Particle size distribution of soil elements.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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He has trained hundreds of doctors globally in minimally invasive aesthetic surgery and medical procedures as well as in his author techniques. \nHe is the creator of Scarless Serdev Suture® lifts of face and body and pioneer in many other mini-invasive cosmetic surgery techniques: ultrasound liposculpture of face, body and leg elongation and beautification; T-excision and columella sliding in rhinoplasty; non-surgical body contouring; and their combination with his own suture to facial rejuvenation, beautification and tissue volumising. He is also a world authority in ultrasound-assisted (VASER) body contouring and has trained doctors from around the world in its basic and advanced techniques. \nDr. Serdev is Honorary professor at the New Bulgarian University in Sofia and director of its program of qualification in Aesthetic Surgery. He divides his time between his clinic, the University and short travels for international meetings, live surgery demonstrations and hands-on courses in countries of all continents. \nHe is editor and author of several comprehensive textbooks in the field of Cosmetic and Plastic Surgery, including Liposuction, Sutures Lifts on Face and Body Areas, Thread vs Suture lift techniques, Rhinoplasty and has authored many chapters in aesthetic surgery and medicine books, as well as original papers in medical journals.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"20",totalChapterViews:"0",totalEditedBooks:"5",institution:{name:"National Center of Infectious and Parasitic Diseases",institutionURL:null,country:{name:"Bulgaria"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1143",title:"Cosmetic Surgery",slug:"cosmetic-surgery"}],chapters:[{id:"45713",title:"Serdev Sutures® in Upper Face: 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Face",slug:"the-holistic-scarless-rejuvenation-of-the-face",totalDownloads:3584,totalCrossrefCites:1,authors:[{id:"157271",title:"Dr.",name:"Desmond",surname:"Fernandes",slug:"desmond-fernandes",fullName:"Desmond Fernandes"}]},{id:"45708",title:"Midface Thread Lifting: Method of Internal Suturing",slug:"midface-thread-lifting-method-of-internal-suturing",totalDownloads:3589,totalCrossrefCites:1,authors:[{id:"149988",title:"PhD.",name:"Marlen",surname:"Sulamanidze",slug:"marlen-sulamanidze",fullName:"Marlen Sulamanidze"},{id:"151927",title:"Dr.",name:"George",surname:"Sulamanidze",slug:"george-sulamanidze",fullName:"George Sulamanidze"}]},{id:"45714",title:"Mastopexy ‒ How to Reach Consistent Results ‒ New Methods",slug:"mastopexy-how-to-reach-consistent-results-new-methods",totalDownloads:2068,totalCrossrefCites:0,authors:[{id:"149988",title:"PhD.",name:"Marlen",surname:"Sulamanidze",slug:"marlen-sulamanidze",fullName:"Marlen Sulamanidze"}]},{id:"45715",title:"Morphological Foundations of Facelift Using APTOS Filaments",slug:"morphological-foundations-of-facelift-using-aptos-filaments",totalDownloads:1847,totalCrossrefCites:0,authors:[{id:"149988",title:"PhD.",name:"Marlen",surname:"Sulamanidze",slug:"marlen-sulamanidze",fullName:"Marlen Sulamanidze"}]},{id:"45724",title:"Experience in Preventive Measures and Treatment of Complications at Face and Neck Thread Rejuvenation",slug:"experience-in-preventive-measures-and-treatment-of-complications-at-face-and-neck-thread-rejuvenatio",totalDownloads:3524,totalCrossrefCites:0,authors:[{id:"149988",title:"PhD.",name:"Marlen",surname:"Sulamanidze",slug:"marlen-sulamanidze",fullName:"Marlen Sulamanidze"}]},{id:"45716",title:"New Method of Face Elastic Thread Lift",slug:"new-method-of-face-elastic-thread-lift",totalDownloads:6167,totalCrossrefCites:0,authors:[{id:"149988",title:"PhD.",name:"Marlen",surname:"Sulamanidze",slug:"marlen-sulamanidze",fullName:"Marlen Sulamanidze"},{id:"151944",title:"Dr.",name:"Constantin",surname:"Sulamanidze",slug:"constantin-sulamanidze",fullName:"Constantin Sulamanidze"}]},{id:"45721",title:"Minimally Invasive Face and Neck Lift Using Silhouette Coned Sutures",slug:"minimally-invasive-face-and-neck-lift-using-silhouette-coned-sutures",totalDownloads:6279,totalCrossrefCites:4,authors:[{id:"151254",title:"Dr.",name:"Peter",surname:"Prendergast",slug:"peter-prendergast",fullName:"Peter Prendergast"}]},{id:"45709",title:"T3 - Soft Face Lift by Suspension Surgery",slug:"t3-soft-face-lift-by-suspension-surgery",totalDownloads:5219,totalCrossrefCites:0,authors:[{id:"151322",title:"Prof.",name:"Pier Antonio",surname:"Bacci",slug:"pier-antonio-bacci",fullName:"Pier Antonio Bacci"}]},{id:"45720",title:"Correction of Face Involutional Changes by Method of Light Lift Elegance Thread 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a Commissioning Editor at IntechOpen, I work closely with our collaborators in the selection of book topics for the yearly publishing plan and in preparing new book catalogues for each season. This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. I am responsible for developing and maintaining strong relationships with all collaborators to ensure an effective and efficient publishing process and support other departments in developing and maintaining such relationships."}},relatedBooks:[{type:"book",id:"330",title:"Advanced Techniques in Liposuction and Fat Transfer",subtitle:null,isOpenForSubmission:!1,hash:"b46d7571b3a7f78d03a83f809c7a637f",slug:"advanced-techniques-in-liposuction-and-fat-transfer",bookSignature:"Nikolay Serdev",coverURL:"https://cdn.intechopen.com/books/images_new/330.jpg",editedByType:"Edited by",editors:[{id:"32585",title:"Dr.",name:"Nikolay",surname:"Serdev",slug:"nikolay-serdev",fullName:"Nikolay Serdev"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5110",title:"Miniinvasive Techniques in Rhinoplasty",subtitle:null,isOpenForSubmission:!1,hash:"795ab5e7adfc30a3907f52b636c45029",slug:"miniinvasive-techniques-in-rhinoplasty",bookSignature:"Nikolay Serdev",coverURL:"https://cdn.intechopen.com/books/images_new/5110.jpg",editedByType:"Edited by",editors:[{id:"32585",title:"Dr.",name:"Nikolay",surname:"Serdev",slug:"nikolay-serdev",fullName:"Nikolay Serdev"}],productType:{id:"3",chapterContentType:"chapter",authoredCaption:"Authored by"}},{type:"book",id:"7047",title:"Botulinum Toxin",subtitle:null,isOpenForSubmission:!1,hash:"2a6791462a0bbc32c6da3218dcf5ac28",slug:"botulinum-toxin",bookSignature:"Nikolay Serdev",coverURL:"https://cdn.intechopen.com/books/images_new/7047.jpg",editedByType:"Edited by",editors:[{id:"32585",title:"Dr.",name:"Nikolay",surname:"Serdev",slug:"nikolay-serdev",fullName:"Nikolay Serdev"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5346",title:"Body Contouring and Sculpting",subtitle:null,isOpenForSubmission:!1,hash:"79bcaa74382f64066fe10bf59e89be6d",slug:"body-contouring-and-sculpting",bookSignature:"Nikolay P. 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Pollution of water resources has become a major environmental challenge for many urban areas in both developed and developing countries [1, 2, 3]. Although developing countries are characterized by a lower industrialization, the deficiency observed in solid waste and wastewater management and the lack of city planning constitute factors facilitating the environmental pollution of cities. Moreover, unexpected population growth is related to water quality degradation and is causing large increase in nutrients and microbial loads [4, 5, 6]. Therefore, urban areas have the potential that generate environmental impacts at multiple scales [4]. Urbanization often causes environmental degradation and harms human health particularly in developing countries [7].
