\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"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"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7093",leadTitle:null,fullTitle:"Pneumothorax",title:"Pneumothorax",subtitle:null,reviewType:"peer-reviewed",abstract:"This book aims at reviewing contemporary publications on the subject of the pneumothorax. Specifically, the anatomy and physiology of the pneumothorax, the pathological classification into primary and secondary, the different methods of diagnosis, and the rationale behind the different approaches of management are discussed. Strategies for special circumstances are highlighted, such as the pneumothorax around menstrual cycles, during pregnancy, and before general anesthesia for other reasons, air travel, and scuba diving. Attention is drawn to the most contemporary trends of management and the evidence from recently published trials and reviews. A separate chapter is dedicated to controversies in the management of the pneumothorax. Trainees as well as established consultant thoracic surgeons, anesthetists, pulmonologists, pediatricians, obstetricians, and intensivists should find this book both interesting and provocative.",isbn:"978-1-83968-066-3",printIsbn:"978-1-83968-065-6",pdfIsbn:"978-1-83968-067-0",doi:"10.5772/intechopen.73885",price:100,priceEur:109,priceUsd:129,slug:"pneumothorax",numberOfPages:86,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"0b1fdb8bb0448f48c2f234753898f3f8",bookSignature:"Khalid Amer",publishedDate:"December 11th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7093.jpg",numberOfDownloads:5413,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:1,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:2,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 9th 2018",dateEndSecondStepPublish:"July 30th 2018",dateEndThirdStepPublish:"September 28th 2018",dateEndFourthStepPublish:"December 17th 2018",dateEndFifthStepPublish:"February 15th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"63412",title:"Dr.",name:"Khalid",middleName:null,surname:"Amer",slug:"khalid-amer",fullName:"Khalid Amer",profilePictureURL:"https://mts.intechopen.com/storage/users/63412/images/system/63412.jpg",biography:"Qualified from the University of Khartoum - Sudan. Specialised in General surgery by 1980 (MD, University of Khartoum). Moved to UK in 1992, fully trained cardiothoracic surgeon, trained in Cardiff. I worked briefly as a consultant cardiothoracic surgeon in Bristol royal infirmary and the University Hospital of Wales - Cardiff. I left heart surgery to specialise in lung and chest surgery. I have special interest in minimal access thoracic surgery (VATS) for lung cancer (lobectomy), thymectomy for myasthenia, mediastinal cysts and mediastinal tumours.",institutionString:"University Hospital Southampton",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Southampton Hospital",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:"68669",title:"Indications of Surgery in Pneumothorax",doi:"10.5772/intechopen.88640",slug:"indications-of-surgery-in-pneumothorax",totalDownloads:980,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Spontaneous pneumothorax (SP) is a type of collection of air in the pleural cavity that develops in the absence of trauma or iatrogenic cause. Its management has been a matter of debate for many decades. Nevertheless, clear guidelines from the American, British and European societies have been published. In this chapter, we will discuss the different society guidelines and the inter-guideline variations. We will also discuss the author’s perspective for management of first-time pneumothorax which is an unsettled issue between respiratory physicians and thoracic surgeons. Finally, deviation from clinical guidelines is usually associated with deficient patient care, and in this chapter, the reflection on patient care from not following the pneumothorax guidelines will be discussed in detail.",signatures:"Hany Hasan Elsayed",downloadPdfUrl:"/chapter/pdf-download/68669",previewPdfUrl:"/chapter/pdf-preview/68669",authors:[{id:"63374",title:"Prof.",name:"Hany",surname:"Elsayed",slug:"hany-elsayed",fullName:"Hany Elsayed"}],corrections:null},{id:"65152",title:"Primary Spontaneous Pneumothorax, a Clinical Challenge",doi:"10.5772/intechopen.83458",slug:"primary-spontaneous-pneumothorax-a-clinical-challenge",totalDownloads:1481,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Primary spontaneous pneumothorax (PSP) is a common disease in medical practice that affects young healthy people with a significant recurrence rate. PSP is the presence of air in the pleural space not caused by injury or medical intervention. Some risk factors include male gender, age, and smoking. Classic clinical presentation starts with acute-onset chest pain and shortness of breath. Physical examination can be normal in small pneumothoraces, but in larger pneumothoraces, breath sounds and tactile fremitus are typically decreased or absent, and percussion is hyperresonant. Chest X-ray can help confirm the diagnosis. Evacuation of air from the pleural cavity and prevention of future recurrences are the primary goals of treatment and depend on the patient’s presentation. Initial deciding factors to direct the management are first-time or recurrent spontaneous pneumothorax and size of the pneumothorax. Treatment may include conventional chest tube drainage, video-assisted thoracoscopic surgery (VATS), or open surgery.",signatures:"Fabian Andres Giraldo Vallejo, Rubby Romero, Melissa Mejia and Estefania Quijano",downloadPdfUrl:"/chapter/pdf-download/65152",previewPdfUrl:"/chapter/pdf-preview/65152",authors:[{id:"188071",title:"Dr.",name:"Fabian",surname:"Giraldo",slug:"fabian-giraldo",fullName:"Fabian Giraldo"},{id:"268282",title:"Dr.",name:"Ruby",surname:"Romero",slug:"ruby-romero",fullName:"Ruby