Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"10409",leadTitle:null,fullTitle:"Soil Erosion - Current Challenges and Future Perspectives in a Changing World",title:"Soil Erosion",subtitle:"Current Challenges and Future Perspectives in a Changing World",reviewType:"peer-reviewed",abstract:"Soil erosion is a major environmental issue with a worldwide impact and direct and indirect effects on soil productivity and consequently on human survival. Although a natural process, soil erosion has increased significantly due to human intervention, especially in the last centuries, through diverse activities such as intensive agriculture, overgrazing, urban sprawl, deforestation, and industrial and mining activities. Presently, soil erosion and degradation promoted by human action have reached extreme levels, necessitating urgent measures to promote soil conservation and rehabilitation. This book presents perspectives on soil erosion occurring in different parts of the world as well as some successful initiatives and strategies for soil conservation and rehabilitation.",isbn:"978-1-83962-300-4",printIsbn:"978-1-83962-299-1",pdfIsbn:"978-1-83962-304-2",doi:"10.5772/intechopen.91595",price:119,priceEur:129,priceUsd:155,slug:"soil-erosion-current-challenges-and-future-perspectives-in-a-changing-world",numberOfPages:152,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"100d8afa798aacadca65a149a4f902b5",bookSignature:"António Vieira and Silvio Carlos Rodrigues",publishedDate:"May 12th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10409.jpg",numberOfDownloads:2749,numberOfWosCitations:0,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:3,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:5,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 8th 2020",dateEndSecondStepPublish:"June 29th 2020",dateEndThirdStepPublish:"August 28th 2020",dateEndFourthStepPublish:"November 16th 2020",dateEndFifthStepPublish:"January 15th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"103627",title:"Prof.",name:"António",middleName:null,surname:"Vieira",slug:"antonio-vieira",fullName:"António Vieira",profilePictureURL:"https://mts.intechopen.com/storage/users/103627/images/system/103627.png",biography:"Antonio Vieira is a geographer with a Ph.D. in Geography from the University of Coimbra, Portugal (2009). He is an assistant professor in the Department of Geography, University of Minho (UM), Portugal, and an integrated member of the Communication and Society Research Centre (CECS), UM. He is a member of several scientific organizations, including the Portuguese Association of Geomorphologists (Chair from 2017 to 2019) and the Portuguese Association of Risk, Prevention and Security (vice-president since 2015). He is also a member of FuegoRED and coordinator of the FESP International Network. Dr. Vieira’s main research includes granitic geomorphology, geomorphological heritage and changes in land use, GIS and remote sensing, and their application to land use, geomorphological heritage, and soil erosion following forest fires and mitigation measures.",institutionString:"University of Minho",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Minho",institutionURL:null,country:{name:"Portugal"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"320125",title:"Dr.",name:"Silvio Carlos",middleName:null,surname:"Rodrigues",slug:"silvio-carlos-rodrigues",fullName:"Silvio Carlos Rodrigues",profilePictureURL:"https://mts.intechopen.com/storage/users/320125/images/system/320125.png",biography:"Silvio Carlos Rodrigues obtained a Ph.D. in Physical Geography from the University of São Paulo, Brazil, in 1998 and is a full professor at the Institute of Geography, Universidade Federal de Uberlândia (UFU), Brazil. He served as chair of the UGB in 2005–2006, secretary of the COMLAND of the IGA in 2010–2012, a full member of the CNPQ Advisory Committee on Physical Geography (CA-SA) in 2008–2010, and a full member of the Chamber of Natural Resources (CRA) with FAPEMIG in 2014–2016. Dr. Rodrigues is editor-in-chief of Sociedade & Natureza and a member of UNESCO\\'s EOLSS International Editorial Council. He is also a reviewer for the Brazilian Journal of Geomorphology, Caminhos de Geografia, and Environmental Earth Sciences. He has experience in geosciences, with emphasis on geomorphology, acting mainly on the following themes: geomorphology, geomorphological cartography, soil erosion, integrated environmental analysis, and geomorphological mapping.",institutionString:"Federal University of Uberlândia,",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"676",title:"Soil Degradation",slug:"soil-degradation"}],chapters:[{id:"74451",title:"RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes",doi:"10.5772/intechopen.95055",slug:"rgb-spectral-indices-for-the-analysis-of-soil-protection-by-vegetation-cover-against-erosive-process",totalDownloads:401,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The vegetation cover plays a fundamental role in protecting the soil from erosive processes. Many researchers have developed investigations for the calculation of the RUSLE C Factor, with the use of operating bands in the near infrared. With the current advances in Geospatial Technologies, there are a good number of RGB airborne sensors in Unmanned Aerial Vehicles (UVA). The objective of this chapter is to evaluate some RGB indexes, proposed in the literature, for the protection of the soil from erosive processes by vegetation cover, in a region with a high agricultural vocation. The methodology consisted of capturing RGB images in an area of the Ecuadorian coastal region and calculating in thematic indices, within the visible one, which offer the possibility of quickly differentiating vegetation from other types of coverage on the ground. The evaluation allowed to define which indexes present the best results and adaptation to the type of crop or plant mass mapped, and to propose their use for zoning of risk of erosion under the agro-ecological conditions of the study area.",signatures:"Henry Antonio Pacheco Gil and Argenis de Jesús Montilla Pacheco",downloadPdfUrl:"/chapter/pdf-download/74451",previewPdfUrl:"/chapter/pdf-preview/74451",authors:[{id:"326877",title:"Ph.D.",name:"Henry Antonio",surname:"Pacheco Gil",slug:"henry-antonio-pacheco-gil",fullName:"Henry Antonio Pacheco Gil"},{id:"343735",title:"Dr.",name:"Argenis",surname:"de Jesús Montilla Pacheco",slug:"argenis-de-jesus-montilla-pacheco",fullName:"Argenis de Jesús Montilla Pacheco"}],corrections:null},{id:"75239",title:"Spatial Estimation of Soil Erosion Risk Using RUSLE/GIS Techniques and Practices Conservation Suggested for Reducing Soil Erosion in Wadi Mina Catchment (Northwest, Algeria)",doi:"10.5772/intechopen.96190",slug:"spatial-estimation-of-soil-erosion-risk-using-rusle-gis-techniques-and-practices-conservation-sugges",totalDownloads:363,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"To meet the pressing water needs in Algeria, the state has put in place a strategy consisting of the creation of hydraulic infrastructure for the mobilization of surface water resources. In fact, 74 dams are currently in operation; these structures are silting up at a rapid pace, generating an estimated annual loss of 45 million m3. Sidi Mhamed Benaouda dam located in the Oranian hill, with a water capacity of respectively 241 million m3 plays a crucial economic role in this region. The protection of this dam against erosive processes is a pressing economic goal. To do this, the RUSLE/GIS approach was used to map the erosive hazard. The results obtained in the Mina catchment, following a subdivision of 1315 homogeneous land parcels, show a total annual loss of 60 million tons/year with an average loss of 11.2 t/ha/year. About 50% of the catchment area was predicted to have very low to low erosion risk, with soil loss between 0 and 7.4 t/ha/year. Erosion risk is moderate over 13.9% of the catchment, where calculated soil loss is between 7.4 and 12 t/ha/year. Erosion risk is high to dangerous over 36.1% of the catchment, where calculated soil loss is more than 12 t/ha/year. According to this study, it appeared clearly that we must intervene quickly by using reliable and effective conservation techniques.",signatures:"Ahmed Benchettouh, Sihem Jebari and Lakhdar Kouri",downloadPdfUrl:"/chapter/pdf-download/75239",previewPdfUrl:"/chapter/pdf-preview/75239",authors:[{id:"308992",title:"Prof.",name:"Sihem",surname:"Jebari",slug:"sihem-jebari",fullName:"Sihem Jebari"},{id:"314333",title:"Dr.",name:"Ahmed",surname:"Benchettouh",slug:"ahmed-benchettouh",fullName:"Ahmed Benchettouh"},{id:"325116",title:"Prof.",name:"Lakhdar",surname:"Kouri",slug:"lakhdar-kouri",fullName:"Lakhdar Kouri"}],corrections:null},{id:"74771",title:"Remote Sensing and GIS-Based Soil Loss Estimation Using RUSLE in Bahir Dar Zuria District, Ethiopia",doi:"10.5772/intechopen.95393",slug:"remote-sensing-and-gis-based-soil-loss-estimation-using-rusle-in-bahir-dar-zuria-district-ethiopia",totalDownloads:535,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The severity of soil loss in the Ethiopian highlands has been increased from time to time. Hence, the assessment of soil erosion using models is very important for planning successful and sustainable soil management. This study was conducted in Bahir Dar Zuria district, Ethiopia with aiming to quantify the amount of soil loss using the GIS-based RUSLE (Revised Universal Soil Loss Equation) model. Based on the study, the most pronounced RUSLE factor that increases soil erosion was the slope length (L) and slope steepness (S). Compared with other land uses, bare land and cropland in the higher slopes were more vulnerable to erosion. As expected slope and soil losses have a direct relationship. About 80% of the study area experienced annual soil loss of less than 1.2 ton/ha/yr. Conversely, soil loss was very high for slopes greater than 30%. This indicated that slope has a great impact on regulating soil loss. The annual soil loss for cropland, vegetation, grassland, and degraded land was 19.05, 8.78, 8.82, and 71.16 ton/ha/yr., respectively. This is to means that land use land cover have a strong relationship with the amount of soil loss. The same land cover with different slopes have different soil loss amount. It was found that lack of vegetative cover during the critical period of rainfall, expansion of croplands, and absence of support practices increase soil erosion. Thus, the application of stone lines, contour tillage, terraces, and grass strip barriers are suggested to break the slope length into shorter distances, reducing overland flow velocity and soil erosion. Moreover, improving the awareness of society to reduce the illegal cutting of trees and apply conservation practices to reduce soil erosion in their farmland is very essential.",signatures:"Nurhussen Ahmed Mohammed and Desale Kidane Asmamaw",downloadPdfUrl:"/chapter/pdf-download/74771",previewPdfUrl:"/chapter/pdf-preview/74771",authors:[{id:"286238",title:"Dr.",name:"Desale Kidane",surname:"Asmamaw",slug:"desale-kidane-asmamaw",fullName:"Desale Kidane Asmamaw"},{id:"323673",title:"MSc.",name:"Nurhussen Ahmed",surname:"Mohammed",slug:"nurhussen-ahmed-mohammed",fullName:"Nurhussen Ahmed Mohammed"}],corrections:null},{id:"74627",title:"Determination of the Most Priority Conservation Areas Based on Population Pressure and Erosion Hazard Levels in Lesti Sub-Watershed, Malang Regency, Indonesia",doi:"10.5772/intechopen.95386",slug:"determination-of-the-most-priority-conservation-areas-based-on-population-pressure-and-erosion-hazar",totalDownloads:324,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In a watershed, the Erosion Hazard Level (EHL) is usually associated with erosion rate and existing soil solum. In Lesti Sub-Watershed, erosion rate increases every year due to erosivity factor, erodibility, the length and slope, as well as crop factor and land conservation action. Analysis of erosion associated with population pressure has not been much discussed in the Lesti Sub-Watershed. This topic needs to be explored given that the erosion rate that affects sedimentation in the Sengguruh Reservoir, as an outlet of the Lesti Sub-Watershed, cannot be separated from the population activity therein. The population activity and the choice of use of land suppress the land so that it affects the carrying capacity of the watershed. Measuring land strength is usually based on the value of existing population pressure and its effect on vulnerability or erosion hazard level. This study seeks to assess the relationship between erosion hazard level and population pressure, as well as to determine the priority conservation areas in the Lesti Sub-watershed. The research approach uses a mixed method. The results shows that from 12 sub-districts in Lesti Sub-watershed there is 1 sub-district which has high population pressure as well as severe EHL. This sub-district is the most priority area for environmental conservation.",signatures:"Andi Setyo Pambudi",downloadPdfUrl:"/chapter/pdf-download/74627",previewPdfUrl:"/chapter/pdf-preview/74627",authors:[{id:"323619",title:"M.Sc.",name:"Andi Setyo",surname:"Pambudi",slug:"andi-setyo-pambudi",fullName:"Andi Setyo Pambudi"}],corrections:null},{id:"74327",title:"The Impacts of Soil Degradation Effects on Phytodiversity and Vegetation Structure on Atacora Mountain Chain in Benin (West Africa)",doi:"10.5772/intechopen.93899",slug:"the-impacts-of-soil-degradation-effects-on-phytodiversity-and-vegetation-structure-on-atacora-mounta",totalDownloads:363,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Atacora mountain is a particular ecosystem of West Africa where soil degradation occurs. The present study assessed the impacts of physical soil degradation on vegetation in the Beninese portion of this mountain chain. Phytosociological surveys were carried out along line transects from plain to summit within 22 plots of 30 m x 30 m. Based on indicators of physical soil degradation each plot was classified into one soil degradation class (Light, Moderate, High or Extreme). Impacts on plant diversity were assessed by comparing the floristic composition of soil degradation classes with the index of similarity of Jaccard. Variations between soil degradation classes of species richness, species chorological types, species life forms and species dispersal were also tested using a discriminant analysis combined with ANOVA. The Multi-Response Permutation Procedures analysis was used to pairwise compare the soil degradation classes based on the cover data of the species lists. All soil degradation classes were dissimilar, depending on the floristic