presented at the end of this chapter.",book:{id:"7587",slug:"hydraulic-structures-theory-and-applications",title:"Hydraulic Structures",fullTitle:"Hydraulic Structures - Theory and Applications"},signatures:"Raúl Flores-Berrones and Norma Patricia López-Acosta",authors:[{id:"58505",title:"Dr.",name:"Raul",middleName:null,surname:"Flores-Berrones",slug:"raul-flores-berrones",fullName:"Raul Flores-Berrones"}]}],onlineFirstChaptersFilter:{topicId:"114",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"1082726",title:"Potential of Nonlinear Dynamics Tools in the Real-Time Monitoring of Large Dams: The Case of High Enguri Arc Dam",slug:null,totalDownloads:7,totalDimensionsCites:0,doi:"10.5992/intechopen.1000204",abstract:'Large dams are grand structures with a complex nonlinear dynamic behavior. These nonlinear effects, though relatively small, can be very important for the analysis of dam mesoscopic damage accumulation using available monitoring data, namely, the time series of strains/tilts of the dam structure in response to the periodic filling/draining process of the reservoir. The authors derive the characteristics of the unknown dynamics using the time series of tilts and strains of a structure by means of recurrence plots (RPs), recurrence quantification analysis (RQA), Lempel-Ziv complexity (LZC), mutual information (MI), detrended fluctuation analysis (DFA), and singular spectrum analysis (SSA) for studying dam dynamics. Anomalies in the nonlinear dynamics characteristics of the measured time series of the tilts/strains of dam during the reservoir regular filling/discharge process may signal the abnormal behavior of the object. These methods were used for the analysis of the monitoring data of the 271-m-high Enguri arch dam, still one of the highest (in its class) dams in the world, which was built in the canyon of Enguri river (West Georgia) in the 1970s. Since 1996, the European Centre “Geodynamical Hazards of High Dams” of the Council of Europe has been operating on Enguri dam. Since 2020, the International Project DAMAST: Dams and Seismicity has been going on in the Enguri dam area.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Tamaz Chelidze, Teimuraz Matcharashvili, Ekaterine Mepharidze, Levan Mebonia, Mirian Kalabegashvili and Nadezhda Dovgal"},{id:"1084630",title:"Analysing the Possibility of Failure of Cascade Dam System and a Case Study from Brazil",slug:null,totalDownloads:13,totalDimensionsCites:0,doi:"10.5992/intechopen.1000202",abstract:'A cascade dam system poses more hazards for downstream life and structures, when compared with a single dam located on a river. Therefore, there is a need to develop differentiated procedures to classify and regulate these dams. In the state of Mato Grosso (MT), Brazil, it is common to find multipurpose dams, which can be considered as a cascade, when a dam failure causes adverse effects in downstream dams. The objective of the study is to analyse the possibility of dam failure located in the cascade system operated by the municipality of Várzea Grande, MT by the Associated Potential Damage (APD) classification used throughout the country. In order to do this, the specification namely “Simplified Methodology to Define the Classification Flood Zone of Associated Potential Damage of a Dam” developed by the National Laboratory for Civil Engineering in Portugal (LNEC in Portuguese) was utilised. This specification was adapted by the National Water and Sanitation Agency (ANA in Portuguese) in Brazil. In the case study, there are three dams (Dam 1, Dam 2 and Dam 3) in the cascade system. Dam 1 can cause overtopping problem for Dam 2 and Dam 3. According to APD classification, dams considered for the study are categorised as “high dam”.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Angélica Luciana Barros de Campos, Ruben Jose Ramos Cardia and Welitom Ttatom Pereira da Silva"},{id:"1081439",title:"Recent Evaluation on Total Risk of Cascade Dams on Murat River of Upper Euphrates Basin, Turkey",slug:null,totalDownloads:25,totalDimensionsCites:0,doi:"10.5992/intechopen.1000206",abstract:'The dams within a cascade system pose a high total risk to the downstream life, even if they provide significant benefits in terms of flood protection, irrigation water, and domestic water supply and energy production; a dam in a cascade system also poses a substantial risk from the point of view of other structures in the basin and causes the danger to grow due to the triggering effect from the point of view of dam failure. In this study, the total hazard of the dams in the Murat River located in the upper part of the Euphrates-Tigris Basin, the largest basin in Turkey, will be evaluated, and calculations made about it will be summarized. The possible hazards in a cascade system will be highlighted. Ten large dams of various types ranging from 36 m to 138 m in height from the river basin have been considered in this context. The analysis results show that six dams are under near-source effect in terms of seismicity, and all of the dams considered have a high total risk, although they have different hazard ratios. In addition, three separate dams located within the cascade structure carry a much greater risk regarding the dangers that other structures may create.