Romero"},{id:"278567",title:"Dr.",name:"Melissa",surname:"Mejia",slug:"melissa-mejia",fullName:"Melissa Mejia"},{id:"278568",title:"Dr.",name:"Estefania",surname:"Quijano",slug:"estefania-quijano",fullName:"Estefania Quijano"}],corrections:null},{id:"65217",title:"Video-Assisted Thoracoscopy in the Management of Primary and Secondary Pneumothorax",doi:"10.5772/intechopen.83669",slug:"video-assisted-thoracoscopy-in-the-management-of-primary-and-secondary-pneumothorax",totalDownloads:879,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The management of primary and secondary spontaneous pneumothorax can have many variations depending on the surgeons and their expertise of practice. The end goal is to stop the recurrence. The history of treatment, clinical indications for surgery, and preoperative and postoperative decision-making for intervention are summarized. Surgical intervention plays an important role in the management of recurrent pneumothorax and complex initial pneumothorax. Over the years the surgical techniques have evolved, and currently, video-assisted thoracoscopic techniques are frequently used in the management. In this concise report, we attempt to analyze the surgical techniques currently in use and their outcomes. Furthermore, we attempt to integrate future innovations in the management of this common disorder.",signatures:"Kostantinos Poulikidis, Lee Gerson, John Costello and Wickii T. Vigneswaran",downloadPdfUrl:"/chapter/pdf-download/65217",previewPdfUrl:"/chapter/pdf-preview/65217",authors:[{id:"268004",title:"Prof.",name:"Wickii",surname:"Vigneswaran",slug:"wickii-vigneswaran",fullName:"Wickii Vigneswaran"},{id:"268007",title:"Dr.",name:"John",surname:"Costello",slug:"john-costello",fullName:"John Costello"},{id:"281023",title:"Dr.",name:"Kostantinos",surname:"Poulikidis",slug:"kostantinos-poulikidis",fullName:"Kostantinos Poulikidis"},{id:"281024",title:"Dr.",name:"Lee",surname:"Gerson",slug:"lee-gerson",fullName:"Lee Gerson"}],corrections:null},{id:"65079",title:"Catamenial Pneumothorax",doi:"10.5772/intechopen.82564",slug:"catamenial-pneumothorax",totalDownloads:1113,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Catamenial pneumothorax is a rare condition in which spontaneous pneumothorax is recurrent. The incidence of catamenial pneumothorax has been underestimated for a few number of reasons. Recently, the etiology of catamenial pneumothorax has been more accurately diagnosed because of increased awareness and interest in the disease. Common and effective use of VATS technique contributed to better understanding of the disease. The management of the disease is difficult because of high recurrence rate. Operative and nonoperative interventions should be practiced more to prevent recurrences. Hormonal therapy should be added to treatment in selected cases. In this chapter, we will discuss all aspects of catamenial pneumothorax from diagnosis to treatment.",signatures:"Sezai Celik and Ezel Erşen",downloadPdfUrl:"/chapter/pdf-download/65079",previewPdfUrl:"/chapter/pdf-preview/65079",authors:[{id:"268979",title:"Prof.",name:"Sezai",surname:"Celik",slug:"sezai-celik",fullName:"Sezai Celik"},{id:"279787",title:"Dr.",name:"Ezel",surname:"Erşen",slug:"ezel-ersen",fullName:"Ezel Erşen"}],corrections:null},{id:"68427",title:"Controversies in Pneumothorax Treatment",doi:"10.5772/intechopen.87141",slug:"controversies-in-pneumothorax-treatment",totalDownloads:961,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Surgical intervention either by video-assisted thoracoscopic surgery (VATS) or open procedure proved its worth in reducing the incidence of recurrence in pneumothorax. However, many controversies surround the management of this common medical condition. Despite advances in knowledge and technology, chest physicians and surgeons could not be more divisive about the management of pneumothorax. There are no two thoracic surgical centres and possibly no two surgeons within the same hospital that agree on the management of the different aspects of pneumothorax. The variability in reported outcomes and the paucity of published multicentre randomised controlled trials (RCT) highlight the need for further studies investigating the best options for pneumostasis and pleurodesis. This chapter aims at discussing some of these controversies and reviews the literature at its current state of evidence.",signatures:"Khalid Amer",downloadPdfUrl:"/chapter/pdf-download/68427",previewPdfUrl:"/chapter/pdf-preview/68427",authors:[{id:"63412",title:"Dr.",name:"Khalid",surname:"Amer",slug:"khalid-amer",fullName:"Khalid Amer"}],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:"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:"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:"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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When oil is added to water and the mixture is stirred vigorously, oil droplets and water droplets are dispersed in the oil and water phase, respectively. This increases the total area at the oil-water interface; therefore, such an emulsion is unstable. As a result, droplets aggregate and fuse gradually to minimize the contact area between the oil and water phase. To improve the stability of emulsions and utilize those emulsions as functional capsules in fields of cosmetics, pharmaceuticals, and foods, many physical and chemical approaches have been developed. Therefore, emulsion science and technology is necessary for an improvement in quality of life. In this chapter, we do not describe a stabilization technology for emulsions but rather a research trend in the behavior of micrometer-sized droplets within emulsions in a far-from-equilibrium state. Namely, the phenomena of oil droplets moving three-dimensionally (self-propelled motion) in a ternary system composed of water, oil, and surfactant and the methods to measure this motion are described.