\nDue to reasons relating to the economic problems of developing countries, when urban effluents are actually collected, they are most usually discharged into open drainage canals or into septic tanks equipped with infiltration shafts [8]. Infiltration of urban stormwater runoff can be also considered as one of the main factors of the deterioration of soils and groundwater quality [3]. This last is increasingly subject to intensive voluntary discharges of highly polluted effluents, wastewater, and runoff water in urban areas [9]. Studies have shown that in developing countries there is a strong likelihood of a marked correlation between the groundwater contamination in urban areas and the way in which public services operate [10]. In the specific socioeconomical context of developing countries, there is an important challenge of public health related to the possibility of the appearance of biological risk due to the contamination of groundwater resources by pathogenic microorganisms.
\nIn Haiti, oocysts of Cryptosporidium have been detected in surface water used as drinking water and in the water supplied by the public water service of Port-au-Prince [11, 12]. In the surrounding region of Cap-Haitien, investigations conducted on water resources revealed the presence of Cryptosporidium oocysts in surface and groundwater used by the population for domestic purposes [13, 14]. The number of oocysts detected in the city of Cap-Haitien ranges from 741 to 6088 oocysts per 100 liters of water.
\nIn the city Les Cayes (Haiti) has highlighted the presence of the Cryptosporidium oocyst groundwater used by the population for domestic purposes [10, 15, 16]. The number of oocysts detected varied from 5 to 100 per 100 l of water [15]. These results show that the surface water and groundwater of Cayes are contaminated by pollution of fecal origin and are a source of potential biological risk for the health of the population exposed. Human risk assessment due to these results shows in evidence biological risks for consumers that range from 4 to 1274 oocysts per 100 l of water. The management phase of this assessment allows for the questioning of physicochemical mechanisms governing the transfer of oocysts from the ground to underground waters. The objective of this study was to investigate the behavior of Cryptosporidium oocysts under physicochemical conditions and soil characteristics in saturated porous media of Les Cayes and to describe the water pollution dynamics of Cryptosporidium oocysts.
\nThis work reported in this chapter has four main sections. Initially, we introduce the chapter. The first section proposes a synthesis of information on the biological cycle of Cryptosporidium, followed by a review of source and transport of the physicochemical behavior of Cryptosporidium oocysts. The second section describes the geography of the City of Les Cayes. It also describes the hydrogeological properties of this city while taking care to underline the management system of wastewater and solid waste developed in that area. The third section presents the chemical conditions and soil characteristics which can constitute factors influencing the retention of oocysts or facilitate their transfer into groundwater. The fourth section is devoted also to sharing results of works on the mobility and retention of C. parvum oocysts in soil of the City of Les Cayes.
\nCryptosporidium represents the genus of a variety of intracellular parasites that infect vertebrates, including humans, worldwide. Cryptosporidium belongs to Apicomplexa (synonym Sporozoa), an obligate parasitic group of eukaryotes. However, recent studies, based on genetics and physiology, have now relocated the genus Cryptosporidium from Coccidia to Gregarina, which includes free-living stages, enabling host-free multiplication, and so may constitute an additional risk factor for human infection [17]. The life cycle of Cryptosporidium is monoxenous, which means that every stage in the parasitic cycle’s development takes place inside the same host. The multiplication cycle comprises of sexual and asexual stages. The progress of this cycle is schematically depicted in Figure 1. The asexual multiplication results in merozoite formation, enabling reinfection of intestinal epithelial cells and the sexual cycle culminating in thin or thick-walled sporozoite-containing oocysts (4–6 μm with an ovoidal or round shape) [18]. Infection occurs when the oocysts are ingested by a suitable host. While in the intestines, the oocyst releases sporozoites which invade the epithelial linings of the intestines or the lungs. The infectious oocysts are passed through the feces and enter the environment. Under natural conditions, fecal matter shelters oocysts from desiccation and increases the impermeability of the wall to small molecules, thereby reducing their exposure to lethal environmental factors [19]. The resistance of oocysts in a solid matrix such as soil has become a crucial parameter in understanding their transfer to lower layers. Oocysts persist longer in soil than in water at the same temperature, with a preference for moist silty soils rather than moist clay or sandy [20]. Oocysts can remain viable and infectious in water for several months at temperatures ranging from 0 to 30°C. Other tests have shown that boiling water can kill Cryptosporidium oocysts in less than a minute [21]. The exposure to sunlight had no effect on the viability of Cryptosporidium oocysts, but UV at 265 nm and black light at 365 nm lead to a reduction in the number of viable oocysts [22].
\nLife cycle of Cryptosporidium. Reported in [10]. Source: http://www.stanford.edu/group/parasites/ParaSites2005/Cryptosporidiosis/index.html.
Cryptosporidium is a zoonotic intestinal protozoan parasite that affects the gastrointestinal tract of humans and animals [23, 24]. This protozoan parasite is transmitted via the fecal-oral route and is an important cause of childhood diarrhea and mortality [25]. In developing countries, 8–19% of diarreal diseases are attributed to Cryptosporidium [26]. Furthermore, 10% of the population in developing countries excretes oocysts which the scattering and resistant form of Cryptosporidium sp. is eliminated with feces [27]. Immunocompetent persons will usually recover from the illness within 2 weeks, whereas immunocompromised individuals, including patients undergoing chemotherapy for cancer, patients with AIDS, infants, children, and elderly, may be afflicted with chronic and debilitating illness [28]. In natural surface water and soil, Cryptosporidium oocysts can survive up to 6 months [19, 29]. Oocysts are resistant to a number of environmental stresses, including chlorination during drinking water treatment.
\nThe infectivity oocysts are high, and ingestion of just a single oocyst generates an infection probability [30]. Many outbreaks of foodborne cryptosporidiosis have been described also. In Milwaukee in North America (1993), a massive outbreak of acute diarrhea was caused by drinking water treatment deficiencies; about 403,000 persons were affected and 69 died [31]. According to [32] there have been at least 18 outbreaks of cryptosporidiosis in which foodborne transmission has been epidemiologically implicated, and 8 of these outbreaks were directly linked to consumption of fresh produce [33]. Furthermore, two more foodborne outbreaks (one in Finland and one in the UK) have been published subsequently [34, 35].
\nSuch environmental contamination can be from soil, particularly soil amended with feces or manure, or from water such as irrigation water or wash water along the food chain [36]. Several models have been used to study pathogen transport. Previous work has investigated the transport of Cryptosporidium oocysts in terrestrial environments [37, 38]. To remove oocysts from water resources, several mechanisms such as filtration methods [39]; laboratory columns [40, 41] or radial stagnation point flow (RSPF) cell [42, 43] have been used. Otherwise various aspects of C. parvum transport and removal in granular porous media have been also examined, such as the influence of solution chemistry, fluid flow rate, and sediment grain size [40]. Detailed experimental observations have since confirmed this to be the case, especially through soil macropores and in karstic geological terrain through fractured bedrocks [44, 45]. Based on experimental evidence, it seems well established that C. parvum oocysts can move in soils through preferential pathways in relatively large amounts [46]. The surface properties of the oocyst wall may mediate how it survives and its interactions with chemical and particulate surfaces in the environment. Therefore, changes in the wall may affect adhesion, transport properties, and mobility in natural environments and treatment plants. Oocyst removal in porous media is still poorly understood. The transport of Cryptosporidium oocysts in the subsurface environment is of great concern for water quality.