composition. Discriminant analysis and ANOVA performed on biodiversity indicators had shown that species richness, and the number of regional species, phanerophytes and sarcochory decreased along the increasing degradation gradient in contrast to the number of species with wide distribution, therophytes and sclerochory. With regard to vegetation structure, the results had shown that only moderately and highly degraded soils presented the similar vegetation type. Physical soil degradation induced modification of floristic composition, phytodiversity loss and modification of vegetation structure. These results showed that the soil degradation gradient corresponds to a vegetation disturbance gradient.",signatures:"Farris Okou, Achille Assogbadjo and Brice Augustin Sinsin",downloadPdfUrl:"/chapter/pdf-download/74327",previewPdfUrl:"/chapter/pdf-preview/74327",authors:[{id:"209358",title:"Prof.",name:"Brice Augustin",surname:"Sinsin",slug:"brice-augustin-sinsin",fullName:"Brice Augustin Sinsin"},{id:"325174",title:"Ph.D.",name:"Farris",surname:"Okou",slug:"farris-okou",fullName:"Farris Okou"},{id:"326994",title:"Prof.",name:"Achille",surname:"Assogbadjo",slug:"achille-assogbadjo",fullName:"Achille Assogbadjo"}],corrections:null},{id:"75436",title:"Erosion Control Success Stories and Challenges in the Context of Sustainable Landscape Management, Rwanda Experience",doi:"10.5772/intechopen.96267",slug:"erosion-control-success-stories-and-challenges-in-the-context-of-sustainable-landscape-management-rw",totalDownloads:336,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The Government of Rwanda sets up a conducive policy environment to invest in several development initiatives. Agriculture sector as the main contributor in the economic development received supports to sustainably manage Rwandan hilly landscape, dominantly ranging from 5 to 55% slope gradient. Intensive erosion control interventions confronted with different approaches have been introduced in the country such as participatory landscape management, (participatory) integrated watershed management and site-located intervention without any specified approach. This chapter intends to describe and evaluate the impacts of these previous approaches used in Rwanda in order to retrieve the success stories and encountered challenges as lessons learnt in the future interventions for optimizing land productivity in a sustainable manner. Participatory landscape approach in Gishwati area was a success story in protecting degraded lands and generating ecosystem benefits. It leads to more sustainable natural resources management from participatory planning up to implementation which addressed the frequent landslides, erosion and flooding while sustainably exploit the land to the profit of local farmers in the livelihoods. About 6,600 ha of lands have been successfully protected with full-packaged bench terraces, rangeland blocks and forest regeneration. This participatory approach also helped to relocate people from high risk zones to other safe places and build capacities of farmers through farm-livestock cooperatives. On the other side, Nyanza and Karongi sites under LWH project also emphasized strong evidences how land husbandry technologies (terraces) efficiently reduced erosion risks and improved farmers’ livelihoods. Lands were made productive with implementation of bench terraces on 3212 and 2673 hectares respectively for the two selected sites. However, challenges were observed from technical and socio-economic contexts which might have caused farmers to abandon or under-exploit the terraced lands. Finally, the chapter suggests to scale up the participatory landscape management approach which supports the involvement of farmers’ communities in the process.",signatures:"Jules Rutebuka",downloadPdfUrl:"/chapter/pdf-download/75436",previewPdfUrl:"/chapter/pdf-preview/75436",authors:[{id:"324918",title:"Dr.",name:"Jules",surname:"Rutebuka",slug:"jules-rutebuka",fullName:"Jules Rutebuka"}],corrections:null},{id:"76192",title:"Biochar: A Sustainable Approach for Improving Soil Health and Environment",doi:"10.5772/intechopen.97136",slug:"biochar-a-sustainable-approach-for-improving-soil-health-and-environment",totalDownloads:427,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Current agriculture faces multiple challenges due to boom in food demand and environmental concerns. Biochar is increasingly being recognized by scientists and policy makers for its potential role in carbon sequestration, reducing greenhouse gas emissions, renewable energy, waste mitigation and as a soil amendment. The purpose of this review is to provide a balanced perspective on the agronomic and environmental impacts of biochar amendment to soil. Application of biochar to soil can play a significant role in the alteration of nutrients dynamics, soil contaminants as well as microbial functions. Therefore, strategic biochar application to soil may provide agronomic, environmental and economic benefits. Recent findings also supported that in order to enhance crop yield, improve soil quality and soil health, biochar has proven significant role as fertilizer and soil conditioner respectively.",signatures:"Shreya Das, Samanyita Mohanty, Gayatri Sahu, Mausami Rana and Kiran Pilli",downloadPdfUrl:"/chapter/pdf-download/76192",previewPdfUrl:"/chapter/pdf-preview/76192",authors:[{id:"329835",title:"Assistant Prof.",name:"Gayatri",surname:"Sahu",slug:"gayatri-sahu",fullName:"Gayatri Sahu"},{id:"340032",title:"Ms.",name:"Shreya",surname:"Das",slug:"shreya-das",fullName:"Shreya Das"},{id:"355301",title:"Dr.",name:"Samanyita",surname:"Mohanty",slug:"samanyita-mohanty",fullName:"Samanyita Mohanty"},{id:"355302",title:"Ms.",name:"Mausami",surname:"Rana",slug:"mausami-rana",fullName:"Mausami Rana"},{id:"355303",title:"Mr.",name:"Kiran",surname:"Pilli",slug:"kiran-pilli",fullName:"Kiran Pilli"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3854",title:"Environmental Risk Assessment of Soil Contamination",subtitle:null,isOpenForSubmission:!1,hash:"88e43f7e0affb0c1ba3eccf8675e10f2",slug:"environmental-risk-assessment-of-soil-contamination",bookSignature:"Maria C. 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1. Introduction
This article has two primary purposes: the first is to provide the industry with an evaluation of the importance of AI development as a force for change and the second to create an internal basis for the Swedish Innovation program PiiA’s future development efforts, within which AI can be described as the next phase of industry’s digitalisation. Both these objectives are naturally compatible with the overall ambition of the report: to reach our target group of industry leaders and to serve as a source of knowledge for ongoing activities within relevant companies.
Technological, industrial development is awash with grand ambitions that have turned into mere passing fads and costly dead ends. With this in mind, throughout our work in assessing the development of AI, we have endeavoured to take into account the magnitude and direction of different vectors of change. On the one hand, we have attempted to understand the power of demand for AI by assessing the economic impacts at a macro level. We have focused on productivity and qualitative values at various stages of industry value systems. On the other hand, we have attempted to assess the range of available technologies by analysing initiatives taken on a global scale and through focused academic research. We have also put considerable effort into understanding the major commercial—or applied—forces that are crucial to development, both in the short and medium term.
We have also strived to place AI development in the context of current systemic developments, as characterised by the ‘platformisation’ of company IT resources. By this we mean the transfer of automation and IT support to the cloud—a trend that is creating new competitive dynamics. Finally, we have attempted to translate this big picture into real impacts on the factory floor and to revisit well-known concepts such as organisational development which—with the help of the raw power of AI technology—have the potential to make the previously impossible, possible.
The project was a collaboration between PiiA and Blue Institute, with valuable input from Blue Institute’s network of CEOs and industry leaders on all levels. A big thank you is extended to everyone who contributed to this study.
2. The market
The computerisation of global industry began in earnest in the 1980s. The use of microprocessors made it possible to automate in new and efficient ways, and the process, automotive and electronics industry made significant productivity gains. Now, the world is entering a new technological and economic paradigm in the form of digitalisation, the first wave of which has already transformed the media and communications industries. In a second wave of change, the financial sector and trade will be transformed, and, under the third wave, the wider industry will be transformed. At the same time, artificial intelligence is emerging as the next—and probably most significant—stage of digitalisation.
For the manufacturing industry, this will mean that many companies in subcontracting arrangements will have access to cost-effective technology that allows for further automation and productivity increases. The impacts of AI for the process industry will not be yet another emptying of factory floors, but rather an opening of the way to achieve levels of process development that were previously unobtainable. AI is also expected to bring new levels of integration to the entire value system, on the way to achieving the ultimate vision of self-organisation. It may also change the structure of the industrial landscape; concepts such as ‘ecosystems’ and ‘platforms’ are fast becoming commonplace descriptions, even in traditional industries.
Our current stage of development could be described as ‘increasing algorithmisation’ (see Figure 1). From a developmental context, it represents a megatrend that is both supporting human beings—and enabling us to be replaced by computers. The trend began with the mainframes of the 1960s and continued with the microprocessor revolution of the 1980s and 1990s. Then in the 2000s, came the scalability, mobility and cost-effectiveness of digital platforms. Now, with AI and machine learning emerging as the next phase, the pace of development is set to increase further.
Figure 1.
AI and machine learning effectively add an extra ‘gear’ that will allow for increasingly advanced algorithms that increase efficiency and create new customer value within the industry. Source: Blue Institute (2019).
Demand for industrial AI is growing as the understanding of the value that the technology can potentially release grows. Various technological developments, which have been taken place over the past decade unbeknownst to the general public, are now coming to fruition and can directly be viewed within the context of analyses of potential economic effects and, increasingly, real-life business cases and investments.
These changes have the potential to produce significant economic impacts for the global industry, and they may be particularly marked in the context of societal challenges related to population growth, climate and the environment.
The million-dollar question is: Will we see an explosion of AI, and its disruptive establishment across the global industry? Or, will this, still somewhat unwieldy, technology lead to more sporadic changes in the short term? Either way, our considered assessment is that AI is here to stay, that AI truly has the potential to change the world and industry, and that AI will be looked back on as a real revolution for the production economy.
In this section, we will look at the forces underlying supply-and-demand development trends for AI within the industry. A functioning market dynamic is a crucial prerequisite for ongoing industrial transformation pressure, something that we aim to highlight in this study by addressing three issues:
What are the expected value-creating effects of AI?
Will AI development and the supplier system be able to meet the demand for AI technology that is arising from this potential value creation?
Is this development sustainable, or are we seeing a ‘hype’ phase which will eventually fade, with the actual market breakthrough set to occur several years into the future?
In addition to these issues, this report illustrates, from various perspectives, how the impacts of AI will benefit the industry at the system/platform and operational development levels. The second part of the report will provide an in-depth study of the possibilities and challenges of AI technology.
2.1 Demand
We start this section by looking at the demand side of things (see Figure 2), as well as discussing the stance companies might take. This includes examining more significant developments that will lead to a future digital economy based on business ecosystems and digital platforms. We also introduce the concept of ‘best practice’ and provide an orientation model for individual companies wanting to assess their position and preparedness for change.
Figure 2.
The market for AI technical solutions and machine learning is expected to grow by 40% per year, while demand within the industry is expected to be driven by the significant potential value gains that can be created using the technology.
2.1.1 Substantial value effects within production systems will drive demand for AI
Our fundamental hypothesis is that demand for AI within the industry will correlate with the value that can be extracted from production via more effective analytical tools. We assume that the expected growth effects within the sector will lead to activities at the company level, which in turn will drive demand for AI technology.
According to the Vinnova study Artificial Intelligence in Swedish Business and Society, there is evidence to suggest that the general growth potential within value creation might be realised twice as fast in an economy with extensive AI utilisation, compared to one with limited utilisation.
The many dynamic effects of AI development and the changing regulations around it, also come into play, and these are expected to produce growth effects for the world economy. AI will also contribute to systemic effects, as business ecosystems and digital platforms are developed that transform the manufacturing industry into an information industry. These virtual value systems are decoupled from physical systems and so allow for new organisational models that echo the transformation that media, finance and commerce sectors are already undergoing.