Surfactant molecules form various types of self-assembly in water or buffered aqueous solution [1]. At a relatively low surfactant concentration in water, colloidal self-assemblies, such as spherical, disklike, rodlike, or wormlike micelles, are formed spontaneously. When a small amount of the oil components is added to such a colloidal system, it is solubilized within micelles. An electron microscope is required for the observation of these swelling micelles (microemulsions). They are stable thermodynamically, and this is defined as an equilibrium condition in this chapter. When the oil component is further increased, emulsions consisting of oil droplets with diameters ranging from nanometer to submillimeter are formed. For example, upon the addition of an oil component that was almost insoluble in water, such as
Typical sequential micrographs (time interval = 3 s) of self-propelled motion by micrometer-sized spherical oil droplets in a ternary system composed of water, oil (
A bright-field stereomicroscope can be used to observe micrometer-sized oil droplets in aqueous solutions when oil droplets are thick and their refractive index is significantly different from their surrounding medium (the bulk solution). However, when the difference in refractive index is small and the oil droplets are thin, specific microscopes are required for observation. In this section, the operating principles of polarized, phase-contrast, and fluorescence microscopes, which are used regularly for the observation of soft matter, including oil droplets at the micrometer scale, are introduced briefly.
When the surfactant concentration is relatively high in an emulsion system, the nematic and lamellar structures of lyotropic liquid crystal phases are formed. Since the interaction of each phase with light differs because of the different molecular orientation, the structures can be characterized by the texture of polarized microscopy images. In the system of a polarized microscope, the transmitted light through samples, placed on a stage between two polarizers oriented at 90° to the illumination, is observed. Since birefringence occurs because of the optical anisotropy of phase in the sample, the phases within the sample can be identified from the observed textures. For example, to investigate the stability of a ternary system, Abe et al. investigated dimyristoylphosphatidylcholine/water/saturated hydrocarbon, where propanol was added as a cosurfactant, and clarified the composition that generated a stable oil-in-water microemulsion using a polarized microscope [2]. In addition, Ho et al. observed a dispersion prepared by stirring lauryl or cetyl alcohol with a sodium lauryl sulfate aqueous solution using ultrasonication, under a polarized microscope [3]. By varying the alcohol concentration and temperature, various phases such as the schlieren textures of the nematic liquid crystal phase were confirmed. No textures were observed in self-propelled oil droplets under a polarized microscope unless water-insoluble molecules exhibiting a thermotropic liquid crystalline phase were used, indicating that they were not in a liquid crystal phase but an isotropic liquid phase. Self-propelled micrometer-sized droplets comprising thermotropic liquid crystal phases in a surfactant solution have recently gained substantial attention regarding the topological defect of the droplet in self-propelled motion [4].
Phase-contrast microscopes are suitable for the observations of transparent specimens, in which their refractive index is similar to that of the surrounding medium (such as living cells and bacteria). Self-propelled motion of micrometer-sized oil droplets in aqueous surfactant solution has also been observed under a phase-contrast microscope. Visualization of transparent specimens with a low refractive index has been achieved by utilizing the diffraction and interference of light. Figure 2 shows the optical path and operating principle of visualization in this microscope system. A phase-contrast microscope has a ring slit in the condenser, and the objective lens is equipped with a phase-shift plate (Figure 2A). The ring-shaped illuminating light that passes the ring slit in the condenser is focused on the phase-shift plate and is guided to the image plane through an objective lens uniformly. However, in the presence of a specimen between the condenser and objective lens, some of the illuminating light is diffracted by the specimen and separated into two diffraction order beams (the +1 and −1 order) and one remaining light beam, which is unaffected by the specimen (zeroth-order diffracted light), known as the background light. The two diffracted light beams change with the direction of travel and are therefore not focused on the phase-shift plate. On the other hand, the zeroth-order diffracted light beam goes straight ahead and passes the phase-shift plate. Thus, three light beams are focused on the image plane of the objective lens. The image contrast is strengthened by the following two factors: the generated interference between the diffracted and background light rays in the regions of the field of view that contain the specimen and the reduction in the amount of background light that reaches the image plane.
Schematic illustration of optics (A) and operating principle of visualization (B) of a phase-contrast microscope. δ1 and δ2 are the lagged phase between the transmitted light rays through the specimen and that of the background, respectively.