\nThe Cryptosporidium oocysts are spheroidal shapes with a specific gravity of 1.0 g cm−3 [47]. Cryptosporidium oocysts’ wall has three layers. An acidic glycoprotein is hypothesized to make up the outermost layer. The inner layer also appears to be a filamentous glycoprotein, while the central layer is thought to be a rigid, complex lipid [48]. The thickness of the wall and its capacity to strongly adhere to both organic and inorganic surfaces are features that could be attributed to its survival in the environment [49]. Other studies determined on the oocyst surface that it has high contents of the amino acid cysteine, proline, and histidine [50]. Otherwise oocysts have a negative surface charge under typical environmental conditions [51, 52, 53, 54, 55, 56, 57], likely due to the presence of carboxylate, carboxylic, and phosphate groups on the oocyst surface [55]. Both steric and electrostatic forces can play a role in oocyst association with suspended particles [58]. The understanding and mastering of the complex sorption phenomena involve an evaluation of the hydrophobic and electrostatic surface properties of the parasite [50]. Indeed, the surface properties of the oocyst wall may mediate how it survives and its interactions with chemical and particulate surfaces in the environment. Therefore, changes in the wall may affect adhesion, transport properties, and mobility in natural environments. The most important physical and chemical adhesion properties are the surface charge and hydrophobic characteristics [59]. The surface electrokinetic potential is negatively charged with a range from −19 to −42 mV at a neutral pH [60]. However, the negative surface charge increases with decreasing pH, and the hydrophobicity is low with high medium conductivity [48].
\nDeyac et al. [61] elaborated in a working paper based on the data found in literature on the transport of Cryptosporidium oocysts. Based on its size, the C. parvum oocyst is physically classified as a biological colloid. Surface charges measured by the ξ potential of the oocysts have been found to be neutral to slightly negative in most natural waters. Exact values depend on analytical methods used [50, 62]. Transport and filtration of such colloids in porous media are by advection hydrodynamic dispersion and interactive processes between colloids and solids surfaces [63]. A simple one-dimensional transport model in a steady-state flow field is:
\nwhere c is the concentration of C. parvum oocysts in suspension, s is the concentration of C. parvum oocysts adsorbed reversibly on solid surfaces, ρb is bulk density, θ is porosity, d is the hydrodynamic dispersivity coefficient, v is the advective pore velocity, and λ is the colloid filtration coefficient.
\nIt is commonly observed that v in Eq. (1) is larger for colloids than for water (velocity enhancement) [64]. Note that Eq. (1) accounts for permanent deposition (filtration) of colloids through the first-order term vλc as well as for reversible deposition (sorption) through the second term on the left-hand side. Several models have been introduced to describe the permanent removal of colloids by filtration onto the solid phase [65, 66]. Mass balance considerations for the deposition of colloids in a clean packed filter bed of uniform spheres yield the following relationship between the filtration coefficient and the physical properties of filter bed and colloid [66, 67]:
\nwhere dc is the median grain size diameter, α is an empirical constant referred to as collision efficiency, and ƞ is the collector efficiency. Collector efficiency, ƞ, represents attachment to solid surfaces due to colloid advection and diffusion, interception, buoyancy, and London-van der Waals attractive forces. These four characteristics are expressed in dimensionless form by the Peclet number, NPe; the interception number, NR; the gravitation number, NG; and the London-van der Waals constant, NLo [66]. In addition, a correction factor, As, is introduced that accounts for the pore geometry and its impact on packed bed collector efficiency. Rajagopalan and Tien showed that ƞ is then computed from [66, 67]:
\nFor the evaluation of the C. parvum transport behavior in column experiments, the collector efficiency, Eq. (3) can be computed a priori from the physical properties of the pore space (porosity, median grain size, bulk density), from the physical properties of water (density, viscosity, pore velocity) and from the physical properties of the colloid (density, mean diameter, particle diffusion coefficient). The collision efficiency, α, represents an empirical constant to account for the fact that repulsive forces at the collector surface (double-layer repulsion), which are not accounted for in Eq. (3), will prevent a fraction of the colloids from attachment [65, 67].
\nSeveral studies have highlighted the presence of Cryptosporidium oocysts in the surface and groundwater of the coastal city of Les Cayes [10, 14, 15]. The city is located at 18°34′00″ Northern Latitude and 72°21′00″ west Longitude on the Caribbean coast, on a coastal plain with high rainfall (over 2000 mm/year). Hydrogeologically, the basin of Les Cayes includes Plaine des Cayes and its surrounding mountains. They are drained by two principal rivers: Grand Ravine of the South and l’Acul du Sud along with many other secondary rivers. The basins of these two principal rivers are not very wide (65 and 75 km2) but benefit from a very abundant pluviometry which gives them particularly high specific outputs (70 and 55 ls/km2). Their low water output adds up to ~2.5 m3/s. In the case of l’Acul du Sud, this low water output is supported by an important karstic resurgence that drains the calcareous plates forming the southernmost buttresses of Massif de la Hotte’s summit, Pic Macaya. Similarly, Ravine du Sud, its principal waterway, has moderate water output of 4.96 m3/s and low water output of 1.31 m3/s. The watershed is shared between three distinct types of aquifers: alluvial aquifers with free waters, karstic aquifers, and carbonate aquifers (fissured and fragmented), from where resurgences and outputs vary [68].
\nThe alluvial aquifer of Plaine des Cayes is, hydrographically speaking, located in the Southwestern area of Haiti. Dominated by Massif de la Hotte which measures more than 2000 m in altitude, this area receives abundant rains at the mountaintops (more than 3000 mm/year) and gradually toward the coasts (1400 mm/year) with an average of 1900 mm/year. The renewable groundwater resources which are sustained through direct rain infiltration are concentrated in massive karstified limestones. Its aquifer constitutes, for the entire region, its most important directly exploitable underground water resource. The depth of the water table exceeds 40 m at the plain’s headwaters. In the high and moderate areas, the water table is free. In the low plain, the water table is restricted under an argillaceous covering. It is supplied by abundant and direct rain infiltration (1900 mm/year) on the high and moderate areas of the plain and by discharge from the mountains caused by floods [68].
\nSeveral factors play a role in explaining the presence of Cryptosporidium oocysts in the groundwaters of major cities in Haiti. These same factors can also be considered to elaborate on the hypotheses relating to the contamination of the city of Les Cayes’ nappe. They are (I) urban spaces of the country characterized by the absence of basic services, such that the collection and treatment of wastewater, the collection of solid waste, and the disposal of excreta and (II) the presence of latrines and septic tanks in alluvial and karstic formations and aquifers’ recharge zones. This situation can contribute to the contamination of water resources originating from human fecal discharge. (III) In Haiti, the control of water quality which is to be carried out by public agencies is not always assured [69], and (IV) chlorination remains the only mode of treatment applied to raw water intended for human consumption. This is despite the fact that Cryptosporidium oocysts are resistant to chlorine.
\nThe contamination of groundwater by Cryptosporidium oocysts has been the subject of several studies in which the authors have approached the laboratory on the behavior of Cryptosporidium oocysts under chemical conditions in saturated porous media. In order to appreciate the interactions between oocysts and soil (granular porous media) techniques based on the principles of colloid and surface chemistry were used.
\nViable Cryptosporidium oocysts purified using discontinuous saccharose were obtained from the National Institute of Agronomic Research (INRA). The oocysts were collected in fecal samples from naturally infected dairy calves and kept in an aqueous medium. They were stored (in the dark at 4°C) in potassium dichromate. The concentration of oocysts in the stock solution was about 2.0 × 107 oocysts/ml. They were used for laboratory experiments within 10 days of collection.
\nBatch equilibrium experiments were used to study the effect of chemical condition on the behavior of Cryptosporidium oocysts in porous media. These experiments were conducted with two electrolyte solutions: 0.003 M CaCl2 and 0.001 M NaBr. The Debye-Hückel equation was used Eq. (4):
\nIn this equation, αx represents the diameter of the ion nm, Zx the charge of the ion, and the ion X is I and the ionic strength of the solution. αCa = 0.6; αCl = 0.3.