Several studies have attempted to estimate the economic effects of AI at the macro-level; in this work, we have incorporated insights from three reports by Accenture [1], McKinsey Global Institute [2] and PwC [3]. According to consulting company PwC, AI’s contribution to the global economy in 2030 will amount to an estimated USD 15.7 trillion. This means that in 2030, with the impact of AI, global GDP will be 14% higher than it would be without AI or the equivalent of China and India’s combined GDPs.
The productivity impact corresponds to USD 6.6 trillion, while USD 9.1 trillion is expected to be produced from impacts on the consumer side. PwC’s analysis also includes areas such as trade, transport, finance and health care.
Consulting company Accenture believes AI’s global economic impact will be equivalent to USD 4.8 trillion in increased profitability during the period up to 2022, which does not contradict McKinsey’s or PwC’s analyses that have other timeframes.
In its report, Notes from the AI frontier: Modelling the impact of AI on the world economy, consulting firm McKinsey Global Institute (MGI) calculates that the effects of AI in all of the report’s sectors will generate an impact of between USD 3.5–5.8 trillion or when expanded to include all available advanced analysis methods, on top of machine learning, USD 9.5–15.4 trillion.
When limited to the resource, process and manufacturing industry’s value system, it is in the range of USD 1.7–2.3 trillion according to estimates in this study, or 3–6% per year of the global industrial sector’s total assets.
Industrial productivity improvements are estimated to amount to 1.2% per year until 2030 or in the order of USD 1 trillion. Comparisons can also be made with other major technological shifts. During the nineteenth century, the steam engine increased labour productivity by an estimated 0.3% per year (although the disruptive effect eventually became quite considerable). The robotisation of the industry in the 1990s produced a 0.4% increase, and the consequences of IT development during the 2000s are expected to deliver a 0.6% increase. AI has at least twice the inherent potential.
‘Added value of between SEK 22 and 45 billion per year could be unlocked for PiiAs industries’.
Placed in a Swedish context and related to PiiAs target industries, we estimate that added value of between SEK 22 and 45 billion could be unlocked per year. For PiiAs sectors this represents an average increase of between SEK 3 billion and SEK 7 billion per industry, of which approximately half would be productivity-related, with value also unlocked at the consumer level, through factors such as quality, time savings and better-targeted offerings.
The purpose of the comparisons above is more to illustrate the order of magnitude involved than to present precise figures. Even if the cited studies were to be greatly exaggerated, the effects would certainly be significant. We conclude that the movement that has now been set in motion has few parallels in history in terms of change potential. For companies and businesses, it means there will be few, if any, players who can afford to pass up the competitive improvements that AI will eventually deliver.
And there is a good reason to prepare in advance for the coming changes. Looking from a broader perspective, lopsided distribution of AI development is likely, with an ever-increasing gap between the performance of various countries, companies and workers. In terms of countries, China and the United States are the two nations that currently account for the majority of all AI-related activities, and they are thus the best positioned. Developed industrial economies such as Germany, Japan and Canada and smaller commercial economies such as Sweden and Finland are well placed. They should also be motivated by low productivity-development gains in recent years.
Economies with more modest foundations, such as India, Italy and countries in Southeast Asia, generally have less favourable conditions. Still, they could use their particular strengths within specific categories to build specialised AI capabilities. However, developing economies with low investment capacity, weak skills and weak digital infrastructures run the risk of falling behind.
In this section, we will work out how the concepts of AI, ecosystems, networks and platforms are interconnected. And how they contribute to value-creating market dynamics.
2.1.2 Systemic effects from digital platforms and business ecosystems
Among the most considerable value-creating effects are expected to come from changes at the system level. As previously mentioned, we foresee development in a form that can best be described as the transformation of the manufacturing industry towards becoming an information industry. This should not be read as a prediction of the demise of the production economy—preferably one that business leaders will have to manage two logical frameworks.
This development has also been called the ‘platform economy’. In this section, we will work out the differences and connections between the concepts of AI, ecosystems, networks and platforms. We will also outline how they contribute to value creation and, as a result, to the demand for AI and market dynamics.
The connections between the concepts of ‘networks’ and ‘platforms’ lend themselves to being described with metaphors from biological ecosystems. These ‘ecosystems’ can be thought of as robust, scalable architectures that can automatically solve complex, dynamic problems, including self-organisation, self-governance, sustainability and scalability.
In the business ecosystem, there is a network logic between the companies involved which, in turn, is supported by a digital ecosystem characterised by a distributed peer-to-peer network model. The latter can also be described as a digital platform that makes relationships between companies and other organisations in the business network possible through transactions and technical support. A curated ecosystem reflects the balance between competition and collaboration in an open, dynamic and free market.
The term ‘business ecosystem’ was first mentioned in a 1993 article in Harvard Business Review [4]. The article presents the idea that companies not only belong to industries but are parts of business ecosystems that extend across different industrial and knowledge sectors. The term digital business ecosystem originated when the word ‘digital’ was added to the business ecosystem concept as a reference to the socio-economic development made possible through information and communication technology [5].
The classic effects of network logic affect how the number of users in the network influences the value development for each user, i.e. the so-called ‘positive-network effect’. Adverse network effects, on the other hand, occur in poorly managed networks that reduce value development for each user. The positive network effect is, of course, the foremost and most sought-after competitive advantage within network logic. Consequently, the critical prerequisite for effective networks is to use digital platforms and other features to increase in size, thus increasing the value generated via network effects.
2.1.3 Platform: a transformative concept
The concept of ‘platform’ is thus a transformative one with the potential to bring about significant changes within business logic, economics, and society at large. Any company for whom information on factors such as supply-and-demand status, customer needs, trends, and willingness to pay is an essential asset is very likely to participate in the platform revolution.
The concept of ‘platformisation’, then, is used as a strategy for operating multi-faceted platforms and connecting buyers, sellers and other stakeholders, without necessarily owning the products or services being sold.
In textbooks, traditional linear value systems are likened to value chains or pipes [6]. Platforms represent a transformation from linear structures to a matrix complex of relationships between connected producers and customers. They collaborate through the resources, properties and services provided by the platform’s technology.
This development has, in its first wave, affected sectors where the product itself is information, such as the media, entertainment and financial sectors. The concept of ‘scale without mass’ [7] is vital. Unlike physical products, which have high fixed costs plus substantial marginal costs that are reduced per unit should production be scaled up, digital products, for the most part, have near-zero fixed costs and marginal costs. With the internet as a distribution network, it is, therefore, possible for companies with small fixed assets and a low number of employees to quickly scale up to become international businesses.
The empirical evidence suggests that the platform model vastly outperforms the linear value system when the right conditions are in place. Examples of such successes can be found among today’s major tech companies, including Google, Amazon, Microsoft and Apple—all of which are also known as mega-platforms [8].
2.1.4 How digital platforms are changing traditional industry
The uniting factor in both network and platform logics is the need to match and facilitate connections between producers and buyers, regardless of the type of goods being exchanged (see Figure 3). Industry organisations will change as a consequence of the competitive advantages that platforms can provide within meeting places.
Figure 3.
Physical value chains and the flow of raw materials, materials and products are complemented by equally elementary data streams that enable digital twins at different levels, including entire processes and value systems. The technology and connectivity that makes physical production, as well as the digital twin, describes the digital platform (or ecosystem). Data streams and twins make it possible to create digital business ecosystems, i.e. meeting places where markets are created in new ways. Source: Blue Institute, 2019.
Platforms make it possible to bring new value for customers with low marginal costs to existing physical products—to achieve scale without mass—and we are already getting an early indication of how the industry will separate physical production logic from virtual data-driven logic. The automotive industry is experiencing shrinking margins in vehicle manufacturing and is developing business models that address mobility while being based on AI platforms.
The industrial technology suppliers of tomorrow will not just sell hardware but will also develop into connected suppliers of efficiency and quality within production systems based on analysis, with machine learning delivered in collaboration between human and artificial intelligence. The process industry will not just sell materials, but also data on these materials based on AI analyses that increase the quality and efficiency of the manufacturing industry.
Uncoupling physical assets from the value they create also means that certain products can be marketed as services in the market for best possible use through greatest value creation, rather than being linked to a specific owner. The result is that both efficiency and value can increase—dramatically, in some cases.
Platforms also have the potential to change the cost structures and pricing in physical production. Once someone launches a digital AI platform that allows for trade and provides free marginal production capacity on a larger scale, purchasing prices for semi-manufactured products will theoretically fall at the same rate at which the available capacity is filled. Such a day is probably not too far away. There are also estimates that digital platforms that match labour to needs (once again with the help of AI) have the potential to increase global GDP by 2% by 2025 and create 72 million full-time jobs [9]. It is not surprising that a new word is being increasingly used: algorithm economics. In the same way that apps have changed people’s communication with machines, AI algorithms will revolutionise the development between the machines.
We conclude that while the business economics doctrine will undoubtedly continue to exist once the resources, process and manufacturing industry becomes an information industry, how it is followed will be revolutionised.
We are in the process of leaving an industrial era in which scalability in supply-side economics has been the single biggest driver; as more units are efficiently produced, the cost per unit diminishes. This has driven corporate mergers, globalised supply chains, oligopolies and monopolies. The largest companies have the most massive volumes and cost advantages that are difficult for smaller competitors to achieve. In the transformation into a digital and AI-driven platform economy in which physical products are paired with digital, scalable services, similar constellations will also be created through large-scale demand economics.
The demand economy is driven by aggregated and visible demand, social networks, app-development and other phenomena that make networks bigger and more valuable to all users. The impacts will be just as difficult to absorb as within the large-scale production economy. Scale within the demand economy is the foundation for positive network effects and therefore a future driver of the global economy.
The advantages of platforms over linear value systems will lead to the disruption and dissolution of many industrial businesses. The continuous improvement of physical value chains will be complemented by developments through which data streams will become equally important for competitiveness. These data streams will pave the way for digital twins to be created of objects, machines, processes and, ultimately, the whole value system—all physical production and logistics. Advances are being made towards achieving the vision of self-organising value systems, one of the core concepts of Industry 4.0. The physical world and the computer world will become two sides of the same coin.
One problem that needs to be solved in this context is the ownership of data. Who owns the data that companies generate? Today, there is no real regulatory framework, and the various industries collecting data are uncertain about how much, and what kind of data they should share with other companies. In answer to this, initiatives are underway in several computer labs, and we see examples of public, open laboratories, in areas such as forestry and traffic data. This is one approach to systematising data collection. But better-defined structures and agreed-upon standards are needed to define, describe and share data safely.
Ultimately, the transformation for classic industrial companies involves managing two different logics; the massive scale of the supply economy is not going away, and at the same time the ability to create demand with economies of scale is becoming a significant differentiating competitive factor. The skill lies in being able to handle both.
The use of the term ‘platform’ as found in ‘digital platform’ can be traced right back to the very early days of computerisation and the concept of ‘computing platforms’. From the first mainframe computers, via the client-server model with its personal computers and networks, and into the era of digitisation, the word has been used to define hardware platforms and software platforms, or, to put it another way, general operating systems. The three development paradigms mentioned above are in turn called the first, second and third platforms. We are now in the era of the third platform, more complex and more intertwined than ever before, and characterised by the fact that computing power is found almost everywhere. Ready for use by people and objects, through the Internet of Things.
2.1.4.1 Industry case study: Mälarenergi Smart Flows: optimisation of the district heating network
Through its Smarta Flöden (Smart Flows) project, Swedish company Mälarenergi aims to use AI to optimise the production of district heating based on streaming data. Its goal is to avoid overproduction while continuing to provide reliable district heating to customers. The project receives funding from PiiA and is a collaboration between Mälarenergi, RISE Västerås, Mälardalen University, ABB, Sigholm and Evothings Labs. The Smart Flows project combines learning systems with Industrial IoT and cloud services to enable fully automatic optimisation of industrial process flows. The project is also part of the larger-scale work to create a ‘City Control Room’.
Mälarenergi AB is a commercial company that supplies electricity, district heating, water, district cooling and fast communication solutions, primarily within the Mälardals region. The company also sells electricity to private and corporate customers throughout Sweden. The Group is owned by the City of Västerås and has a turnover of approximately SEK 3 billion.
2.1.4.1.1 The challenge
Measuring, understanding and predicting flows of materials, gases and liquids are central to many process industries, and, as a result, these flows are often subject to continuous optimisation. Air flows in mine ventilation and distribution flows for wastewater are good examples of process flows. Optimising and automating these flows has the potential to produce considerable savings in energy and total costs, which in turn can create positive environmental effects.
Process flows are rarely in a constant state, and instead are continuously developing as demand changes, new infrastructure is expanded, or as customers come and go. As a result, there is a clear need to make the industrial systems adaptable and teachable. District heating systems are an example of a system in which changes take time.