For example, if it is considered that the phase of the zeroth-order diffracted light is directed by the phase-shift plate, the light intensity is zero on the image plane where the three light beams interfere and, thus, the image of the specimen should have dark contrast in the bright field of view. In contrast, when the zeroth-order diffracted light is lagged compared to the other two diffracted light beams, the light intensity becomes maximum because of the interference between the three light beams. Therefore, the image of the specimen with bright contrast is observed in the dark field of view. On the basis of these principles, a transparent specimen, which is difficult to distinguish from the surrounding medium using a bright-field microscope, can be observed by strengthening image contrast.
On observing the self-propelled oil droplets using a phase-contrast microscope, it was found that the interior of the droplets had many small particles and they formed a convective flow. The direction linked with the inlet and outlet of this convective flow was the same as the self-propelling motion direction of the droplets, considering that their motion could be associated with the flow fields within the droplets.
Dynamics of molecules and particles which are labeled by fluorescent molecules and particles can be traced selectively. Therefore, fluorescence observations have been frequently used in the life sciences. A transparent specimen can be observed under a phase-contrast microscope. By using a fluorescence microscope simultaneously, the specific molecule and its distribution can be visualized in a specimen.
When a substance, which absorbs light energy and emits its energy as light, is illuminated by excitation light, such as X-rays, ultraviolet light, or visible light, the transition of its electrons from the ground state to the excited state occurs. However, because this excited state is unstable energetically, the electrons that absorb energy relax readily to their ground state. In this relaxation process, the emitted light is fluorescence. The relaxation time from an excited state to a ground state is short, below ~10 ms in general, and the luminescence is quenched due to fading of a fluorescent substance under a continuous excited light. Taking this into consideration, the system of a fluorescence microscope has a specific set of operation principles.
In general, if a fluorescent dye has a suitable molecular structure for emitting fluorescence in a specimen, the distribution and migration of a fluorescent dye can be detected easily. The distribution and migration of multiple structures are also observed simultaneously by using multiple fluorescent dyes. In addition, if the background brightness is lowered considerably, the detection sensitivity using a fluorescence microscope is much higher than when using other observation techniques. Therefore, this technique is frequently used for the observation of the dynamic behavior of self-assemblies formed by amphiphiles in aqueous solution. For example, micrometer-sized droplets in a ternary emulsion system composed of water, oil, and surfactant exhibited demulsification triggered by a photoisomerization of the photoreactive surfactant having an azobenzene group [5]. This phenomenon was clarified by fluorescence observations using a hydrophilic fluorescent dye, calcein, and a hydrophobic fluorescent dye, pyrene. In oil droplet systems that exhibit self-propelled motion, the fluorescence microscopy technique is considered to be effective for visualizing the convective flows of the droplets.
Self-propelled motion of micrometer-sized oil droplets has been observed in dispersions composed of specific oils and surfactants as shown in Figure 3. For example, in a dispersion prepared by adding a benzaldehyde-type oil component (
Reaction formula in emulsion systems in which micrometer-sized oil droplets were observed.
It has been found that self-propelled motion of oil droplets is caused by generated flow fields induced by the adsorption of surfactant molecules onto the droplet surface in dispersion. Hanczyc et al. reported that oleic anhydride (
On the basis of the above findings, the mechanism of self-propelled motion of oil droplets is interpreted as follows (Figure 4). In a relatively concentrated surfactant solution, self-assembly of surfactant molecules, such as spherical and disklike micelles, distributes heterogeneously [11, 12]. On adding of an oil component into such a surfactant solution, oil droplets with various submillimeter sizes are formed. Surfactant molecules begin to adsorb onto the droplet surfaces, and heterogeneity of droplet surfaces is induced by nonuniform surfactant concentrations, as well as by thermal fluctuations. This causes an imbalance in the interfacial tension between sites with adsorbed surfactant molecules (lower interfacial tension) and bare sites (higher interfacial tension) on the droplet surface. The flow at the oil droplet surface based on the imbalance of the interfacial tension is maintained by Marangoni instability, indicating symmetry breaking [13]. Marangoni flow and subsequent mass transfer are likely caused by relatively strong intermolecular interactions between the surfactant and oil molecules. Furthermore, the momentum between inside and outside of the droplet is exchanged through the Marangoni flow, and the droplet itself is driven in a certain direction. These processes can be considered with regard to interfacial energy: the interfacial energy of the droplet’s leading edge (where surfactants are adsorbed) is smaller than that of its trailing edge, inducing a slight movement of droplets including dynamic interfacial fluctuation. The more surfactant the moving droplet takes on, the more the droplet continues to move because of the flux balance between the oil droplet and the bulk solution. At the foreside of self-propelled motion, larger amounts of surfactant molecules adsorb to the droplet surface, and self-propelled motion occurs because of the feedback mechanism caused by the sustainment of flow fields. Thus, self-propelled motion could be affected by both the attractive interactions between the oil and surfactant molecules and the mobility of surfactant molecules at the droplet surface.
Schematic representation of the proposed mechanism for self-propelled motion of oil droplets. Surfactant molecules are omitted in the fourth image. σ, local interfacial tension of oil droplet surface.