\nFive points were selected. Each point on a random sample has been taken. The samples were collected manually at a depth between 40 and 80 cm (Figure 2). The soil consisted of 35.6% coarse sand, 7.1% fine sands, 25.4% fine silt, 6% coarse silt, and 25.9% clay (Table 1). The soils were air-dried at 35°C and sieved (2 mm), which were then stored at room temperature until used. The physicochemical characteristics of soil, such as pH, organic matter, clay, and CaCO3, and cation exchange capacity (CEC) were measured using standard analytical methods presented in Table 2 and were determined at the National Laboratory of Building and Public Works (LNBTP) Laboratory Analysis of Soil Arras (INRA) and the Institute for Radiological Protection and Nuclear Safety (IRSN).
\nLocation of sampling soil (Plaine des Cayes). Source: [16].
Particle size distribution of soil elements.
Physicochemical analysis of soil.
In the laboratory were added 4 g of soil with a suspension of oocysts in 12 bottles of crystal polystyrene sealed at a ratio of 1:10. To avoid changing the soil properties, the ionic strength of the solutions was adjusted by the addition of a 3 mM solution of calcium chloride (CaCl2) in 6 of the 12 bottle sand and a 100 mM solution of sodium bromide (NaBr) in the remaining 6. The batch tubes were placed vertically in racks on a vibration-free bench top after mixing to allow particles to settle. These were stirred for 24 h using a shaker table at 220 shakes per minute room temperature (23 ± 2°C). At the end of the stirring period, the tubes were placed in an upright position for 20 h. Since soil particles settled much faster than oocysts, due to their different densities, the solution at the top of each tube was removed and analyzed for oocyst amounts. The solution was analyzed by epifluorescence, and the determination of the amount of oocysts retained in the soil (qe) was calculated using Eq. (5):
\nwhere qe is the amount of oocysts absorbed per unit mass of solid expressed in L/g particles; C0, the initial concentration of suspension oocysts/L; Ce, the concentration of colloidal particles balanced oocysts/L; V, the volume of solution used in L; and m, the mass in grams of dry soil.
\nEnumeration of Cryptosporidium oocysts was conducted by the method of concentration and counting [70]. Oocyst concentration in the suspension was determined by monoclonal antibodies’ staining and epifluorescence. Each final suspension tube containing oocysts was placed in an Eppendorf standard micro test tube (2 ml volume). To this suspension, a volume of 250 μl fluorescein isothiocyanate (FITC)-conjugated monoclonal antibody was added, and the mixture was incubated for 30 min at 36°C. Following incubation, the stained sample was subjected to three rinses with phosphate buffered saline (pH 7.0, 0.1 mM) and centrifugally washed (4000×g) for removal of unattached antibodies. Then, 100-μl aliquots were placed on six-well chamber slides and fixed with 100% methanol.
\nTables 2 and 3 present the results of physicochemical analysis and particle size distribution of the soil of the study. These analyses indicate that the soil is characterized by fine particles; the size of the largest element is 2.50 mm. This fraction represents about 25% of the sample (Figure 3) and consisted of 35.6% coarse sand, 7.1% fine sands, 25.4% fine silt, 6% coarse silt, and 25.9% clay. According to [71] the largest CEC values were observed for the particle size analysis 15.2 (cmol/kg) of cation exchange capacity (CEC) and a large surface area due to the presence of silts and clays there for a large number of binding sites. The highest pH value was observed pH (8.52) probably due to the high calcium carbonate content (10.3 g/kg) and a high total calcium concentration (590 g/kg).
\nSoil characterization data used in this study.
Particle size distribution of the soil sample of the study. Source: [16].
The retention of C. parvum oocysts in soil was investigated in a series of batch equilibrium experiments. When combined with soil particles, and in presence of CaCl2 solution, the oocysts were removed from the suspension with a high rate (Table 4). The results summarized in Table 5 showed that oocysts, in the solution of NaBr, were transferred to the solid phase, suggesting a chemical environment favorable for the retention of oocysts. In addition the alkaline pH measured in the physicochemical analyses of the soil can increase the negative charge of zeta potential of oocysts and ultimately reduce the retention capacity of the soil. This could explain the presence of oocysts in groundwater. However, interactions between small colloid particles, the size of C. parvum oocysts, and small soil particles, typically the clay-sized fraction (<2 μm), depend to a large extent on electrostatic and other surface forces. Hence, surface charge, characterized as electrophoretic mobility or zeta potential, may govern the interaction between oocysts and soil particles [72]. Furthermore keeping the soil in its natural condition including clay particles and organic matter, which are important for adhesion and may be high in water suspensions in the environment. The association of oocysts with clay minerals has been attributed to high cation exchange capacity (CEC) of clays [72]. This could explain adsorption of oocysts on soil particles in the test batch. The high content of organic material obtained may affect the transport of pathogenic microorganisms in the soil to promote their retention [73]. Tufenkji et al. found that the oocyst removal efficiency depended on ionic strength and solution pH.
\nSamples | \nC0 (oocysts) | \nCe (oocyst/L) | \nVCaCl2 | \nMsol(g) | \nqe (oocyst.L/g) | \n
---|---|---|---|---|---|
Cc1 | \n1ml contains 40.000 | \n0 | \n39 | \n4 | \n400 | \n
Cc2 | \n2ml contains 80.000 | \n0 | \n38 | \n4 | \n800 | \n
Cc3 | \n3ml contains 120.000 | \n0 | \n37 | \n4 | \n1200 | \n
Cc4 | \n4ml contains 160.000 | \n0 | \n36 | \n4 | \n1600 | \n
Cc5 | \n5ml contains 200.000 | \n13 | \n35 | \n4 | \n2000 | \n
Cc6 | \n6ml contains 240.000 | \n13 | \n34 | \n4 | \n2400 | \n
Results of the test absorption studies between oocysts and soil with CaCl2.
Results of the test absorption studies between oocysts and soil with NaBr.
Other studies have reported that the ionic strength of the solution can influence the behavior of oocysts in the soil. Increasing the ionic strength of the water can alter the hydrophobic behavior of oocysts. According to the Debye-Hückel equation, the two solutions used for this study had different ionic strength. This distribution can be explained by the retention of oocysts in the solution containing CaCl2 (Table 4). While for NaBr, for the same ionic strength, the activity of Na+ ions is almost identical to the activity of Br− ions. There is a total transfer of the oocysts of the aqueous phase to the solid phase (Table 5). Indeed, changes in particle surface chemistry can have a significant influence on particle aggregation and adhesion [62, 74]. An improved understanding of the influence of oocyst purification and handling methods on oocyst surface chemistry and subsequent stability (vide infra) in water may lead to more effective oocyst removal in engineered processes [75]. Thomas et al. [76] recently reported that oocysts suspended in CaCl2 solutions had near-zero electrophoretic mobility. Brush et al. [62] have reported that variations in observed electrophoretic mobilities were caused by dissimilarities in purification techniques.
\nPollution dynamics of water resources refer in this chapter to all mechanisms resulting from anthropogenic activities that contribute to the degradation of water quality. This dynamic is the result of a series of actions to be closely linked to spatial development, population growth, and especially to inaction of the public authorities.
\nThe groundwater of the Plaine des Cayes is an important resource. From an ecological point of view, they represent a quantitatively large water reserve and contribute to the feeding of many lakes and rivers. In addition, they provide a significant part of the water supply for the population. The mode of supply is made from groundwater withdrawals with the installation of wells and boreholes and by capture of sources. Distribution takes place from networks, private connections, and public fountains. According to the data of the information, the water of the municipal system is fed by two boreholes with a flow of 66 l/s and an average production of the order of 10,134 m3/day.