“The hope is to eventually be able to create a hybrid solution between the learning system and the physical model.”
From the time that production is increased at a plant, it can take several hours before consumers’ scores of kilometres away can feel the benefits. But by using real-time data and a connected distribution network, plants will be able to anticipate needs and quickly make decisions about increased or decreased production. The Smart Flows project uses the Internet of Things and cloud services from Microsoft Azure to manage historical and close-to-real-time data dynamically.
The project also has an operational development dimension through which the goal is for customers to be able to buy services in the form of comfortable indoor temperatures which be individualised.
2.1.4.1.2 The experience
The system takes in more than 15,000 properties, ranging from private homes to commercial properties and industries. During the project’s first year, significant time has been spent collecting data. Measurement data from all Mälarenergi’s district heating plants over the past three years have been collected. The majority of values are hourly, but where possible, 15-minute values have also been sampled. Mälarenergi appointed an internal analysis group to analyse the more than one billion data points gathered.
Meanwhile, in parallel, a project team has conducted several user studies, creating profiles for different user categories to understand the operation’s visualisation and analysis needs.
The first predictions made with the AI system were based on the factors of distribution time, weather, and social behaviour. The most successfully generated predictions have been made concerning the heating needs of the building itself. Here, the weather is a substantial contributing factor, and as long as there are good weather forecasts, predictions can be created that adequately reflect reality. The social behaviour of customers is the least reliable factor. If a single customer showers or runs a lot of hot water for a specific period, there will be a massive potential impact on district heating. A variety of methods are being tested to improve the accuracy of social behaviour modelling.
The project has also tested physical models of the district heating network. The results show that the model can predict the dynamic/moving behaviour of the district heating system in terms of heat dissipation. The hope is to eventually be able to create a hybrid solution between the learning system and the physical model, a model with validation capability for the real-time learning system.
One example is where the learning system might want to send a certain amount of heat/water. The physical model can then say whether or not it is physically possible to do so and calculate whether this would result in the water reaching the customers on time. If it proves to be possible according to the laws of physics, then the sending of the heat can proceed. If it is not physically possible, then the exercise serves as a valuable input to the learning system. The algorithm will learn that this exact procedure is not feasible for the next time.
During the period 2019–2021, the goal is to have a complete learning system in place covering all Mälarenergi’s district heating customers, and that is compliant with industrial standards.
Source: PiiA, Mälarenergi, RISE.
2.1.5 In search of best practice
The old business wisdom to ‘follow the money’ takes on new resonance for companies using AI as a transformation tool. In sectors where added value has traditionally been created through marketing and sales, it may be prudent to focus AI effort in the same areas. If operational excellence is crucial—as is the case within the process and manufacturing industry—then there are good reasons to invest heavily in AI for the supply chain and production processes, but also to develop new products and to add new customer value. And in terms of primary resources and materials, there is the potential to increase value for customers who can contribute to top-line growth.
Returning to the corporate and economic side of things, it is time to pose the question of whether the business case for AI is settled and whether now is the right time for significant investment. There may be little question of the way things are heading when we look at the bigger picture, but as we all know, the devil is in the details. In general, machine learning is a powerful technology that so far requires specialised knowledge and incredibly careful preparations, tests and validations before it can deliver.
Against this background, the primary purpose of this paper is to engage and contribute to Swedish industry’s practical knowledge and preparedness for action, and to seek out best practice. While we are well aware of the current advantages and disadvantages of the technology, future developments may progress very quickly.
One model that is widely used within the Blue Institute and PiiA are the S-curve (see Figure 4) [10]. In the context of the digitally-driven industrial shift that we currently find ourselves in, we are preparing to leave the S-curve’s initial innovation phase with its lab experiments and industry pilots, to move into the next stage with trailblazers leading the way in seeking a best practice that delivers results; we call this the ‘best practice’ phase.
Figure 4.
Digital development with AI as an essential component leaves the pure innovation phase. After that comes the ‘Best Practice’ phase in which trailblazers dare to experiment and inspire others. Source: Blue Institute, 2019.
Best practice, in turn, lays the foundation for an accelerated transformation of an industry. Experience from previous technological shifts has shown the power of good role models. For example, over just a few years in the 1980s, the Swedish pulp and paper industry became the world leader in computerised automation. One explanation for this is that company leaders were inspired by their Swedish colleagues in the sector and shared their experiences. When industry leaders dare to take the lead, rewards await in the form of competitive advantages. If you can get others to follow, industrial benefits can be created on a large scale.
Examining the development of applied industrial AI and using the empirical evidence we have through, among other things, PiiAs project base, we can identify three types of companies in different stages of the curve (see Figure 5):
The majority of companies—an estimated 70%—belong in the ‘aspiring for insights’ category. They realise that change is coming, but still lack readiness and ability, which must therefore be developed.
We are now seeing the rise of the ‘innovation pilot’ category to which an estimated 20% of businesses belong. They are engaged and have dared to take the first steps down the path to applied industrial AI or are receiving help in making preparations for applications on a larger scale.
The ‘accelerator’ category includes a small group of pioneers, estimated to be less than 10% of companies, who have found their own best practice solutions and are ready to scale up and transform their businesses using AI as a tool.
Figure 5.
Three typical development steps for implementing AI in an industrial context. From creating insight into opportunities and challenges, to the more full-scale transformation of a company’s processes. Source: Blue Institute, 2019.
In this report, we return to three prerequisites for succeeding with applied AI in industry, examining them from different perspectives:
Leadership and adaptability involve creating appropriate change teams with the skills needed for the task ahead. Still, it also involves taking into account the job changes that AI will eventually lead to. This includes having the ability to collaborate between humans and machines—collaborative intelligence—and understanding the consequences this has on the organisation and working models. To put the question of jobs into perspective, an estimated 14% of the global workforce will need to change their job duties as a consequence of AI [1].
Also crucial is data, both from an ownership perspective and a quality perspective. Information is the raw material of AI technology, which is then converted into money with the help of algorithms. The final fundamental prerequisite is security and risk management. These challenges also feature in Vinnova’s 2018 study [11], and we will look more closely into these aspects in the second part of the report: The Technology.
In the race to the top of the S-curve, it’s crucial to address the challenges that crop up along the way. This starts with the ‘aspiring for insights’ category, gaining the insights they need to understand the opportunities AI presents and to know the conditions within their own companies. Such companies may need to analyse their data management, organisational data strategy and the value of their data. It may also be a good idea to lay the groundwork for rules and relevant policies for data security management within the company. This might include minimising the risk of data breaches, as well as security measures for people and assets. It is increasingly common for policies for managing data in connection with AI applications to address ethics and the risks of skewed, biased data sets when learning specific analysis models.
Those in the ‘innovation pilots’ category, meanwhile, have gained insights and probably also received help with organising their data and testing machine learning on one or more suitable processes. Within PiiA’s empirical data, we see companies at this stage that are trying out different methods and suppliers to gain knowledge and decision-making expertise for the next step, which we have referred to as ‘accelerators’ in this model.
Those in the ‘accelerator’ group now need to increase the pace of implementation and, therefore, transfer the responsibility for transformation to their line organisations, along with appropriate expert support. These development steps also come with increasing demands on the ability of companies to manage job transformation, as well as data as a strategic asset, as well as the security and ethical issues around data usage.
A study by McKinsey examined 400 AI applications in 19 different sectors. It found that in 69% of cases, AI was a means to improve existing, more straightforward, analytical methods. Entirely new applications accounted for only 16% of cases. In comparison, the remaining 15% of cases were unable to benefit from deep learning technology for reasons, including a lack of data.
McKinsey Global Institute, Notes from the AI frontier: Applications and value of deep learning, 2018.
2.1.6 Organisational development
Applying AI is essentially a matter of organisational development. It’s a skill for which different corporate leaders will have different aptitude levels. A driven individual is sure to see AI for the powerful tool that it stands to become and will also have the ability to create teams in which creativity, process knowledge, and a knowledge of tools, methods and good leadership all make a difference.
Most complex processes in the supply chain stand to benefit from artificial intelligence and machine learning. In simplified terms, the methodology can be divided into four parts:
Data collection, preparation, and training of the model.
Using the trained model for analysis and prediction.
Using the analysis for augmentation, i.e. enhancing or increasing human abilities.
Using the analysis for automation, which can now be developed to new levels. It additionally becomes possible to introduce automation into areas where its implementation was previously seen as too complicated or expensive.
AI and automation are often used as interchangeable concepts, but the underlying technology differs. Automation describes systems that are programmed to perform specific repetitive tasks, such as an industrial robot which repeats the same step over and over again or a word-processing program which can repetitively perform what previously manual tasks were. AI systems, on the other hand, are designed to find patterns, to learn from experiences and to make consistent decisions. AI does not need specifically programmed paths to determine how it should behave in different situations (see Figure 6). Together, AI and automation may represent the next step in streamlining various processes within industry, whether these be in production or administration. Automated machines use data; AI understands data; so, they complement each other.
Figure 6.
The streamlining of the industry’s supply chains is enabled using machine learning (ML) which makes predictions of process behaviour based on collated data. These predictions can then be used to enhance people’s abilities or to automate processes fully. Source: Blue Institute, 2019.
Augmented reality (AR), for example, has advantages in situations where people tend to perform poorly at consistently monitoring processes. AR can provide support when such monotonous tasks transition into critical business situations. AI-supported AR also helps warehouse workers and truck drivers to keep track of goods and products. AR can help process operators carry out routine checks on machines and processes, as well as providing support to service personnel, and speeding up emergency troubleshooting. The technology makes it possible to provide enhanced expert assistance remotely to production facilities far from the technology supplier’s nearest expert centre.
2.1.7 The AI flywheel
We conclude this section with a metaphor for the successful application of AI—the data or AI flywheel. It is a concept that nods to the fact AI that in a business context needs an ‘inertia mass’—a combination of data, knowledge and energy that all interact with each other for a project to be successful.
From a business and company perspective, it is essential to create mighty inertia masses, within which machine learning innovations (as a part of the company) fuel other operations, which in turn can become products or services, which in turn can provide leverage effects through additional AI, and so on. Those companies that are first to succeed in this area will be the few surprising ‘platform’ companies capable of growing with information – scale without mass (see also the section ‘How digital platforms are changing traditional industry’).
The AI Flywheel concept is often associated with Amazon Web Services (AWS). Amazon likes to use the word flywheel to describe how different parts of its business function as a perpetual motion machine, within which more data yields better products, more customers, and even more data and so on. The company’s machine learning platforms create momentum throughout the organisation. Offering machine learning to outsiders as a paid service is in itself profitable. The fact that such projects also generate data provides even more leverage.
AWS grew by 40% in the first quarter of 2019 with an operating margin of 29%. Somehow, Amazon has cracked one of the business world’s riddles; how to create small innovative teams within a much larger (bureaucratic) business. Agile teams that learn quickly develop competence in many AI-related areas and then spread the knowledge to the rest of the organisation in useful, coherent and collaborative ways that create value throughout the organisation. It is impressive. But how can traditional industry adopt the logic of the AI flywheel? In this context, we will content ourselves with exploring the concept of the basic organisation—the team—which makes more prominent strategies possible.
The most common mistake when companies adopt AI is that they start focused on the technology and not the business needs. Hiring data scientists and giving them access to data to build ‘something interesting’ is very likely to lead to a dead end.
As discussed, teams with different competencies are required, of which the four basic ones are: product or production managers (who can describe, in detail, which problem to solve), systems engineers (who know what data can be used), computer scientists (who know how to build useful models), and cross-discipline specialists. This latter group can be called DevOps engineers or translators (Development-Operations is a term borrowed from the IT industry and agile development, while translators is a term borrowed from McKinsey). In this context, this invaluable category of people (usually made up of engineers) create commitment and knowledge and can move relatively freely between production, process and operational development, as well as customer’s needs and preferences.
The members of the team use different tools and work together to solve the group’s challenges. But each is ideally a person with the ability to scale up their operations and fill the flywheel metaphor with power and torque.
Of course, more or less similar competence combinations have always been used to develop products and operations. What is new is machine learning technology and a requirement for expertise in this area, at least for the time being. For companies that are advancing in the AI world, it is necessary to take serious steps to create such pilot teams. For companies that are scaling up, the accelerators, such teams are still needed but should push up against the boundaries and away from regular business operations. In such circumstance, there is a potential to achieve flywheel effects at the company level.