To verify the proposed mechanism of self-propelled motion of oil droplets experimentally, the surrounding and internal flow fields of droplets that were observed under a phase-contrast microscope should be analyzed in detail. These flow fields have been visualized, and their motion speed was analyzed using particle image velocimetry (PIV). This is a method that analyzes velocity and its vector of distinguishable particles (tracers), such as micrometer-sized fluorescent beads, in the flow fields by the following procedure. Firstly, the movement of dispersed tracers in the flow fields is monitored successively at a certain time interval. Secondly, the specified region containing some tracers in the
Principle of particle image velocimetry (PIV) measurement. The velocity vector of tracers (white circle) in the square region is calculated by the moving distance for a certain time interval (Δ
Figure 6A shows the PIV results of self-propelled motion of oil droplets composed of benzaldehyde-derivative (
Visualization of the surrounding (A, time interval: 2 s) and internal (B, time interval: 3 s) flow fields of self-propelled oil droplet by using fluorescent beads under a fluorescence microscope.
In contrast, hydrophobic fluorescent beads were dispersed into benzaldehyde derivative
From the proposed mechanism of self-propelled oil droplets based on the heterogeneity in the droplet surface, their motion time, direction, and mode may be controlled and altered by the molecular conversion of oil and surfactant components. Various organic reactions, such as polymerizations [14, 15], enzymatic reactions [16], and synthetic organic reactions [17–19], occur in emulsion systems because of an increase in reagent compatibility, the enhancement of reaction rates, and the induction of regioselectivity. In this section, we introduce the oil droplet system that induces unique dynamics, such as directional motion, division, and deformation, triggered by the molecular conversion of oil and surfactant components.
Micrometer-sized objects exhibiting well-controlled motion have drawn much attention because of their potential use as probes or sensors for exploring environmental or biological systems and as carriers for transporting compounds in very small spaces [20–22]. To control the motion time and direction of micrometer-sized oil droplets, gemini-type cationic surfactants containing a carbonate linkage in the linker moiety (
Schematic illustration showing the control of the motion time and direction of oil droplets in the presence of gemini-type cationic surfactant containing a carbonate linkage in the linker moiety under a basic condition.
Even though oil droplets composed of benzaldehyde
Schematic illustration of division during self-propelled motion of oil droplets induced by the reversible reaction of benzaldehyde
A different division mode from the above system was also investigated using a cationic surfactant having a five-membered acetal moiety (
Although the above oil droplets maintained spherical morphology during self-propelled motion, there have been no reports on micrometer-sized self-propelled oil droplets mimicking amoeboid motion in aqueous solution. Similar to white blood cells, amoeboid motion of amoebae such as
To construct an oil droplet system exhibiting deformation, a mixture of two miscible compounds having similar molecular structure, fatty aldehyde
Schematic illustration of deformable self-propelled oil droplets composed of a fatty aldehyde and an alcohol.
In this chapter, we demonstrate the observation methods for the self-propelled motion of micrometer-sized oil droplets using phase-contrast, polarized, and fluorescence microscopes and discuss the mechanism of motion. Since it is visible to human eye, self-propelled motion of the millimeter-sized oil droplets on the surface of aqueous solutions has been reported by many researchers [24, 29–35]. Recently, their divisions, fusions, and morphological changes have also been described. For example, Sumino et al. reported, with small-angle X-ray diffraction, that the gel phase formed spontaneously around millimeter-sized self-propelled oil droplets exhibiting “blebbing” [35]. However, since the generated self-assemblies in micrometer-sized droplet systems are difficult to identify by spectroscopic methods, the mechanisms of their self-propelled motion have not been clarified. When the mechanisms are fully understood at the nanometer to micrometer scale, the described findings will be useful to the development of more stable emulsion systems. In addition, such systems could be utilized for the reaction (microreactors) and transportation (microcarriers) of biomolecules and low-molecular-weight organic compounds [36, 37]. Furthermore, microchannel technology combined with the self-propelled droplet system is expected to lead to the development of droplet-type analysis system at the micrometer scale that can carry out reaction, analysis, and detection automatically without the need for an external force. We envisage that such automatic reaction field and analysis systems will be helpful for the improvement of novel emulsion technology.
This work was supported by the Grants-in-Aid for Scientific Research (No. 25790033 and No. 16K17504 for T.B.) and Scientific Research on Innovative Areas “Fluctuation and Structure” (No. 25103009 for T.T.) from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
The Fourth Industrial Revolution, or Industry 4.0, aims at automating traditional manufacturing and industrial practices exploiting the most recent technologies depicted in Figure 1. Integrating artificial intelligence (AI) and robotics with traditional practices, the world of manufacturing processes is undergoing a transformation from activities that rely on human experience and skills into flexible environments, including objective decisional systems fully integrated within the industrial process. Advanced robotics is meant to develop autonomous and intelligent systems that could reduce the intervention of human workers [1] in many of the crucial and repetitive tasks that represent the core business of companies. Augmented and virtual reality can give operators more information about their tasks [2] and help them to alleviate mental stress during some jobs. Additive manufacturing [3] can speed up the production process. Internet of things (IoT) [4] allows new forms of communication between machines, giving rise to smart devices that can help humans achieve their objectives. Radiofrequency identification (RFID) technologies are used for efficient logistics and inventory warehouse management [5] reducing costs while increasing quality and competitiveness.