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Hasan Tosun"},{id:"1082008",title:"Uncertainty Factors Influencing Hydroelectric LCA Studies: A Review",slug:null,totalDownloads:6,totalDimensionsCites:0,doi:"10.5992/intechopen.1000185",abstract:'Despite the increase in research on Life Cycle Assessment (LCA) of Hydroelectric Power Plants (HPP) there are issues that need to be better discussed. This review aims to discuss factors that influence HPP LCAs such as: indirect emissions, different stages of HPPs (construction, operation, and decommissioning), scale/productivity of HPPs, types of projects (reservoir and run-of-river) and use of the ground. Most of the results obtained by HPP LCAs indicate that the construction phase is the most influential phase for indirect emissions due to the use of steel and concrete. The comparison of the HPP’s LCA results with the LCA of other energy sources indicates that for the analyzed category Global Warming Potential (GWP), the HPPs present a good environmental performance considering the quantified emissions, their productivity and useful life. The present review highlights some uncertainty factors that influence HPP LCA studies and cites the need to carry out future studies on the environmental impacts of HPPs including these factors.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Marla T.B. Geller and Anderson Alvarenga de Moura Meneses"},{id:"1084778",title:"Geomembranes in Dam Engineering",slug:null,totalDownloads:14,totalDimensionsCites:0,doi:"10.5992/intechopen.1000175",abstract:'Geomembrane systems are used to provide, enhance, or restore watertightness in dams since 1959. In new construction, they are installed on embankment dams, RCC dams, and cofferdams, while in rehabilitation they are used on all types of dams. They can be installed as a full-face liner, or to line parts of the dam where a higher risk of infiltration is expected, or as external water stop at peripheral and vertical joints and at contraction joints. They can be exposed to the water of the reservoir or be covered by a ballast layer; a watertight seal at all peripheries prevents water infiltration underneath the geomembrane liner. A geomembrane water barrier is a technically and cost-effective sustainable solution. The chapter discusses the design of the state-of-the-art solutions, the technical and economic advantages, installation aspects, performance, and references, with significant examples of all available options. A recent solution for underwater placement, developed for repair but applicable also in new construction, will be presented.
',book:{id:"11187",title:"Special Topics in Dam Engineering",coverURL:"https://cdn.intechopen.com/books/images_new/11187.jpg"},signatures:"Gabriella Vaschetti"},{id:"1083097",title:"Managing the Quality of the Impounded Water",slug:"tbc-29",totalDownloads:11,totalDimensionsCites:0,doi:"10.5992/intechopen.1000168",abstract:'Design, construction, and operation of a dam should involve planning and careful consideration not only of the foundation and mass of the dam itself but also of the proper management of the reservoir, and of communities displaced by the reservoir, and impacted in any way upstream or downstream. Many management problems involve a reservoir’s density stratification, resulting in low oxygen, phosphorus release, and hydrogen sulfide (H2S) in the lower layers. Control measures include selective withdrawal and artificial aeration. Case examples are given. Other problems introduced by damming are often best dealt with by measures slow and well-considered, as illustrated by examples. References for further study are provided.
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Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 12th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!0,editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. 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