Mining Magazine asked 115 mining companies where they saw the most significant opportunities for AI. The answers are interesting, and the priorities identified would resonate with those in a range of sectors outside mining.
Better decision management and error minimisation.
Understanding market trends and customer behaviour.
Discovering mineral deposits.
Autonomous vehicles and drilling units.
Automated monitoring of health and safety risks.
Increased productivity.
Production planning.
Maintenance planning.
Automation and support for regulatory and team compliance.
Rescheduling after unforeseen events.
2.2 Supply
We will now move on to analyse the supply side of things by assessing technology development and the supplier system. We will do this firstly through an overview of the area of AI development with a brief conceptual summary, followed by describing the structure and strategic challenges of the supplier industry. The chapter ends with our conclusions about the effects on the market system.
Behind the applied development of digital technology for the industry lie significant investments. In simple terms, they can be described as three development hubs, each of which, according to Blue Institute estimates, accounts for about one-third of an impressive SEK 1.5 trillion invested globally in R&D each year (see Figure 7):
The driving forces for the first hub—private-public innovation collaborations—consist of national ambitions along with industry insights on the values at stake in the fourth industrial revolution. These have generated significant investments through which private and public national capital unites in various programs around the world. Industrie 4.0 in Germany is among the most renowned. In Sweden, the Strategic Innovation Programs have been established focusing on selected growth areas. By and large, these private, public investments amount to 500 billion annually.
The second development hub consists of large tech companies’ annual R&D investments in the construction of cloud services and investments in AI. The Big Five—Apple, Alphabet, Microsoft, Facebook, Amazon, plus IBM—are estimated to invest nearly 500 billion a year.
The third hub consists of the traditional ICT industry plus automation providers.
A significantly more fragmented industry, but a further estimated 500 billion is invested in research and development.
Figure 7.
The applied development of digital technology for the industry is being driven by historical investments in what can schematically be described as three fields of influence or developmental hubs. Each of these accounts for about one-third of an impressive 1.5 trillion invested in technology development each year. Source: Blue Institute.
Development projects are now starting to leave the laboratories on all fronts and to arrive on the market, first as innovation projects, then as best practice, and then, eventually, as robust commercial offerings.
The driving forces behind each hub are essential: a return is required on those large investments; standardisation work is about to yield results, and the world’s industrial leaders are beginning to understand the vast sums and value at stake with the impending transformation of the industry. Last but not least, a dynamic is arising as the three developmental hubs start to propel each other, as development results are released onto the market. This means that the momentum for the whole system increases further.
2.2.1 Significant breakthroughs in AI technology
Significant breakthroughs in AI are coming to the public’s attention more and more frequently. This is occurring across all application areas. Initiatives within foundational research and product development are producing visible results, and the development curve is growing steeper.
The most mundane and yet revolutionary example is personal assistants such as Alexa, Siri, and Google Assistant, which are continually learning more, and making themselves known via our phones and calendars.
Estonia wants to make its government and judiciary as efficient as possible and so is developing an AI model to act as a judge in minor legal cases within where the value of the dispute is less than EUR 7000.
The OpenAI development institute recently unveiled a pre-developed language model (GPT-2) that can generate realistic texts in different kinds of style and prose. The text robot is so powerful that the Institute is refraining from releasing the fully trained model due to the risk that it may contribute to the spread of so-called ‘fake text’.
In March 2019, the Google company DeepMind presented a model capable of diagnosing complex eye diseases in real-time. In thirty seconds, Google cloud algorithms can provide a detailed prognosis with the same precision as world-leading eye specialists.
In January 2019, a research group at Columbia University announced that they had made significant progress by creating a robot that can imagine itself. After a day of intensive training, it was able to adapt to different situations, manage new tasks and detect and repair injuries in its own body.
2.2.2 Basic AI concepts
The concept of Artificial Intelligence (AI) lacks established unambiguous definitions and demarcations. The nature of the field allows for broad philosophical, social and mathematical discussions. AI Research in itself is both specialised and dispersed across subfields that often lack contact with each other. This makes the area in its entirety challenging to comprehend fully. However, for this analysis, we have chosen to use the same definition that Vinnova used in its study of artificial intelligence for the Confederation of Swedish Enterprise and Society, 2018, namely:
‘The ability of a machine to mimic intelligent human behaviour. Artificial intelligence is also the designation of the science and technology field that aims to study, understand and develop computers and software with intelligent behaviour.’
AI can thus be defined as the ability of machines to perform cognitive functions that we associate with human minds, such as perception, reasoning, learning, interacting with the environment, problem-solving and, ultimately, even creativity.
A dominant theory for assessing the characteristics of AI systems is the so-called Turing-test [12]: a computer passes the test if a person, after having asked several written questions, cannot discern whether the answers have come from a human or a machine.
The set of abilities [13] which are considered essential to enable artificial intelligence to be experienced as humanly intelligent include:
Natural language management (NPL).
The ability to store knowledge.
An automated ability to reason.
Machine learning to make discoveries tailored to given conditions.
Vision technology to see.
Robotics for moving or manipulating objects.
Another central figure of thought in AI is the rational, intelligent agent. The agent is a piece of software, an algorithm, which is expected to operate autonomously and be able to sense its environment, endure for a long time and adapt to changing conditions, as well as setting up and reaching goals. All the properties of the Turing test are also valid for the rational agent to function.
2.2.3 Narrow and broad AI
AI can be classified in many different ways, but a standard description is narrow versus broad or general AI. All the AI that exists today is narrow or rather; specialised. Our intelligence, however, is general. If at some point in the future, AI becomes general, it will probably change society fundamentally. When, and if, this will occur is debated and time spans from ten to several hundred years from now—or never—have been suggested. A marginal part of AI and machine learning development today touches on general artificial intelligence. The majority of development resources are focused on making narrow/specialised AI more effective.
2.2.4 Machine learning
Machine learning is an area of computer science that explores methods of getting computers to learn from data without having been programmed for the task. The area is related to statistics and pattern recognition (see Figure 8).
Figure 8.
AI and different learning methods put in a development perspective.
Machine learning has been the prevailing developmental track for practical applications of AI for a few decades. Progress has been made through the application of machine learning to increasingly larger sets of data. In a relatively short time, different machine learning subtypes have been developed, within which algorithms are continually being improved and adapted for various applications.
Those in the sector talk of ‘supervised learning’, which means that an AI algorithm uses sets of data to ‘train’ while receiving feedback from people to learn when the relationship between given inputs and outputs meets the requirements. Unsupervised learning means that the network works without prior knowledge. The computer must teach itself the underlying structures only using the input provided and not through any pre-given response.
Deep learning is a type of machine learning that can process a wide range of data points, may involve more straightforward data processing, and can provide more accurate results than traditional machine learning approaches—although it requires a greater amount of data to do so.
Deep learning connects software-based ‘neurons’ in a neural network. The network can receive large amounts of input and process it through multiple layers that learn more complex functions for each layer. Once the network has learned, for example, what an object looks like, it can recognise the same item in a new image.
Reinforcement learning means that the algorithm is rewarded when it is successful, for example, through the accumulation of points in a game using a step-by-step approach to reach the maximum score. The algorithm remembers the successful features and outcomes and corrects itself for ever better results. It learns by discovering. This method, which is inspired by the brain’s dopamine system, is used when there is insufficient training data available, when the ideal, ultimate goal cannot be defined explicitly or when the only way to learn about the environment is to get started and interact with it.
Reinforcement learning is the latest breakthrough in machine learning and received widespread publicity when the AlphaGo computer program from Google-owned DeepMind in 2015 defeated one of the Chinese champions in the board game Go. Since then, the technology has gained several commercial breakthroughs and is used, among other things, to streamline the operation of gas turbines, wind turbines and energy use in computer halls. The method has good potential for future industrial applications.
For an in-depth look at machine learning, we refer to Part 2 of the report. In this section, we will continue with an analysis of the supplier system.
2.2.5 The availability of AI is dominated by significant platform suppliers
The range of applied AI technology is increasing at a rapid pace: we are seeing infrastructures, tools, algorithms, data and pre-trained AI models for various purposes, all offered as standard products by all major platform providers (see Figure 9). The development of automation suppliers means that industrial control systems will also get built-in machine learning capabilities. The telecommunication industry is beginning to offer distributed and cloud-integrated edge technology that shares IoT concepts. There is also an increase in specialised AI providers for different applications.
Figure 9.
Computing power is a prerequisite for the development of artificial intelligence. It took 90 years to reach the first million instructions per second (MIPS) per $1000—now 1.2 MIPS/$1000 are added every hour. Source: Ray Kurzweil and KurzwelAI.net.
Technology providers of all categories contribute to the quick commercial distribution of machine learning technology, and several market studies show strong anticipated growth in the coming years. According to the analysis company Markets & Markets [14], the market for machine learning, language management and vision systems will grow from about USD 22 billion in 2018 to more than USD 190 billion in 2025.
This corresponds to a growth rate of almost 40%. IT consultants and system integrators are also seeing business opportunities and are gaining knowledge around the new tools. According to various studies, AI development within the IT consultancy industry is seen as among the most pronounced technological breakthroughs of all time [15].
But it is the big tech companies that are driving the lion’s share of commercial AI development. The platform companies Apple, Alphabet, Microsoft, Facebook, Amazon and IBM together have an estimated value of over USD 4 trillion. They account for 55% of the value of the Nasdaq 100 Index (see Figure 10).
Figure 10.
Top: Platform companies in the US have a definite lead in the development of AI platforms. Market values December 2018. Below: Comparison between the three largest IndTech companies in Europe vs. the USA. Source: EC, EU industrial policy after Siemens-Alstom, Blue Institute.
It is these companies that are behind the commercialisation of AI in the West. In Asia, Tencent, Alibaba and Samsung are dominating, while Europe lacks corresponding strengths. In comparison, however, Europe has advantages in industrial technology companies (IndTech). The European companies ABB and Siemens are significantly larger than their US counterparts.
The long-term AI strategies of the platform companies include a large R&D component reinforced by acquisitions. For example, Google’s purchase of DeepMind for USD 400 million, Twitter’s acquisition of Magic Pony for USD 150 million and Microsoft’s purchase of Github for USD 7.5 billion. To ensure the availability of top academics from universities, employees are being offered high salaries, unlimited computer and computing resources and minimal bureaucracy [16].
The concentration of resources, expertise and access to data is therefore currently focused on a few global commercial players. This is a part of the platform war, the battle for market domination over cloud services within which the mightiest battle is between Microsoft, Amazon Web Services and IBM.
Underlying this growing market landscape is the quiet market dominance of the platform companies. Generic cloud products reach end-users directly or via domain providers. Within the industrial context, automation, process and machine suppliers can add industry-specific value.
Automation suppliers operate in this way, serving as targeted market channels that increase the value of the platform companies’ large-scale production of computing power and machine learning (see Figure 11). Two groups of more independent initiatives flank these platform alliances and centres. One consists of companies that sell predictive analysis solutions and build individual platforms. According to a qualitative evaluation by analysis company Forrester [17], this segment is led by SAS, IBM and SAP, with a long tail of smaller players.
Figure 11.
For industrially applied AI, three groups of suppliers can be distinguished. In the middle are the large general platform providers, which are creating more and more alliances with companies that can serve as value-adding specialised channels of the platform suppliers’ large-scale AI offering. These are flanked by general analysis players, within which there are several large companies, as well as specialised industrial suppliers of various sizes. The dynamics of the industry are expected to give rise to significant consolidation. Source: Blue Institute, 2019.
The second flank is made up of specialised companies that supply systems for speech, language, vision and generally applicable machine learning platforms for industry and others. There is a similarly long tail of small and medium-sized players. The sector is immature, heavy with development and likely to undergo further consolidation.
2.2.6 Automation, industrial IT and digitalisation lead to IndTech
As demand for digital platforms increases and the boundaries between industrial IT, automation and other domains become blurred, more and more players are becoming interested in industrial technology. Cloud service providers Microsoft, IBM and Amazon are building alliances and challenging traditional automation providers such as ABB, Siemens, Emerson and Rockwell.
A second challenge for automation suppliers comes in the form of ICT companies. Ericsson, Cisco, Huawei, Nokia, Samsung and other industry operators are looking for applications for 5G technology, and they consider the industry’s Internet of Things an opportunity. The goal of 5G is to make wireless technology available for applications that have significantly higher bandwidth, speed and reliability requirements than personal use applications. According to Ericsson, operators stand to increase revenues by 34% if the process industry and electricity industry increase the use of wireless communication [18]. Ericsson is supporting this development through its IoT Accelerator Platform Initiative. This is a one-stop-shop that will make it easy and safe to connect IoT modules and that will also assist in translating the technology into a business setting.