Industry 4.0 pillar technologies.
Among all the aforementioned technologies, AI is perhaps the one that received more interest during the years. Indeed, nowadays, the industrial interest in AI applications in various sectors is undeniable. However, for industries, artificial intelligence is both a source of enthusiasm and skepticism. One reason is that deep learning (DL) is a technology based on data, and problems solved using AI are as good or as bad as the data they are trained on. In addition, companies perceive AI as a black box and would prefer understandable and explainable processes [6]. Both these aspects should be taken into consideration when developing industrial AI solutions.
Current automation-assisted production is mostly open-loop and relies on specific checkpoints to perform product quality analysis. Early systems based on vision date back to the nineties. Such an approach suits best when critical issues can be formally expressed by taking advantage of geometrical measurements or well-known features on the inspected objects. Unfortunately, these techniques cannot perform many quality-control activities because they need a predefined sequence of actions where quality checks should be designed carefully to meet the precise production requirements. Moreover, human nature shows formidable efficiency in learning simple checks even if it would be difficult to formalize such operations with a sequence of rules. Indeed, experience plays a relevant role in human evaluation for products quality assessment. Similarly, vision inspection processes performed by automated machines will require the development of novel algorithms that should be trained and improved with time and experience.
The introduction of automation systems in the production lines that exploit AI techniques has reduced the need for human intervention in the manufacturing process of many products. This innovation had a major impact on many industrial applications, and visual inspection is by far the activity that has profited most. Thanks to deep neural networks (DNNs), difficult computer vision tasks, such as object classification or detection and image segmentation, have been addressed recently using an adequate number of training data. DNNs are scalable, experience-based, and have similar performance to human workers. Since the development of AlexNet [7], solutions based on deep learning have been encouraged, and convolutional neural networks (CNN) also have been extensively utilized for automating optical quality inspections. However, since such networks need a huge amount of labeled data for training their parameters, it is difficult to have an adequately large set of faulty samples with well-optimized industrial processes for creating a
Welding is a fundamental activity in many industrial manufacturing processes, such as automotive, shipbuilding, aerospace, and electronics. It is a crucial operation for the overall quality of the production line because a defect not detected in the early stages can determine the rejection of the entire product. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and presenting in detail a solution for quality checks in fuel injectors welding during the production stage.
Inspection analysis can be classified into one of the following categories [8]—
As of today, different methods have been proposed for inspecting the welding process online [9]. Their design is suited to diverse defects types and differ in the data processed during the evaluation. Among the sensing technologies employed in literature, optical detectors [10], acoustic measurements [11], and vision analysis [12] are surely the most utilized. While, for classification applications, artificial neural networks [13, 14, 15] and fuzzy inference systems [16, 17] are usually preferred thanks to the wide range of problems and diversity of defects they could cope with as in the case of classification of steel strip defects [18, 19].
However, the focus of these works is on defects classification and not on their detection. Therefore, they could not cope with feature understanding problems such as discriminating between good samples and defective ones. A different approach is proposed by Ak et al. [20] where X-ray images are used to detect defects in metal castings.
Recent literature is plenty of research addressing the problem of welding localization employing off-the-shelf DL architectures or introducing slight modifications on the tail of popular networks. These approaches are mostly based on the R-CNN [21], Faster R-CNN [22], and YOLO [23] architectures. The reason behind their adoption is that these architectures usually require little fine-tuning procedures for efficiently localizing welding areas and spots. Such efficiency is strictly related to the presence of plain metal surfaces in the surrounding area of the welding by enabling simple and accurate segmentation of the feature under inspection. This is the case of resistance spot welding (RSW) processes typically employed to connect metal sheets at a low cost and in a short time.
Concerning detection approaches, early methods based on traditional computer vision techniques [24] require hand-crafted features and complex threshold settings to adapt to environmental conditions. However, approaches based on deep learning allow increasing the robustness of the detection coping with environmental noise and the sensitivity of the welding processes.
The majority of approaches are built upon the above-mentioned architectures for welding spots localization. Fast R-CNN [25] is a region proposal network that computes the region of interest (ROI) on the feature map, thus improving upon the R-CNN architecture. Faster R-CNN integrates convolutional layers for object classification, feature extraction, bounding box regression, and region proposals into a network, further improving the detection performance but still not reaching real-time capabilities. Unlike the R-CNN family, which has a two-stage detection architecture, YOLO implements a regression network with a grid of bounding boxes and associated class probabilities, thus enabling real-time detection with recent hardware. In the race for timing performance, YOLOv2 [26] borrowed the anchor mechanism from SSD [27] and Faster R-CNN, which also enhanced the network
Considering the reduced dimension of small spot welds, low-resolution feature maps in the backbone, and convolution strides dimension could cause an information leak. To face this issue, the work proposed by Dai et al. [34] introduces a modified MobileNEtV3 [35] architecture obtaining a good tradeoff between
Focusing on the classification and detection of defects over the welding area or joint, off-the-shelf solutions are no more efficient by themselves, and some issues need to be faced to enable the use of DNNs. Clustering and segmentation become difficult because the feature to be recognized are not easily separable. This chapter introduces some of the most common issues in the employment of DL for industrial quality inspection discussing the practical case of detection of welding defects in diesel injectors heads.