\nWork carried out on the groundwaters of the country indicates their saline contamination [77] and the presence of pathogenic microorganisms (S/Committee on drinking water and human waste disposal, 1991) [78] and chemical pollutants [79]. In Haiti, several factors include the lack of a system for the collection and drainage of urban effluents, the absence of wastewater treatment plants, the discharge of septic tank effluents into karstic and alluvial formations, as well as the construction of latrines in the aforementioned geological formations, contribute to microbiological contamination of groundwater. A total of 110 fecal coliforms per 100 ml of water was detected in the water distributed by the public service of the city of Les Cayes (S/Committee on drinking water and human waste disposal, 1991).
\nBased on findings, it appears that the population of the city of Les Cayes resorts to the use of nonreturn latrines and WC with septic tanks, improved latrines, and with non-defined systems (defecation in open air) for the evacuation of their excreta. In the same manner, the people of Les Cayes use various channels for the evacuation of their solid waste: direct removal by the trucks and other discharges into waterways, wastelands, drains, etc. Quite often, the waste which undergoes putrefaction on garbage heaps as well as it does in trash bins produces an extremely toxic liquid called lixiviant. This situation can contribute to the contamination of the water table’s water resources through human fecal matter.
\nAll of these pose serious threats to water resources that are heavily exploited in the area through wells, springs, and boreholes. Groundwater is generally polluted by the excreta. People are digging latrines until they reach the surface of the water table. After a rise of a water table above the ground surface, the excreta can be mobilized by the runoff and can be dispersed all over the surface and can pose a significant pollution hazard. It is clear that solid waste and wastewater have as receptacle the aquatic ecosystems and greatly contribute to altering the quality of water resources.
\nRegarding cryptosporidiosis in Haiti, in the early 2000s, a series of environmental investigations had been conducted in Port-au-Prince and its surroundings, Les Cayes, and Cap-Haitien to identify human contamination sources. So far, these surveys consisted only in detecting Cryptosporidium oocysts in the environment by screening different types of water (surface water, groundwater, public water supplies) used by the population [10, 11, 12]. Cryptosporidiosis is one of the most frequent causes of diarrhea in Haiti. Transmission in young children, HIV-infected individuals, and people living in low socioeconomic conditions is probably due to consumption of water or food contaminated by Cryptosporidium oocysts. In tropical areas and under unfavorable socioeconomic conditions, cryptosporidiosis of the child is associated with a risk of prolonged diarrhea, malnutrition, and possibly psychomotor retardation and lectin-related mannose deficiency [80].Cryptosporidiosis is responsible for 17.5% of acute diarrhea cases in children <2 years old [81] and for 30% of HIV patients with chronic diarrhea [82]. Cryptosporidiosis is associated with Haitian children with malnutrition and lectin-related mannose deficiency. In a study conducted in Port-au-Prince, the presence of Cryptosporidium oocysts was shown in 158 people (a prevalence of 10.3%). Among these individuals, 56 out of 57 (98%) adults and 7 out of 36 (19%) children were HIV+. Genotyping identified three species: 59% C. hominis, 38% C. parvum, and 3% C. felis [12]. It is one of the leading causes of morbidity and mortality in people with AIDS [82]. Cryptosporidiosis is considered as a significant health problem in Haiti where cryptosporidiosis appears to be closely related to environmental issues. Water and sanitation are environmental issues to their very core and together constitute one of the top drivers of development. Water and sanitation provision have an impact on the health of the environment, through downstream pollution in particular.
\nPollution of water resources has a particular impact on the environment, especially since water is an essential component of the ecosystem. Water pollution affects air, soil, and plants. In this, the stagnation of wastewater in the open spaces generates foul odors which constitute an inconvenience for the population. This mode of management or disposal of wastewater causes inter alia disturbances in the nutrient cycle, changes in the structure and functioning of the biotic community, and a biological imbalance in aquatic ecosystems. The high pollutant loads contained in water surface are at the origin of eutrophication. In addition, the weakness of the collection system means that solid waste is dispersed in gullies in public squares, in public markets, and accumulates in open drainage channels contributing to the degradation of the environment. In addition, the accumulation of organic waste in the urban environment favors murine swarming, a reservoir of Cryptosporidium muris, a species recently found in humans in Peru [83]. Leachate from leachate is leaking into the drinking water system, with old and poorly maintained pipelines on the surface in many streets. These conditions of urban insalubrity explain the particularly high rate of contamination by Cryptosporidium oocysts of water for human consumption. The impact on the environment affects the attractiveness of the city of Les Cayes.
\nPollution of water resources in developing countries is a public health problem. This problem affects everyday life and the living standards of urban populations. In the urban areas of poor countries, uncontrolled population growth puts severe pressure on existing natural resources, particularly water resources, resulting in accelerated environmental degradation. In particular, the water resources of the city of Les Cayes are subject to various pressures on a daily basis as a result of poor sanitation and poor solid waste management. To understand the dynamics of water resource pollution by microorganisms, a study on the behavior of Cryptosporidium oocysts under chemical conditions was carried out. The objective of this study was to investigate the behavior of Cryptosporidium oocysts under chemical conditions and soil characteristics in saturated porous media and to describe also the water pollution dynamics of Cryptosporidium oocysts. Batch equilibrium tests were used to describe the partitioning of Cryptosporidium particles between solid and liquid phases and play a significant role in the oocyst removal from the pore fluid. Therefore it is necessary to reduce the level of exposure of the population of Les Cayes through the construction of livestock farms to prevent the free movement of animals in the city, the elimination of wild dumps, and the treatment of urban effluents, latrine sludge, and septic tanks. It is important to regularly monitor water quality (monitoring) and evaluate progress. Good management of water resources on our study site requires close collaboration between research institutes, water companies, and health authorities.
\nEarly approaches of artificial intelligence (AI) have sought solutions through formal representation of knowledge and applying logical inference rules. Later on, with having more data available, machine learning approaches prevailed which have the capability of learning from data. Many successful examples today, such as language translation, are results of this data-driven approach. When compared to other machine learning approaches, deep learning (deep artificial neural networks) has two advantages. It benefits well from vast amount of data—more and more of what we do is recorded every day, and it does not require defining the features to be learned beforehand. As a consequence, in the last decade, we have seen numerous success stories achieved with deep learning approaches especially with textual and visual data.
In this chapter, first a relatively short history of neural networks will be provided, and their main principles will be explained. Then, the chapter will proceed to two parallel paths. The first path treats text data and explains the use of deep learning in the area of natural language processing (NLP). Neural network methods first transformed the core task of language modeling. Neural language models have been introduced, and they superseded n-gram language models. Thus, initially the task of language modeling will be covered. The primary focus of this part will be representation learning, where the main impact of deep learning approaches has been observed. Good dense representations are learned for words, senses, sentences, paragraphs, and documents. These embeddings are proved useful in capturing both syntactic and semantic features. Recent works are able to compute contextual embeddings, which can provide different representations for the same word in different contextual units. Consequently, state-of-the-art embedding methods along with their applications in different NLP tasks will be stated as the use of these pre-trained embeddings in various downstream NLP tasks introduced a substantial performance improvement.
The second path concentrates on visual data. It will introduce the use of deep learning for computer vision research area. In this aim, it will first cover the principles of convolutional neural networks (CNNs)—the fundamental structure while working on images and videos. On a typical CNN architecture, it will explain the main components such as convolutional, pooling, and classification layers. Then, it will go over one of the main tasks of computer vision, namely, image classification. Using several examples of image classification, it will explain several concepts related to training CNNs (regularization, dropout and data augmentation). Lastly, it will provide a discussion on visualizing and understanding the features learned by a CNN. Based on this discussion, it will go through the principles of how and when transfer learning should be applied with a concrete example of real-world four-class classification problem.