Suppliers of industrial IT and automation now need strategies to deal with platform companies as well as IoT infrastructure.
The dominance of the platform suppliers makes it impossible for automation companies to avoid dependency on their resources, and the challenge for them will be to create relationships that develop the industry’s strengths (industrial, process and customer relationships) and increase customer value without becoming marginalised in the platform war. The platform and ICT companies can, by extension, be expected to contribute to making automation solutions more uncomplicated and more cost-effective and also to add new value. Intelligent apps in intelligent ecosystems are a development trend that has the potential for a significant impact, thanks to the fact that platform companies are opening up their APIs.
Platforms provide process and machine suppliers with additional automation and the potential for advanced in-house analysis. Machine suppliers and the automation industry share an ambition to build connected competence centres for optimisation and fault remediation in customer facilities. By extension, this strategy is also about competition for the valuable data that can be mined from industrial manufacturing.
A new image for the industry’s suppliers is emerging, where the ability to create real customer value will distinguish winners from losers. If IndTech suppliers succeed in this, they will have a much more developed role in future industrial value systems as highly specialised vertical suppliers of efficiency and quality. At the same time, the process flows will be held together using collaborative logistic systems.
2.2.7 Industrial case study: focus on mining companies: the ENSAF project: energy and safety diagnostics.
There is currently a significant interest in the early diagnosis of problems in underground mining facilities. There is a trend towards achieving fully automated mining, meaning that should hazard arise underground, increasingly there are few or no personnel around to address them. This makes it crucial to have a capacity for early detection of risks from fires caused by factors such as the overheating of vehicles, equipment, cables and so on.
It is possible to detect the risks of overheating early by placing sensors in facilities and on mining vehicles, which then continuously transmit information to a central diagnostic system. With this approach comes the need to continually train staff in different fire scenarios and in managing different situations.
Project ENSAF (a PiiA funded project) is a collaboration between ABB, RISE, Mälardalen University and Epiroc, which owns one-third of Mobilaris. With the assistance of the Swedish mining companies, who are involved in helping set project requirements and who act as sounding boards for the work, the project is aiming to find a solution to the significant challenges that fires pose in mines. The proof of concept, which involves the fitting of suitable sensors to one of Epiroc’s vehicles in one of Boliden’s mines, is planned to start at the end of 2019. Data collection will continue into 2020.
Boliden is a high-tech metals company with its mines and smelters, and it is working over the long-term to guarantee society’s access to the base and precious metals; from the mining of ores (minerals) to the production and delivery of high-quality metals to the industry. Its production capacity is high due to experience, innovation and advanced technology, developed in collaboration with various Nordic technology and engineering companies. Approximately 5800 people work at Boliden, and its operations are conducted in Sweden, Finland, Norway and Ireland.
‘In the event of a fire, the smoke, in particular, poses a serious threat to both people and appliances. It is, therefore, important to be able to detect if a fire is about to start’.
2.2.7.1 The challenge
The destructive impacts of mining fires can be significant, both in terms of human suffering and in terms of costs and lost revenues. On an annual basis, about one fire per week occurs in a Swedish mine, with the majority started by vehicles moving about the mine. Sweden has been spared from major mining fires in modern times, but in the global sector, it happens all too often. Take the well-documented case of the Pike River coal mine in New Zealand, wherein 2010 some 29 people died following several gas explosions. In addition to all the human suffering, the accident put the plant out of service for 45 days due to fire extinguishing and remediation work. The production loss corresponded to half a billion Swedish Crowns, in addition to all the restoration costs and elevated insurance premiums. Additionally, large penalties can be imposed if the root cause of a mining accident can be attributed to safety deficiencies.
In the event of a fire, the smoke, in particular, poses a severe threat to both people and appliances. It is, therefore, essential to be able to detect if a fire is about to start. Other types of risks that can arise include leaks on hydraulic lines, which may cause oil under high pressure to produce intense sprays or fog formations.
2.2.7.2 The experience
The ENSAF project is creating a system that leverages all existing fixed measuring sensors in mines and on vehicles and links various measurements (such as temperature, hydrocarbons, CO2 and CO concentrations, relative humidity and flow) to each other via simulation models. The aim is to identify problems at an early stage and nail down as precise a location for the problem as possible. The information collected is used as input to a decision-tree model to assess the risk of fire and also to determine the content of any toxic gases that may be hazardous to humans or machinery through corrosion. It will be possible to follow real-time developments in the mine and compare the measurement data collected with the simulations.
Development of the system is primarily conducted by Epiroc and Mobilaris, ABB and MDH, with Boliden acting as a sounding board and contributing with experience on mining conditions. RISE contributes, among other things, with knowledge around fire and protection, as well as conducting fire tests in its premises in Borås. ABB sees the potential, through the conceptualisation of development, for a complete solution that could be offered commercially, incorporating its automation system. Epiroc provides the measurements from the mining vehicles, with the data collected in its Certiq system. It then communicates the safety information to Mobilaris.
In terms of sensors, the project has been able to detect, among other things, gas formations caused by cables loaded with currents higher than they are rated for. Smoke detectors are used, to detect not only the shape of smoke but also oil mist that can occur through leakage. Thermal cameras can be used at longer distances to detect temperature increases on, for instance, cables.
Today, Certiq collects object data (e.g. hydra-like oil level, engine power, etc.) 24/7 from several thousand mining vehicles across the world. Since the establishment of ENSAF, the system has gone from communicating and gathering data from a few hundred vehicles to some 3000. The sensors tested under ENSAF are now implanted in Certiq, which transmits sensor data to Mobilaris. Here, gas values from the vehicles in the mine shaft, for example, can be monitored and may trigger alarms. In a fire situation caused by a mining vehicle, it is possible to correlate all available data and perform root cause analyses, and, with the help of deep learning, provide answers to the cause of the fire. In the future, artificial applications could anticipate possible fire situations and suggest appropriate maintenance activities to avoid fires.
Using measurements and analyses, ventilation can be adapted to suit real needs. This can save energy without risking functionality and provide a good working environment for both people and machines. Close to 50% of the energy consumed in an underground mine goes to ventilation. In the case of a fire hazard, the system is set to minimise the risk to underground personnel. The system is based on sensors that can communicate with each other to increase communication security locally and also with central systems that can give an overall picture of the situation.
Sources: ABB, PiiA, MDH.
This chapter continues in part 2.
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PiiA Insight. Västerås, Sweden: Swedish IndTech; 2018'},{id:"B11",body:'VINNOVA. Artificiell intelligens i svenskt näringsliv och samhälle. Stockholm, Sweden: Vinnova; 2018'},{id:"B12",body:'Turing AM. Computing Machinery and Intelligence. Oxford: Mind Oxford University Press; 1950'},{id:"B13",body:'Russel N. Artificial Intelligence, A Modern Approach. London, UK: Pearson; 2016'},{id:"B14",body:'Markets & Markets, Artificial Intelligence Market. Pune, India: Markets and Markets; 2017'},{id:"B15",body:'Konsultkompaniet, Så blir it-branschen 2019. Stockholm Sweden: Konsultkompaniet; 2019'},{id:"B16",body:'Financial Times AI academics under pressure to do commercial research. 2019'},{id:"B17",body:'The Forrester Wave: Predictive Analytics and Machine Learning Solutions, Q1. 2017'},{id:"B18",body:'Dagbladet S. Ericsson ikapp och förbi Huawei I 5G-racet. 2017'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Örjan Larsson",address:"orjan.larsson@blueinst.com",affiliation:'
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1. Introduction
The under-five mortality rate (U5MR) represents children who die before reaching the age of 5 per 1000 live births [1]. The definition of live birth is a sign of life such as breathing, heartbeat, or voluntary muscular movements of the newborn after separation from the mother, regardless of the gestational age [2]. The majority of these deaths occur by preventable or treatable diseases, especially in low- and middle-income countries (LMICs). The most common causes of death under 5 years old are acute respiratory infections, diarrhea, malaria, and birth complications [3]. Underlying problems are also critical in U5MR. Malnutrition is the leading cause of about 45% of all childhood deaths [4].
The World Bank divides the world’s economies into four groups according to their income status: low-income, lower-middle-income, upper-middle-income, and high-income countries. The classification is made annually based on the gross national income (GNI) per capita in US dollars (USD) of the preceding year. According to the current data for 2021, countries with a GNI per capita of less than 1046 USD are classified as low-income countries, and those with less than 12,695 USD are classified as middle-income countries [5].
Child mortality under the age of 5 consists of several subgroups. These are neonatal mortality (birth—the first month of life), infant mortality (birth—the first year of life), and child mortality (1–5 years). Looking at the U5MR in each subgroup, it is observed that the neonatal group has the highest risk. Among this population, the youngest has the highest mortality rate. A review showed that approximately one-third of all neonatal deaths occur within the first day after birth, and nearly three-fourths occur within the first week of life [6].
Data from the last three decades show that the fastest decline in U5MR occurred between 2000 and 2009. Globally, the annual rate of reduction (ARR) was 1.9 percent in 1990–1999, 4.0 percent in 2000–2009, and 3.4 percent in 2010–2019. While 1 out of every 11 children could not see their 5th birthday in the 1990s, this number dropped to 1 in 27 in 2019. In 34 LMICs, U5MR was reduced by more than two-thirds between 1990 and 2019 [7].
Although there was a similar decrease in neonatal mortality rates (NMR), it was slower than the decline in child mortality rates between 1 and 59 months. Between 1990 and 2019, the average ARR for child mortality in the first 1–59 months was 3.6 percent, compared to only 2.5 percent in the neonatal period. As a result of these rates, deaths in children under 5 years of age began to concentrate in the neonatal period. While the ratio of newborn deaths to all childhood deaths under 5 was 40 percent in 1990, this rate increased to 47 percent in 2019 (Figure 1) [7].
Figure 1.
Distribution of global under-five deaths by age, 1990–2019 [7].
The U5MR is a good indicator of the healthcare system and general social-economic development. The reasons are as follows:
First, these data precede results, not inputs. For example, the number of doctors and nurses per child is an input, it is related to child health, but positive/negative inputs do not necessarily mean that outcomes will be positive/negative. The U5MR clearly shows the extent to which these inputs achieve their goal.
The U5MR is closely related to some negative socio-economic factors (malnutrition, low immunization rates, poor maternal health, and education). Therefore, it is a strong indicator of inequality and systemic health problems.
At the same time, as many of these deaths are preventable, the rate reflects better than any other measure the lack of access to critical and essential quality health care, including family planning, antenatal and postnatal services, and disease prevention and case management.
For these and similar reasons, many global initiatives, mainly the United Nations Global Strategy for Women’s Children’s and Adolescents’ Health (2016–2030) [8] and the Sustainable Development Goals (SDG) [9], have set child survival goals to improve the U5MR.
In this chapter, we aim to examine the causes of death under the age of 5 and the methods of prevention.
2. Causes
In LMICs, maternal and neonatal diseases, respiratory infections and tuberculosis, enteric and other infections, and malaria are the leading causes of under-five mortality. While in high-income countries, complex neonatal conditions such as prematurity top the list of causes of under-five mortality, in LMICs, infectious diseases still rank high among causes of death.
2.1 Maternal and neonatal disease
Since the decline in NMR has not been achieved to a similar extent as in U5MR, the rate of deaths due to maternal and neonatal diseases among under-five mortality has increased recently, especially in high-income countries. In one study, the two most common causes of death in the neonatal period in LMICs were perinatal asphyxia and severe infections such as sepsis, meningitis, and pneumonia [10]. Approximately 23% of the 2.5 million annual infant deaths in the neonatal period are related to infections [7]. Deaths are due to complications associated with prematurity account for about one-fifth of neonatal deaths in these countries. These three causes account for 90% of total neonatal deaths in LMICs [11].
2.2 Respiratory infections and tuberculosis
Despite improvements in living conditions and increases in vaccination rates, respiratory infections are the leading cause of under-five death after neonatal diseases in LMICs. It is estimated that approximately 100 million pneumonia episodes occur annually under 5 [12]. With the increase in Pneumococcal conjugate and Haemophilus influenza type b vaccination, changes in the etiology of pneumonia have occurred recently. Among bacterial pathogens, non-type b Haemophilus influenza and Staphylococcus aureus are more common, whereas RSV stands out among viral pathogens. Although less common, CMV pneumonia can be fatal in HIV-infected and immunosuppressed patients [12].