Quality inspection systems based on vision techniques in most cases follow the workflow depicted in Figure 2. The process starts by collecting the sample images using a set of cameras or sensors exploiting an adequate source of illumination. Such samples are then processed to improve images quality. Therefore, once the features are extrapolated, the evaluation of the quality and the classification of the defect are performed. Measurement and classification could either be implemented with traditional computer vision algorithms, with modern DNN architectures, or with a fusion of both of them, as in the case presented in the following. Usually, the inspection system also provides an actuation step that triggers actions, depending on the analysis result, to the production lines that directly communicate with the control unit (commonly based on programmable logic controllers (PLCs)).
Typical visual inspection workflow.
The work discussed in the study by Sassi et al. [36] originated from industrial demands with the specific target of detecting welding defects on diesel injectors in the production line. Such a project focused on realizing the most effective combination of traditional computer vision methods and deep neural network architecture for identifying the defects in the welding. In particular, the aim was to substitute the existing vision inspection system extending the classes of detectable defects in the analysis phase.
Welding joint defects may appear in different typologies: some are related to anomalies on the surface of the joint, while others are related to its geometrical properties, such as its thickness and position. Four categories have been defined for the analysis of the welding joint, as depicted graphically in Figure 3 showing an example from each category:
D1 (
D2
D3 (
D4 (
Examples of defect classes. IN D3, green and red circles show the detected inner and outer edges of the welding joint. In D4, the red arrows highlight thin welding, while the green ones are standard ones.
Defects D3 and D4 are quantitative measurable and are examined employing an algorithm based on traditional computer vision techniques (similar to the existing commercial solution). On the contrary, the others (D1 and D2) are more qualitative and are recognized through a method based on deep learning.
Furthermore, the analysis of the defects must be performed within a time slot that depends on the actual production line (1.8 seconds cycle time in the depicted scenario) to avoid interferences with the manufacturing process. This amount of time is required for the actuation system and the welding stage to process a new injector as input to the system.
During dataset preparation, the ideal case is the one in which several samples (in the order of thousands or more) are available for each class to be detected, the classes have balanced data, and they are well separated from each other. In such an ideal case, it is possible to give the network a representative set of samples of the whole input space for the training and avoid confusing the network with an uneven distribution of the inputs or the similarities between the classes.
Unfortunately, industrial production lines having well-optimized processes are usually present with few defective products and much more good samples. Therefore, it is often unfeasible to get sets of defective samples large enough to train CNNs for classification purposes. In the majority of the cases, the objective of the training moves from defect classification to anomaly detection. The worst-case scenario is the one presenting an
Different sampling strategies could be implemented to deal with
An alternative approach that is often used to increase the robustness of the classification is
Other ways for enlarging the dataset have been experimented like passing the input data through an encoder-decoder network that applies different transformations featured with random noise [42]. Another approach worth mentioning is the generation of
Virtual data generation could be obtained by producing synthetic images with the intent to cover the whole input feature space. Generative adversarial network (GAN) [44] or the most recent conditional GAN (cGAN) [45] could be alternatively used for this purpose. However, this is computationally expensive and requires taking into account all possible configurations and boundary conditions for generating samples as close as possible to real ones. Domain randomization techniques [46] could be applied to synthetically generated data for improving the generalization capabilities and the robustness of the network.
Similar to humans, when learning new concepts or rules, if not clearly defined, the training can lead to fuzzy assumptions, possibly resulting in wrong outcomes. Additionally, when dealing with data obtained by a sensing apparatus, it is important to check the correctness of the acquired data samples to avoid possible causes of classification errors. A cleaning process should remove outliers (wrong data association of a sample with a class) and spurious samples that could confuse the learning process. Industrial processes often rely on qualitative evaluation, and unfortunately, different quality experts in the same industrial process classify the same product as belonging to different classes. If the same confusion is transferred to the DL architecture, the learning process will probably worsen the decision process. For this reason, a preprocessing stage on the data is essential. In most cases, the help of professionals of the sector for interpreting, filtering, and preprocessing the data is welcome.