Deep neural networks currently provide the best solutions to many problems in computer vision and natural language processing. Although we have been hearing the success news in recent years, artificial neural networks are not a new research area. In 1943, McCulloch and Pitts [1] built a neuron model that sums binary inputs, and outputs
A neuron that mimics the behavior of logical AND operator. It multiplies each input (x1 and x2) and the bias unit +1 with a weight and thresholds the sum of these to output 1 if the sum is big enough (similar to our neurons that either fire or not).
In 1957, Rosenblatt introduced perceptrons [2]. The idea was not different from the neuron of McCulloch and Pitts, but Rosenblatt came up with a way to make such artificial neurons learn. Given a training set of input-output pairs, weights are increased/decreased depending on the comparison between the perceptron’s output and the correct output. Rosenblatt also implemented the idea of the perceptron in custom hardware and showed it could learn to classify simple shapes correctly with 20 × 20 pixel-like inputs (Figure 2).
Mark I Perceptron at the Cornell Aeronautical Laboratory, hardware implementation of the first perceptron (source: Cornell University Library [3]).
Marvin Minsky who was the founder of MIT AI Lab and Seymour Papert together wrote a book related to the analysis on the limitations of perceptrons [4]. In this book, as an approach of AI, perceptrons were thought to have a dead end. A single layer of neurons was not enough to solve complicated problems, and Rosenblatt’s learning algorithm did not work for multiple layers. This conclusion caused a declining period for the funding and publications on AI, which is usually referred to as “AI winter.”
Paul Werbos proposed that backpropagation can be used in neural networks [5]. He showed how to train multilayer perceptrons in his PhD thesis (1974), but due to the AI winter, it required a decade for researchers to work in this area. In 1986, this approach became popular with “Learning representations by back-propagating errors” by Rumelhart et al. [6]. First time in 1989, it was applied to a computer vision task which is handwritten digit classification [7]. It has demonstrated excellent performance on this task. However, after a short while, researchers started to face problems with the backpropagation algorithm. Deep (multilayer) neural networks trained with backpropagation did not work very well and particularly did not work as well as networks with fewer layers. It turned out that the magnitudes of backpropagated errors shrink very rapidly and this prevents earlier layers to learn, which is today called as “the vanishing gradient problem.” Again it took more than a decade for computers to handle more complex tasks. Some people prefer to name this period as the second AI winter.
Later, it was discovered that the initialization of weights has a critical importance for training, and with a better choice of nonlinear activation function, we can avoid the vanishing gradient problem. In the meantime, our computers got faster (especially thanks to GPUs), and huge amount of data became available for many tasks. G. Hinton and two of his graduate students demonstrated the effectiveness of deep networks at a challenging AI task: speech recognition. They managed to improve on a decade-old performance record on a standard speech recognition dataset. In 2012, a CNN (again G. Hinton and students) won against other machine learning approaches at the Large Scale Visual Recognition Challenge (ILSVRC) image classification task for the first time.
Technically any neural network with two or more hidden layers is “deep.” However, in papers of recent years, deep networks correspond to the ones with many more layers. We show a simple network in Figure 3, where the first layer is the input layer, the last layer is the output layer, and the ones in between are the hidden layers.
A simple neural network with two hidden layers. Entities plotted with thicker lines are the ones included in Eq. (1), which will be used to explain the vanishing gradient problem.
In Figure 3,
Activation function is the element that gives a neural network its nonlinear representation capacity. Therefore, we always choose a nonlinear function. If activation function was chosen to be a linear function, each layer would perform a linear mapping of the input to the output. Thus, no matter how many layers were there, since linear functions are closed under composition, this would be equivalent to having a single (linear) layer.
The choice of activation function is critically important. In early days of multilayer networks, people used to employ
Eq. (1) shows how the error in the final layer is backpropagated to a neuron in the first hidden layer, where
Figure 4 shows the derivative of
Derivative of the sigmoid function.
Today, choices of activation function are different. A rectified linear unit (ReLU), which outputs zero for negative inputs and identical value for positive inputs, is enough to eliminate the vanishing gradient problem. To gain some other advantages, leaky ReLU and parametric ReLU (negative side is multiplied by a coefficient) are among the popular choices (Figure 5).
Plots for some activation functions. Sigmoid is on the left, rectified linear unit is in the middle, and leaky rectified linear unit is on the right.
Deep learning transformed the field of natural language processing (NLP). This transformation can be described by better representation learning through newly proposed neural language models and novel neural network architectures that are fine-tuned with respect to an NLP task.
Deep learning paved the way for neural language models, and these models introduced a substantial performance improvement over n-gram language models. More importantly, neural language models are able to learn good representations in their hidden layers. These representations are shown to capture both semantic and syntactic regularities that are useful for various downstream tasks.
Representation learning through neural networks is based on the distributional hypothesis: “words with similar distributions have similar meanings” [9] where distribution means the neighborhood of a word, which is specified as a fixed-size surrounding window. Thus, the neighborhoods of words are fed into the neural network to learn representations implicitly.
Learned representations in hidden layers are termed as distributed representations [10]. Distributed representations are local in the sense that the set of activations to represent a concept is due to a subset of dimensions. For instance, cat and dog are hairy and animate. The set of activations to represent “being hairy” belongs to a specific subset of dimensions. In a similar way, a different subset of dimensions is responsible for the feature of “being animate.” In the embeddings of both cat and dog, the local pattern of activations for “being hairy” and “being animate” is observed. In other words, the pattern of activations is local, and the conceptualization is global (e.g., cat and dog).
The idea of distributed representation was realized by [11] and other studies relied on it. Bengio et al. [11] proposed a neural language model that is based on a feed-forward neural network with a single hidden layer and optional direct connections between input and output layers.
The first breakthrough in representation learning was word2vec [12]. The authors removed the nonlinearity in the hidden layer in the proposed model architecture of [11]. This model update brought about a substantial improvement in computational complexity allowing the training using billions of words. Word2vec has two variants: continuous bag-of-words (CBOW) and Skip-gram.
In CBOW, a middle word is predicted given its context, the set of neighboring left and right words. When the input sentence “creativity is intelligence having fun” is processed, the system predicts the middle word “intelligence” given the left and right contexts (Figure 6). Every input word is in one-hot encoding where there is a vocabulary size (
CBOW architecture.
In Skip-gram, the system predicts the most probable context words for a given input word. In terms of a language model, while CBOW predicts an individual word’s probability, Skip-gram outputs the probabilities of a set of words, defined by a given context size. Due to high dimensionality in the output layer (all vocabulary words have to be considered), Skip-gram has higher computational complexity than CBOW (Figure 7). To deal with this issue, rather than traversing all vocabulary in the output layer, Skip-gram with negative sampling (SGNS) [13] formulates the problem as a binary classification where one class represents the current context’s occurrence probability, whereas the other is all vocabulary terms’ occurrence in the present context. In the latter probability calculation, a sampling approach is incorporated. As vocabulary terms are not distributed uniformly in contexts, sampling is performed from a distribution where the order of the frequency of vocabulary words in corpora is taken into consideration. SGNS incorporates this sampling idea by replacing the Skip-gram’s objective function. The new objective function (Eq. (3)) depends on maximizing
Skip-gram architecture.
Both word2vec variants produced word embeddings that can capture multiple degrees of similarity including both syntactic and semantic regularities.
A regular extension to word2vec model was doc2vec [14], where the main goal is to create a representation for different document levels, e.g., sentence and paragraph. Their architecture is quite similar to the word2vec except for the extension with a document vector. They generate a vector for each document and word. The system takes the document vector and its words’ vectors as an input. Thus, the document vectors are adjusted with regard to all the words in this document. At the end, the system provides both document and word vectors. They propose two architectures that are known as distributed memory model of paragraph vectors (DM) and distributed bag-of-words model of paragraph vectors (DBOW).