Within this group of diseases, tuberculosis occupies a special place in LMICs. About a quarter of the world’s population is infected with Mycobacterium tuberculosis, which is not always easy to treat with its extrapulmonary involvement and resistant strains [13]. About half of all people with tuberculosis live in eight countries: Bangladesh, China, India, Indonesia, Nigeria, Pakistan, Philippines, and South Africa [14]. In 2019, there were a total of 50,000 deaths due to tuberculosis in children under 5 years of age [15].
2.3 Enteric infections
Enteral infections are the leading cause of death in children under 5 years among all infections, causing 1.5–2 million deaths per year [16, 17]. In LMICs, there are six episodes of diarrhea per year in infants and three episodes per year in children [18]. Rotavirus, Cryptosporidium, Shigella, and enterotoxigenic Escherichia coli (ETEC) are the most common pathogens observed in the moderate and severe diarrheal disease under 5 years of age in LMICs [19]. In the past, severe dehydration and fluid loss were the main causes of death from diarrhea. Other causes, such as septic bacterial infections, are now increasingly responsible for all diarrheal deaths [20].
2.4 Malaria
Infection with Plasmodium falciparum potentially is fatal and most commonly manifests as a nonspecific febrile illness often without localizing signs. About 635,000 deaths from malaria occurred in LMICs in 2019, of which more than 50% were children under 5 years of age [14]. Moreover, malaria in pregnancy leads to fetal growth retardation, low birth weight, and later malaria infection in the infant, which increases neonatal mortality [21, 22]. In sub-Saharan Africa, a region at high risk of malaria, peripheral malaria is detected in the blood of about half of pregnant women during antenatal care [23].
In addition to these main causes, some factors increase the frequency and treatability or mortality of these diseases. The Covid-19 pandemic, which has been ongoing for about two years, has placed a heavy burden on the health care system. A survey by UNICEF conducted in 77 countries shows that almost 68 percent of countries reported slight reductions in health checks and immunizations for children. In addition, 63 percent of countries reported interruptions in antenatal checkups and 59 percent in postnatal care [24]. In regions with a high burden of HIV, malaria, and tuberculosis, the Covid-19 pandemic may increase HIV-related deaths by 10%, tuberculosis-related deaths by 20%, and malaria-related deaths by up to 36% over 5 years [7].
Some other exacerbating underlying factors include food insecurity, inadequate feeding practices, household air pollution, lack of hygiene, and access to safe water or adequate sanitation.
3. Trends in under-five mortality
While the U5MR was about 85 per 1000 live births worldwide in 1990, it dropped to 38 per 1000 live births by 2019, a decline of over 50%. According to 2019 data, the global NMR was 17 per 1000 live births. While this rate was 11 per 1000 for children aged 1 month to 1 year, it was 10 per 1000 for children aged 1–5 years. The gap between the highest and lowest regional U5MR narrowed from 171 deaths per 1000 live births in 1990 to 73 in 2019. In 1990, the number of countries with a mortality rate above 100 per 1000 live births was over 50, while this number has dropped to 5 in 2019. Variation in NMR and U5MR in LMICs by year is shown in Figures 2–5.
Figure 2.
Number of under-5 deaths between 1990 and 2019. Data were obtained from the healthdata.org website.
Figure 3.
U5MR between 1990 and 2019. Data were obtained from the healthdata.org website.
Figure 4.
Number of neonatal deaths between 1990 and 2019. Data were obtained from the healthdata.org website.
Figure 5.
NMR between 1990 and 2019. Data were obtained from the healthdata.org website.
Despite these developments, U5MR in low-income countries remains high. These countries are all located in sub-Saharan Africa. In sub-Saharan Africa, the region with the highest U5MR, one in 13 children does not reach the age of five. This rate is 15–20 times higher than in developed countries. Of the 37 countries in this region, 31 have a high mortality rate (U5MR above 50 per 1000 live births). Although sub-Saharan Africa and Central and South Asia account for about 50% of children under 5, 80% of all under 5 deaths in 2019. On a country basis, nearly half of under-five deaths in 2019 occurred in five countries: Nigeria, India, Pakistan, the Democratic Republic of the Congo and, Ethiopia. Nigeria and India alone account for nearly a third. The alteration of mortality rates by regions over time is shown in Figure 6.
Figure 6.
U5MR (deaths per 1000 live births) by regions, 1990, 2000, and 2019 [7].
When evaluating neonatal mortality, sub-Saharan Africa has the highest rate in 2019 (27 per 1000), followed by Central and South Asia (24 per 1000). Nearly 80% of neonatal deaths in 2019 occurred in these two regions. From 1990 to 2019, sub-Saharan Africa was the only SDG region without a decline in neonatal mortality due to the relatively low decrease in NMR and increase in the birth rate. In this period, newborn deaths did not decline in 21 of 48 countries in sub-Saharan Africa, and the total number of newborn deaths remained stable at about 1 million per year.
The difference between the sexes has tended to narrow over time in under-five mortality. In general, the risk of death is higher for boys under the age of 5 than for girls. However, due to regional risks, the mortality rate of girls is much higher than expected in some countries. These countries are located in the South and West Asia. After 1990, there were similar developments in this area, and the number of countries with higher than expected girl mortality dropped from 25 to 7.
The SDG aims to finish under-five mortality from preventable causes by reducing the NMR to 12 and below per 1000 live births and the U5MR to 25 and below per 1000 live births by 2030. If the current trend in under-five mortality continues, 27 percent of the 195 countries evaluated in the latest UNICEF report will not meet the SDG target. Achieving the 2030 target requires high-level efforts and a focus on child mortality in the remaining 53 countries. All these countries are in the status of LMICs. In light of current data, it is estimated that 48 million children under the age of 5 will die between 2020 and 2030. Nearly 80% of these deaths are expected to occur in sub-Saharan Africa and Central and South Asia, where almost all of the countries they host are LMICs. Moreover, if current trends continue, it does not seem possible for nearly 90% of sub-Saharan African countries to reach the neonatal mortality target of the SDG [7].
4. Prevention
Access to life-saving interventions is at the forefront of consistently reducing mortality in LMICs. Examining the main causes of under-five deaths in these countries, it is observed that care during and after birth, vaccinations, and preventive and therapeutic services in early childhood play a crucial role [25]. An analysis conducted in 118 LMICs found that disrupting access to basic life-saving interventions could cause millions of under-five deaths in as little as six months [26]. We can consider protective interventions under several headings:
4.1 Perinatal care
Due to the increase in the birth rate and the dependence of mother-infant life on basic life-saving interventions, prenatal, natal and postnatal care is a priority issue in LMICs. At this point, comprehensive prenatal care, skilled and rapid care during childbirth, regular postnatal care for mother and child, and high-quality and adequate care for premature and sick newborns should be the goal. For reasons other than congenital anomalies causing one in 10 neonatal deaths [27], the following precautions should be taken:
Home visits during pregnancy can play an active role in reducing NMR. A large-scale study in India has shown that antenatal care provided by Accredited Social Health Activists significantly reduces infant mortality rates under 1-year-old [28]. All pregnant women should be visited by experienced teams at least four times during pregnancy, including one visit in the first trimester. At these visits, preeclampsia and eclampsia can be diagnosed and treatment initiated in appropriate cases. In addition, these visits provide an opportunity to start folic acid and iron supplements that improve fetal health and to test for HIV, which is at risk of transmission from mother to fetus. Home visits can also help ensure that adequate precautions are taken in areas where malaria is common. Tetanus vaccinations recommended during pregnancy can be given during these visits. Finally, these visits can encourage pregnant women to deliver in the hospital and provide information about birth complications and nutrition.
Facility-based delivery should be provided to all pregnant women and, expectant mothers should be encouraged in this regard. Basic hygiene rules such as hand washing, sterile cord-cutting, and cord care should be followed in these facilities. To achieve this, using clean delivery kits (CDK) should be expanded for both home and facility deliveries. The use of CDK containing soap, gloves, cord ties, and other sterile equipment has been shown to reduce neonatal mortality and neonatal tetanus in studies in LMIC countries [29, 30, 31]. Drugs (oxytocin, etc.) and materials (partograph, etc.) used in the active management of labor should also be available. In communities where access to facilities is not possible, the number of staff with basic emergency obstetric and newborn care (BEmONC) training should be increased. However, a fast and safe referral and transport system should be ready in case of complications where the capabilities and knowledge of these staff may be inadequate. The purpose of this transportation is to transport the patient to facilities where comprehensive emergency obstetric and newborn care (CEmONC) can be provided.
In the postnatal period, attention should be paid to hypothermia and allow the mother to feed her baby within the first hour of life. It should be ensured that the mother and newborn stay in the facility for the first 24 hours, and an early postnatal visit should be scheduled to recognize the danger signs. In cases of suspected or proven infection, neonates should be treated with antibiotics, and newborns at risk should be followed up in neonatal intensive care units with adequate personnel and equipment.
4.2 Vaccination and medication
Vaccination is an essential component of primary health care and an indisputable human right. Also, it is the single most effective prevention method for reducing mortality in children under 5. The following vaccines are at the forefront of preventing early childhood deaths: measles, polio, diphtheria, tetanus and pertussis, Haemophilus influenza type B, pneumococcal, and rotavirus vaccines. Reaching large populations with effective community health programs is crucial for vaccination success. Despite significant advances in immunization, too many children worldwide—including around 20 million infants each year—do not have adequate access to vaccines [32].
Since it is more difficult to diagnose tuberculosis (TB) in childhood, prevention may be more feasible than cure. At this point, neonatal use of the BCG vaccine, which is still the only vaccine against tuberculosis, could be the solution. Neonatal administration of BCG has been associated with a lower prevalence of TB disease [33].
Access to antibiotics and antimalarial to treat diarrhea, malaria, and lower respiratory tract infections also contribute to reducing child mortality. In diarrhea, oral rehydration solution and zinc supplementation help reduce mortality.
4.3 Community-based prevention
Community health workers play a vital role in providing health care to underprivileged communities. Public health workers organize public education programs, especially in the field of preventive health services. The performance of these workers is critical to increasing vaccination and breastfeeding rates, disseminating malaria prophylaxis devices such as insecticide-treated bed nets, and improving hygiene behaviors.
Studies in rural areas of three LMICs found that proximity to health facilities was a significant risk factor for infant, child, and general under-five mortality [34, 35, 36]. Similarly, a meta-analysis found that a distance of over 5 km from the health facility can increase mortality by up to 60% in newborns and children under five years of age [37]. In these and similar situations, governments should eliminate inequality of opportunity among communities and ensure access to health services for all. For example, maternity waiting homes, initiated under the leadership of the World Health Organization (WHO), can be expanded for pregnant women with limited access to facilities.
The integrated management of childhood diseases, in collaboration with national and international organizations, will help to address regional and cross-national disparities and share experience and knowledge. Finally, it is essential to build a people-centered and high-quality health system to implement all these preventive measures. At the same time, families’ desire for medical care and behavior to seek medical help when their children are ill should be encouraged.
4.4 Nutrition education and management of malnutrition
Encouraging breastfeeding is a practical and cost-effective method of preventing early childhood deaths. However, about two out of three infants do not have access to exclusive breastfeeding for 6 months. Breast milk provides all the energy and nutrients a baby needs in the first months of life, and continues to meet at least one-third of the baby’s nutritional needs during the second half of the first year [38]. Breastfeeding not only improves the child’s nutrition but also protects the child from the dirty water used in food preparation and cleaning kitchen utensils. Vitamin A supplementation should be encouraged as it has been proven to reduce the risk of illness and death from measles and diarrhea. Access to clean water and ensuring hygiene will be particularly effective in reducing the incidence of diarrheal diseases. Screening for acute malnutrition allows early detection of cases with a risk for mortality.
5. Conclusion
The U5MR has been significantly reduced over the last 30 years through concerted action and resource allocation. The next goal of countries achieving the SDG targets should be to ensure the protection of children by eliminating inequalities that arise for reasons such as household income, race and ethnicity, and subnational division. While there are some developments in child health globally, 75% of countries failing to meet the SDG targets are found in sub-Saharan Africa clearly showing the inequality that children face in terms of their chances of survival due to birthplace. These inequalities are exacerbated in times of crisis, such as the Covid-19 pandemic, and have an even more negative impact on vulnerable children [39]. Attention should be given to sub-Saharan Africa, where mortality rates are highest in all age groups and the population continues to grow, considering that 11 million children could be saved by 2030 if all countries achieve the SDG targets.