A last and quite important aspect is the adoption of correct performance metrics and
It has been seen that the first layers of CNNs learn kernels acting as color blob detectors or Gabor filters. Such a property seems to be very general and the features learned do not appear to be strictly dependent on the particular training set that has been adopted. As humans can learn from experience and transfer the notion learned in diverse application domains, similarly, a DL architecture can transfer the features learned on a particular dataset to another CNN, which will be trained on a different one [51]. Such a technique is called
Following the
The work combines a traditional computer vision pipeline together with a DL architecture. This pipeline was necessary to maintain the compatibility with classical production lines and provide a correct input to the welding defect detection phase. The algorithm receives the raw image as input, converts it from Bayer format to grayscale, and improves the edge detection by equalizing the levels and applying a Gaussian blur. In a successive step, since different kinds of injectors can be analyzed by the same system, the type of injector is identified, and the position of its center is obtained. The algorithm proceeds to detect the outer shell of the injector head by estimating an external radius that approximates the detected blob. Then, using the extracted information, the algorithm performs an area search for welding points and estimates a welding circle on the joint. Subsequently, the algorithm collects statistics about the number of welding points found and their positions. In traditional industrial systems, a set of thresholds decided by the manufacturing company is used to evaluate the welding quality from the measured quantities.
A schematic overview of the algorithm is shown in Figure 4. The algorithm’s output gives quantitative information about the welding and produces a processed image to be given as input to the second analysis stage. The extracted information allows evaluating the continuity of the welding in a certain area on the injector’s head, verifying the centering of the inner part of the injector with respect to the outer one, and eventually the welding thickness. This information is also beneficial to clean the image from unnecessary data for the subsequent analysis and to center the injector images to obtain more controlled conditions on the input of the successive stage.
Schematics of the components of the geometrical analysis pipeline.
The DL architecture chosen in that work is the DenseNet-121. Figure 5 depicts the structure of the network. DenseNet efficiently simplifies the connectivity pattern between layers guaranteeing maximum information flow by reusing the features through the network. Concerning the training phase, every layer has direct access to the gradients from the original input image and the
Schematic representation of the layers and blocks in the DenseNet-121 deep learning architecture.
In the approach presented by Sassi et al. [36], the
Unfortunately, the MINC dataset is highly unbalanced. Therefore, three classes, that is,
Sometimes, during production lines maintenance or innovations, the replacement of a machine, the change of a supplier, or the change in a manufacturing process, could lead to a significant variation on the usual production procedure in terms of the visual quality of the products. Such situations could vanish the capacity of a machine computation to return the expected results.
In this context, continuing on the problem of detecting welding defects on injectors heads, the work presented by Tripicchio et al. [48] proposes possible solutions to this issue without requiring an architectural change in the learning architecture. The new case had to handle some modifications concerning the parameters associated with the welding process, producing input samples with specific artifacts that the previously designed and trained network did never encounter. In particular, such new inputs were correlated to a variation in the substance used for the soldering that generated gold-violet spots on the injector head in random positions. Such noise introduces a novel complexity in the detection of the defects because the spots can hide or visually resemble the presence of bumps and holes in the welding layer. The followed approach was to make fewer changes as possible in the architecture of the network, operating a smart preprocessing and applying filtering techniques.
The results show the ability to train a network with almost 7 million parameters on just 306 training images belonging to the new alteration, achieving a
Such a result has been achieved leveraging on two important aspects. The first is the design of a custom preprocessing and filtering stage, while the second is the adoption of a novel data balancing strategy.
A preprocessing stage is needed on the input images with the aim of erasing or smoothing the chromatic nuances that could confuse the feature learning process. In particular, three filtering approaches have been proposed and tested (Figure 6). The first filter (
Different filters applied on a sector of the same injector contour image. (a) No filter. (b) Median fill filter. (c) Patch filter.
Different analyses have been done to assess the performance improvement given by such filters. As a result, a
Concerning data imbalance, an exploration of different unbalanced splits has been performed. To prevent overfitting and lead the learning process toward generalization, the authors propose to compute the performance metrics at each evaluation step considering the input imbalance. In particular, metrics like
Defective injectors were chosen as positive samples and
Cross-validation has been applied to improve generalization concerning the stochastic gradient descent optimization. The network has been trained multiple times by combining different variations of the proportions between defective and good samples and changing the numbers of epochs. During the training phase, each epoch is compared with all previous epochs for obtaining the one with the highest performance in terms of
The
This chapter highlights the importance of the employment of deep learning architectures in the context of future industrial applications with a focus on welding and welding defects detection. The industrial sector and especially the manufacturing industry pose several challenges to the design of efficient and robust quality inspection processes. The most common issues are discussed in detail, and possible countermeasures are suggested to overcome such issues. In particular, the problem of data imbalance, scarcity of examples, environmental noises, change in the nominal conditions of the process, or the presence of artifacts are discussed. Application examples from previous works of the authors are proposed to clarify how the suggested countermeasures can be put into practice. Although many industries are still scared of adopting deep learning approaches due to a lack of knowledge of their internal processes or reasoning, extensive use of artificial intelligence applications is envisaged for the near future.
AI | Artificial Intelligence |
CNN | Convolutional Neural Network |
DL | Deep Learning |
DNN | Deep Neural Network |
GAN | Generative Adversarial Network |
ICT | Information and Communication Technologies |
IoT | Internet of Things |
PLC | Programmable Logic Controller |
ROI | Region of Interest |
RSW | Resistance Spot Welding |
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