DM: In this architecture, inputs are the words in a context except for the last word and document, and the output is the last word of the context. The word vectors and document vector are concatenated while they are fed into the system.
DBOW: The input of the architecture is a document vector. The model predicts the words randomly sampled from the document.
An important extension to word2vec and its variants is fastText [15], where they considered to use characters together with words to learn better representations for words. In fastText language model, the score between a context word and the middle word is computed based on all character n-grams of the word as well as the word itself. Here n-grams are contiguous sequences of
The idea of using the smallest syntactic units in the representation of words introduced an improvement in morphologically rich languages and is capable to compute a representation for out-of-vocabulary words.
The recent development in representation learning is the introduction of contextual representations. Early word embeddings have some problems. Although they can learn syntactic and semantic regularities, they are not so good in capturing a mixture of them. For example, they can capture the syntactic pattern look-looks-looked. In a similar way, the words hard, difficult, and tough are embedded into closer points in the space. To address both syntactic and semantic features, Kim et al. [16] used a mixture of character- and word-level features. In their model, at the lowest level of hierarchy, character-level features are processed by a CNN; after transferring these features over a highway network, high-level features are learned by the use of a long short-term memory (LSTM). Thus, the resulting embeddings showed good syntactic and semantic patterns. For instance, the closest words to the word richard are returned as eduard, gerard, edward, and carl, where all of them are person names and have syntactic similarity to the query word. Due to character-aware processing, their models are able to produce good representations for out-of-vocabulary words.
The idea of capturing syntactic features at a low level of hierarchy and the semantic ones at higher levels was realized ultimately by the Embeddings from Language Models (ELMo) [17]. ELMo proposes a deep bidirectional language model to learn complex features. Once these features are learned, the pre-trained model is used as an external knowledge source to the fine-tuned model that is trained using task-specific data. Thus, in addition to static embeddings from the pre-trained model, contextual embeddings can be taken from the fine-tuned one.
Another drawback of previous word embeddings is they unite all the senses of a word into one representation. Thus, different contextual meanings cannot be addressed. The brand new ELMo and Bidirectional Encoder Representations from Transformers (BERT) [18] models resolve this issue by providing different representations for every occurrence of a word. BERT uses bidirectional Transformer language model integrated with a masked language model to provide a fine-tuned language model that is able to provide different representations with respect to different contexts.
In NLP, different neural network solutions have been used in various downstream tasks.
Language data are temporal in nature so recurrent neural networks (RNNs) seem as a good fit to the task in general. RNNs have been used to learn long-range dependencies. However, because of the dependency to the previous time steps in computations, they have efficiency problems. Furthermore, when the length of sequences gets longer, an information loss occurs due to the vanishing gradient problem.
Long short-term memory architectures are proposed to tackle the problem of information loss in the case of long sequences. Gated recurrent units (GRUs) are another alternative to LSTMs. They use a gate mechanism to learn how much of the past information to preserve at the next time step and how much to erase.
Convolutional neural networks have been used to capture short-ranging dependencies like learning word representation over characters and sentence representation over its n-grams. Compared to RNNs, they are quite efficient due to independent processing of features. Moreover, through the use of different convolution filter sizes (overlapping localities) and then concatenation, their learning regions can be extended.
Machine translation is a core NLP task that has witnessed innovative neural network solutions that gained wide application afterwards. Neural machine translation aims to translate sequences from a source language into a target language using neural network architectures. Theoretically, it is a conditional language model where the next word is dependent on the previous set of words in the target sequence and the source sentence at the same time. In traditional language modeling, the next word’s probability is computed based solely on the previous set of words. Thus, in conditional language modeling, conditional means conditioned on the source sequence’s representation. In machine translation, source sequence’s processing is termed as encoder part of the model, whereas the next word prediction task in the target language is called decoder. In probabilistic terms, machine translation aims to maximize the probability of the target sequence
This conditional probability calculation can be conducted by the product of component conditional probabilities at each time step where there is an assumption that the probabilities at each time step are independent from each other (Eq. (6)).
The first breakthrough neural machine translation model was an LSTM-based encoder-decoder solution [19]. In this model, source sentence is represented by the last hidden layer of encoder LSTM. In the decoder part, the next word prediction is based on both the encoder’s source representation and the previous set of words in the target sequence. The model introduced a significant performance boost at the time of its release.
In neural machine translation, the problem of maximizing the probability of a target sequence given the source sequence can be broken down into two components by applying Bayes rule on Eq. (5): the probability of a source sequence given the target and the target sequence’s probability (Eq. (7)).
In this alternative formulation,
Bandanau et al. [20] propose an attention mechanism to directly connect to each word in the encoder part in predicting the next word in each decoder step. This mechanism provides a solution to alignment in that every word in translation is predicted by considering all words in the source sentence, and the predicted word’s correspondences are learned by the weights in the attention layer (Figure 8).
Sequence-to-sequence attention.
Attention is a weighted sum of values with respect to a query. The learned weights serve as the degree of query’s interaction with the values at hand. In the case of translation, values are encoder hidden states, and query is decoder hidden state at the current time step. Thus, weights are expected to show each translation step’s grounding on the encoder hidden states.
Eq. (8) gives the formulae for an attention mechanism. Here
The success of attention in addressing alignment in machine translation gave rise to the idea of a sole attention-based architecture called Transformer [21]. The Transformer architecture produced even better results in neural machine translation. More importantly, it has become state-of-the-art solution in language modeling and started to be used as a pre-trained language model. The use of it as a pre-trained language model and the transfer of this model’s knowledge to other models introduced performance boost in a wide variety of NLP tasks.
The contribution of attention is not limited to the performance boost introduced but is also related to supporting explainability in deep learning. The visualization of attention provides a clue to the implicit features learned for the task at hand.
To observe the performance of the developed methods on computer vision problems, several competitions are arranged all around the world. One of them is Large Scale Visual Recognition Challenge [22]. This event contains several tasks which are image classification, object detection, and object localization. In image classification task, the aim is to predict the class of images in the test set given a set of discrete labels, such as dog, cat, truck, plane, etc. This is not a trivial task since different images of the same class have quite different instances and varying viewpoints, illumination, deformation, occlusion, etc.
All competitors in ILSVRC train their model on ImageNet [22] dataset. ImageNet 2012 dataset contains 1.2 million images and 1000 classes. Classification performances of proposed methods were compared according to two different evaluation criteria which are top 1 and top 5 score. In top 5 criterion, for each image top 5 guesses of the algorithm are considered. If actual image category is one of these five labels, then the image is counted as correctly classified. Total number of incorrect answers in this sense is called top 5 error.
An outstanding performance was observed by a CNN (convolutional neural network) in 2012. AlexNet [23] got the first place in classification task achieving 16.4% error rate. There was a huge difference between the first (16.4%) and second place (26.1%). In ILSVRC 2014, GoogleNet [24] took the first place achieving 6.67% error rate. Positive effect of network depth was observed. One year later, ResNet took the first place achieving 3.6% error rate [25] with a CNN of 152 layers. In the following years, even lower error rates were achieved with several modifications. Please note that the human performance on the image classification task was reported to be 5.1% error [22].
CNNs are the fundamental structures while working on images and videos. A typical CNN is actually composed of several layers interleaved with each other.
Convolutional layer is the core building block of a CNN. It contains plenty of learnable filters (or kernels). Each filter is convolved across width and height of input images. At the end of training process, filters of network are able to identify specific types of appearances (or patterns). A mathematical example is given to illustrate how convolutional layers work (Figure 9). In this example, a 5 × 5 RGB image is given to the network. Since images are represented as 3D arrays of numbers, input consists of three matrices. It is convolved with a filter of size 3 × 3 × 3 (height, weight, and depth). In this example, convolution is applied by moving the filter one pixel at a time, i.e., stride size = 1. First convolution operation can be seen at Figure 9a. After moving the kernel one pixel to the right, second convolution operation can be seen at Figure 9b. Element-wise multiplication
Convolution process. (a) First convolution operation applied with filter W1. Computation gives us the top-left member of an activation map in the next layer. (b) Second convolution operation, again applied with filter W1.