On the other hand, newborns, whose mortality rate is declining more slowly than other age groups and therefore continues to increase their share in the under-five mortality rate, also deserve special attention. Moreover, it is more difficult to reduce the number of deaths during this period due to the establishment of more advanced health care facilities and the need for quality health care services over a longer time, beginning with the antenatal period. In addition to these, it is necessary to provide adequate health services during childbirth and in the early postnatal period. These services, along with NMR, will also reduce maternal mortality rates, the risk of stillbirths, and disability.
When we look at under-five deaths as a whole, it is essential that evidence-based life-saving interventions are accessible and accepted in the community. In addition, the impact of contextual factors such as female empowerment, nutrition, and health system resources should not be ignored in achieving success.
Another issue that should not be ignored in child mortality is the reliability of the data. In resource-poor areas, deaths may not be reported, or the cause of death may not be identified. This information may not be made available to national authorities and international health organizations promptly. Therefore, there is a need for a high-level integrated database at local, national, regional, and global levels, especially for under-five mortality data.
In summary, ending preventable child deaths is only possible by improving access to well-equipped health care professionals during pregnancy and childbirth, life-saving interventions such as vaccinations, breastfeeding, and the provision of low-cost medicines, and access to water and sanitation, which are now lacking in low-income countries.
\n',keywords:"under-five mortality, neonatal mortality, low and middle-income countries, infections, perinatal mortality",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/79070.pdf",chapterXML:"https://mts.intechopen.com/source/xml/79070.xml",downloadPdfUrl:"/chapter/pdf-download/79070",previewPdfUrl:"/chapter/pdf-preview/79070",totalDownloads:89,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 6th 2021",dateReviewed:"September 19th 2021",datePrePublished:"December 20th 2021",datePublished:null,dateFinished:"October 20th 2021",readingETA:"0",abstract:"The under-five mortality rate (U5MR) represents children who die before reaching the age of 5 per 1000 live births. It is directly related to the development and economic income levels of countries. For this reason, high rates are observed in low- and middle-income countries (LMICs). The neonatal period deserves more attention as the decline in mortality rates has recently stalled. The most common causes of death under 5 years old are acute respiratory infections, diarrhea, malaria, and birth complications. Although neonatal disorders and birth complications have recently come to the fore, among these reasons, deaths due to infections are still high in LMICs. The crucial topics in prevention are perinatal care and vaccination. Apart from these, access to medicine, food, and clean water is essential in preventing deaths under 5. For preventive services to achieve their goal, these services must reach everyone. Ending preventable child deaths is only possible by improving access to well-equipped healthcare professionals during pregnancy and childbirth, life-saving interventions such as vaccinations, breastfeeding and the provision of low-cost medicines, and access to water and sanitation, which are now lacking in low-income countries.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/79070",risUrl:"/chapter/ris/79070",signatures:"Mehmet Tekin",book:{id:"11005",type:"book",title:"Mortality Rates in Middle and Low-Income Countries",subtitle:null,fullTitle:"Mortality Rates in Middle and Low-Income Countries",slug:null,publishedDate:null,bookSignature:"Dr. Umar Bacha",coverURL:"https://cdn.intechopen.com/books/images_new/11005.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-970-2",printIsbn:"978-1-83969-969-6",pdfIsbn:"978-1-83969-971-9",isAvailableForWebshopOrdering:!0,editors:[{id:"244265",title:"Dr.",name:"Umar",middleName:null,surname:"Bacha",slug:"umar-bacha",fullName:"Umar Bacha"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. 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Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2016 Chapter 1'},{id:"B2",body:'Sullivan, J. and N. K. Tureeva. INFANT AND CHILD MORTALITY. Uzbekistan Health Examination Survey 2002. Calverton, Maryland, USA: Analytical and Information Center, State Department of Statistics, and ORC Macro; 2004'},{id:"B3",body:'GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736-1788'},{id:"B4",body:'United Nations Children’s Fund (UNICEF) and the World Health Organization (WHO). Countdown to 2030: Tracking Progress towards Universal Coverage for Reproductive, Newborn and Child Health: The 2017 Report. Washington, DC: UNICEF and WHO; 2017'},{id:"B5",body:'https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2021-2022'},{id:"B6",body:'Jeeva SM et al. When Do Newborns Die? A systematic review of timing of overall and cause-specific neonatal deaths in developing countries. Journal of Perinatology. 2016;36(1):1-11'},{id:"B7",body:'United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Levels & Trends in Child Mortality: Report 2019, Estimates Developed by the United Nations Inter-agency Group for Child Mortality Estimation. New York, NY: United Nations Children’s Fund; 2019'},{id:"B8",body:'https://www.everywomaneverychild.org/wp-content/uploads/2017/10/EWEC_GSUpdate_Full_EN_2017_web-1.pdf'},{id:"B9",body:'http://sustainabledevelopment.un.org/'},{id:"B10",body:'Usman F, Imam A, Farouk ZL, Dayyabu AL. Newborn mortality in sub-Saharan Africa: Why is perinatal asphyxia still a major cause? Annals of Global Health. 2019;85:112'},{id:"B11",body:'Alliance for Maternal and Newborn Health Improvement (AMANHI) mortality study group. Population-based rates, timing, and causes of maternal deaths, stillbirths, and neonatal deaths in south Asia and sub-Saharan Africa: A multi-country prospective cohort study. The Lancet Global Health. 2018;6(12):1297-1308'},{id:"B12",body:'Marangu D, Zar HJ. Childhood pneumonia in low-and-middle-income countries: An update. Paediatric Respiratory Reviews. 2019;32:3-9'},{id:"B13",body:'Global tuberculosis report 2020. Geneva: World Health Organization; 2020.'},{id:"B14",body:'https://www.who.int/health-topics/tuberculosis'},{id:"B15",body:'http://ghdx.healthdata.org/gbd-results-tool'},{id:"B16",body:'Bryce J, Boschi-Pinto C, Shibuya K, Black RE, WHO Child Health Epidemiology Reference Group. WHO estimates of the causes of death in children. Lancet. 2005;365(9465):1147-1152'},{id:"B17",body:'Boschi-Pinto C, Velebit L, Shibuya K. 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First malaria infections in a cohort of infants in Benin: biological, environmental and genetic determinants. Description of the study site, population methods and preliminary results. BMJ Open. 2012;2(2):e000342'},{id:"B23",body:'Bauserman M, Conroy AL, North K, et al. An overview of malaria in pregnancy. Seminars in Perinatology. 2019;43(5):282-290'},{id:"B24",body:'https://data.unicef.org/resources/levels-and-trends-in-child-mortality/'},{id:"B25",body:'MCEE-WHO methods and data sources for child causes of death 2000-2017. Geneva: World Health Organization; 2018'},{id:"B26",body:'Roberton T et al. Early estimates of the indirect effects of the COVID- 19 pandemic on maternal and child mortality in low-income and middle- income countries: A modelling study. The Lancet Global Health. July 2020;8:E901-E908'},{id:"B27",body:'https://www.who.int/news-room/fact-sheets/detail/congenital-anomalies'},{id:"B28",body:'Nadella P, Subramanian SV, Roman-Urrestarazu A. The impact of community health workers on antenatal and infant health in India: A cross-sectional study. SSM-Population Health. 2021;15:100872'},{id:"B29",body:'Park JH, Hamer DH, Mbewe R, et al. Components of clean delivery kits and newborn mortality in the Zambia Chlorhexidine Application Trial (ZamCAT): An observational study. PLoS Medicine. 2021;18(5):e1003610'},{id:"B30",body:'Raza SA, Avan BI. Disposable clean delivery kits and prevention of neonatal tetanus in the presence of skilled birth attendants. International Journal of Gynaecology and Obstetrics. 2013;120(2):148-151'},{id:"B31",body:'Seward N, Osrin D, Li L, Costello A, Pulkki-Brannstrom AM, Houweling TAJ, et al. Association between clean delivery kit use, clean delivery practices, and neonatal survival: Pooled analysis of data from three sites in South Asia. PLoS Medicine. 2012;9(2):e1001180'},{id:"B32",body:'https://www.who.int/health-topics/vaccines-and-immunization'},{id:"B33",body:'Mangtani P, Abubakar I, Ariti C, et al. Protection by BCG vaccine against tuberculosis: A systematic review of randomized controlled trials. Clinical Infectious Diseases. 2014;58:470-480'},{id:"B34",body:'Schoeps A, Gabrysch S, Niamba L, et al. The effect of distance to health-care facilities on childhood mortality in rural Burkina Faso. American Journal of Epidemiology. 2011;173(5):492-498'},{id:"B35",body:'Okwaraji YB, Edmond KM. Proximity to health services and child survival in low- and middle-income countries: A systematic review and meta-analysis. BMJ Open. 2012;2(e001196):1-9'},{id:"B36",body:'Karra M, Fink G, Canning D. Facility distance and child mortality: A multicountry study of health facility access, service utilization, and child health outcomes. International Journal of Epidemiology. 2017;46(3):817-826'},{id:"B37",body:'Rojas-Gualdrón D, Caicedo-Velázquez B. Distance from healthcare facilities and early life mortality: A systematic review and meta-analysis. Revista Facultad Nacional de Salud Pública. 2017;35(3):420-431'},{id:"B38",body:'https://www.who.int/health-topics/diarrhoea'},{id:"B39",body:'https://www.oecd.org/coronavirus/policy-responses/covid-19-crises-and-fragility/'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Mehmet Tekin",address:"dr.mehmettekin@gmail.com",affiliation:'
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IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\\n\\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\\n\\n
\\n\\t
Does your institution already have a budget for covering Open Access publication costs?
\\n\\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
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\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\\n\\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
\\n\\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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Marine eutrophication has a negative impact on food security, ecosystem health and economy through disruptions in tourism, fisheries and health industries. Both N and P have known point and non-point sources. Control of point sources has been easier than non-point sources particularly agricultural sources for both N and P as well as fossil fuel combustion for N, which remains a major challenge. Implementing mitigation strategies for N has been reported to be effective for P mitigation; however, the converse is not true due to mobility and volatility of N. Excessive N and P cause algae blooms, anoxic conditions, and ocean acidification with these conditions leading to dead zones, fish kill, toxin production, altered plant species diversity, food web disruption, tourism disruption and health issues. Management of N and P pollution includes reduction of leaching from farms through crop selection, timely and precise application of fertilizer and building artificial wetlands, proper management of animal waste, reduction of fossil fuel N emission, mitigating N and P from urban sources and restoration of aquatic ecosystem. Mitigation measures need to focus on dual nutrient strategy for successful N and P reduction.",book:{id:"7547",slug:"monitoring-of-marine-pollution",title:"Monitoring of Marine Pollution",fullTitle:"Monitoring of Marine Pollution"},signatures:"Lucy Ngatia, Johnny M. 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Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters.",book:{id:"7547",slug:"monitoring-of-marine-pollution",title:"Monitoring of Marine Pollution",fullTitle:"Monitoring of Marine Pollution"},signatures:"Sidrah Hafeez, Man Sing Wong, Sawaid Abbas, Coco Y. T. Kwok,\nJanet Nichol, Kwon Ho Lee, Danling Tang and Lilian Pun",authors:[{id:"225316",title:"Dr.",name:"Sawaid",middleName:null,surname:"Abbas",slug:"sawaid-abbas",fullName:"Sawaid Abbas"},{id:"259861",title:"Ms.",name:"Sidrah",middleName:null,surname:"Hafeez",slug:"sidrah-hafeez",fullName:"Sidrah Hafeez"},{id:"259890",title:"Prof.",name:"Man Sing",middleName:null,surname:"Wong",slug:"man-sing-wong",fullName:"Man Sing Wong"}]},{id:"35057",doi:"10.5772/33720",title:"Surface Water Quality Monitoring in Nigeria: Situational Analysis and Future Management Strategy",slug:"surface-water-quality-monitoring-in-nigeria-situational-analysis-and-future-management-strategy",totalDownloads:13273,totalCrossrefCites:14,totalDimensionsCites:27,abstract:null,book:{id:"1998",slug:"water-quality-monitoring-and-assessment",title:"Water Quality",fullTitle:"Water Quality Monitoring and Assessment"},signatures:"A.M. Taiwo, O.O. Olujimi, O. Bamgbose and T.A. 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Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters.",book:{id:"7547",slug:"monitoring-of-marine-pollution",title:"Monitoring of Marine Pollution",fullTitle:"Monitoring of Marine Pollution"},signatures:"Sidrah Hafeez, Man Sing Wong, Sawaid Abbas, Coco Y. T. 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In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand—a remote country of islands in the South Pacific Ocean—are examined. 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Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. 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His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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