Convolution depicted in Figure 9 is performed with one filter which results in one matrix (called activation map) in the convolution layer. Using
Formation of a convolution layer by applying n number of learnable filters on the previous layer. Each activation map is formed by convolving a different filter on the whole input. In this example input to the convolution is the RGB image itself (depth = 3). For every further layer, input is its previous layer. After convolution, width and height of the next layer may or may not decrease.
Pooling layer is commonly used between convolutional layers to reduce the number of parameters in the upcoming layers. It makes the representations smaller and the algorithm much faster. With max pooling, filter takes the largest number in the region covered by the matrix on which it is applied. Example input, on which 2 × 2 max pooling is applied, is shown in Figure 11. If the input size is
Max pooling.
Standard CNNs generally have several convolution layers, followed by pooling layers and at the end a few fully connected layers (Figure 12). CNNs are similar to standard neural networks, but instead of connecting weights to all units of the previous layer, a convolution operation is applied on the units (voxels) of the previous layer. It enables us scale weights in an efficient way since a filter has a fixed number of weights and it is independent of the number of the voxels in the previous layer.
A typical CNN for image classification task.
What we have in the last fully connected layer of a classification network is the output scores for each class. It may seem trivial to select the class with the highest score to make a decision; however we need to define a loss to be able to train the network. Loss is defined according to the scores obtained for the classes. A common practice is to use softmax function, which first converts the class scores into normalized probabilities (Eq. (10)):
where
An example of softmax classification loss calculation. Computed loss, Li, is only for the ith sample in the dataset.
The ability of a model to make correct predictions for new samples after trained on the training set is defined as generalization. Thus, we would like to train a CNN with a high generalization capacity. Its high accuracy should not be only for training samples. In general, we should increase the size and variety of the training data, and we should avoid training an excessively complex model (simply called overfitting). Since it is not always easy to obtain more training data and to pick the best complexity for our model, let’s discuss a few popular techniques to increase the generalization capacity.
This is a term,
Another way to prevent overfitting is a technique called dropout, which corresponds to removing some units in the network [26]. The neurons which are “dropped out” in this way do not contribute to the forward pass (computation of loss for a given input) and do not participate in backpropagation (Figure 14). In each forward pass, a random set of neurons are dropped (with a hyperparameter of dropping probability, usually 0.5).
Applying dropout in a neural net.
The more training samples for a model, the more successful the model will be. However, it is rarely possible to obtain large-size datasets either because it is hard to collect more samples or it is expensive to annotate large number of samples. Therefore, to increase the size of existing raw data, producing synthetic data is sometimes preferred. For visual data, data size can be increased by rotating the picture at different angles, random translations, rotations, crops, flips, or altering brightness and contrast [27].
Short after people realized that CNNs are very powerful nonlinear models for computer vision problems, they started to seek an insight of why these models perform so well. To this aim, researchers proposed visualization techniques that provide an understanding of what features are learned in different layers of a CNN [28]. It turns out that first convolutional layers are responsible for learning low-level features (edges, lines, etc.), whereas as we go further in the convolutional layers, specific shapes and even distinctive patterns can be learned (Figure 15).
Image patches corresponding to the highest activations in a random subset of feature maps. First layer’s high activations occur at patches of distinct low-level features such as edges (a) and lines (b); further layers’ neurons learn to fire at more complex structures such as geometric shapes (c) or patterns on an animal (d). Since activations in the first layer correspond to small areas on images, resolution of patches in (a) and (b) is low.
In early days of observing the great performance of CNNs, it was believed that one needs a very large dataset in order to use CNNs. Later, it was discovered that, since the pre-trained models already learned to distinguish some patterns, they provide great benefits for new problems and new datasets from varying domains. Transfer learning is the name of training a new model with transferring weights from a related model that had already been trained.
If the dataset in our new task is small but similar to the one that was used in pre-trained model, then it would work to change the classification layer (according to our classes) and train this last layer. However, if our dataset is also big enough, we can include a few more layers (starting from the fully connected layers at the end) to our retraining scheme, which is also called fine-tuning. For instance, if a face recognition model trained with a large database is available and you would like to use that model with the faces in your company, that would constitute an ideal case of transferring the weights from the pre-trained model and fine-tune one or two layers with your local database. On the other hand, if the dataset in our new task is not similar to the one used in pre-trained model, then we would need a larger dataset and need to retrain a larger number of layers. An example of this case is learning to classify CT (computer tomography) images using a CNN pre-trained on ImageNet dataset. In this situation, the complex patterns (cf. Figure 15c and d) that were learned within the pre-trained model are not much useful for your new task. If both the new dataset is small and images are much different from those of a trained model, then users should not expect any benefit from transferring weights. In such cases users should find a way to enlarge the dataset and train a CNN from scratch using the newly collected training data. The cases that a practitioner may encounter from the transfer learning point of view are summarized in Table 1.
Very similar dataset | Very different dataset | |
---|---|---|
Very little data | Replace the classification layer | Not recommended |
A lot of data | Fine-tune a few layers | Fine-tune a larger number of layers |
Strategies of transfer learning according to the size of the new dataset and its similarity to the one used in pre-trained model.
To emphasize the importance of transfer learning, let us present a small experiment where the same model is trained with and without transfer learning. Our task is the classification of animals (four classes) from their images. Classes are zebra, leopard, elephant, and bear where each class has 350 images collected from the Internet (Figure 16). Transfer learning is performed using an AlexNet [23] pre-trained on ImageNet dataset. We have replaced the classification layer with a four-neuron layer (one for each class) which was originally 1000 (number of classes in ImageNet). In training conducted with transfer learning, we reached a 98.81% accuracy on the validation set after five epochs (means after seeing the dataset five times during training). Readers can observe that accuracy is quite satisfactory even after one epoch (Figure 17a). On the other hand, in training without transfer learning, we could reach only 76.90% accuracy even after 40 epochs (Figure 17b). Trying different hyperparameters (regularization strength, learning rate, etc.) could have a chance to increase accuracy a little bit more, but this does not alleviate the importance of applying transfer learning.
Example images for each class used in the experiment of transfer learning for animal classification.
Training and validation set accuracies obtained (a) with transfer learning and (b) without transfer learning.
Deep learning has become the dominant machine learning approach due to the availability of vast amounts of data and improved computational resources. The main transformation was observed in text and image analysis.
In NLP, change can be described in two major lines. The first line is learning better representations through ever-improving neural language models. Currently, self-attention-based Transformer language model is state-of-the-art, and learned representations are capable to capture a mix of syntactic and semantic features and are context-dependent. The second line is related to neural network solutions in different NLP tasks. Although LSTMs proved useful in capturing long-term dependencies in the nature of temporal data, the recent trend has been to transfer the pre-trained language models’ knowledge into fine-tuned task-specific models. Self-attention neural network mechanism has become the dominant scheme in pre-trained language models. This transfer learning solution outperformed existing approaches in a significant way.
In the field of computer vision, CNNs are the best performing solutions. There are very deep CNN architectures that are fine-tuned, thanks to huge amounts of training data. The use of pre-trained models in different vision tasks is a common methodology as well.
One common disadvantage of deep learning solutions is the lack of insights due to learning implicitly. Thus, attention mechanism together with visualization seems promising in both NLP and vision tasks. The fields are in the quest of more explainable solutions.
One final remark is on the rise of multimodal solutions. Till now question answering has been an intersection point. Future work are expected to be devoted to multimodal solutions.
IntechOpen publishes different types of publications
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