Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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In 2008, he held a Doctor of Philosophy (Candidate of Science) degree from Taras Shevchenko National University of Kyiv in specialty of Algebra and the Theory of Numbers. His PhD thesis "Theory of the column and row determinants and inverse matrix over a skew field with involution" introduces and develops the theory of new column and row determinants for matrices with noncommutative entries. In 2021, he was awarded a Doctor of Physical and Mathematical Sciences degree from Institute of Mathematics of NAS of Ukraine in Kyiv. His habilitation ScD thesis " Generalized inverse matrices over the quaternion skew field and their applications" is devoted to generalized inverse matrices over the quaternion skew field, first of all to their determinantal representations, and their applications to solving quaternion matrix equations, some differential matrix equations, and problems of quaternion matrix minimizations and approximations. Now, he is working as the Leading Researcher of PIAPMM of NAS of Ukraine. In 2014, he held an academic degree of Senior Research Fellow (Algebra and the Theory of Numbers) from Ministry of Education and Science of Ukraine that is equivalent to Associate Professor. He obtained the award for significant achievements in the field of science from the Lviv Regional State Administration and Regional Council (2019, 2021). His research interests are mostly in Algebra, Linear Algebra and their Applications. He has more than 80 scientific publications, from them more than 60 are papers with the high science citation index that have been published in well-known professional scientific journals and editor\'s books. 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1. Introduction
Landscape planning, often referred to as ‘environmental planning’ or ‘ecological planning’, is a way of directing or managing changes in the landscape so that human actions are in tune with nature and environment (Zaucer and Golobic, 2010).
Landscape planning is undergoing change due to new requirements. Its previous main task of controlling spatial uses and the development of nature and the landscape has extended. Implementation of the European requirements for the Natura 2000 network, for the Water Framework Directive (WFD), the Floods Directive as well as the Strategic Environmental Assessment (SEA) can be made considerably easier and can be coordinated with the help of landscape planning. It is ideally suited, for example, as the basis of the Strategic Environmental Assessment or as an extensive information base for river basin planning covering all natural resources. In addition, landscape planning increasingly supports the tasks of providing members of the public with environmental information and their participation in sustainable local community and landscape planning (Haaren, et al., 2008).
Landscape planning can be better used as a versatilely usable information basis for overall spatial planning, impact mitigation regulation or environmental assessments if the information is presented according to the requirements of this planning and these instruments. Spatial planning has always been regarded as an activity that, in seeking solutions for a certain social problem, brings a change into the territory. It primarily looks after social needs, the economical use of the resource and its fertility. Because spatial planning is essentially economic in orientation, it is generally characterized as developmental planning (Zaucer and Golobic, 2010). The basic distinction between spatial planning and landscape planning is that the former is essentially economic and developmental in orientation, while the latter is more concerned with environmental and landscape qualities and thus protective in orientation. It must be noted that landscape planning does not represent a substitute for spatial planning. With developed approaches and methods, it may complement a set of spatial planning approaches and methods and contribute to larger efficiency of bottom‐up comprehensive planning (Zaucer and Golobic, 2010). The aim of landscape planning is thus to prevent or at least limit the degradation of the environment to a minimum while increasing, as far as possible, ‘creativity’ in order to meet the developmental needs. By combining approaches from the natural sciences and the planning disciplines landscape planning has developed a range of different methods and tools for integration of environmental objectives into the process of analysis and the development of planning proposals. Landscape planning approaches and methods are transparent and systematic, which makes them useful for participatory and comprehensive spatial planning (Zaucer and Golobic, 2010).
Landscape planning is currently developing away from rigid planning to a generally accessible and easy to update information base and a basis for action. By using new data processing and transfer technologies, landscape planning can be developed into an information and communication platform, which also communicates data and knowledge about nature and the landscape to the public and makes simple consultation and participation possibilities available via the internet. This development is borne by a new understanding of government action which is characterised by more proximity to citizens and transparency in politics and the administration. Landscape planning therefore supports implementation of Agenda 21 as well as the objectives of the Aarhus Convention and the associated EU Directives and federal laws for the introduction of more democracy in environmental issues. It promotes the enlightenment of members of the public and businesses as well as their commitment to their environment and homeland. Because landscape planning creates fundamentals, competence and incentives for own initiatives, resourcefulness and commitment to the integration of environmental aspects in landscape usage (Haaren, et al., 2008).
Landscape planning is also a way to effectively include the environmental requirements of different sectors into planning process. One of the most valuable approaches is vulnerability analysis, where environmental qualities are assessed from the viewpoint of potential threat resulting from planned actions. It functions as an integrating and conflict‐solving tool, since it (Zaucer and Golobic, 2010):
includes a whole territory (of a chosen administrative / planning unit),
considers a whole range of diverse environmental components,
supports active dialog between stakeholders,
may embrace all interests (natural, social, economic, and political) and evaluate their consequences and thus supports crosssectoral or comprehensive planning, and
supports search for an optimal solution.
Landscape planning today should not be viewed as a static plan but as a dynamic, continuously or modularly changeable information and working basis. Landscape planning is expected to be need- and problem-oriented. Against the background of fast changes in use of nature and landscape, these requirements are becoming increasingly important (Haaren, et al., 2008). Landscape planning, design and management are practised directly or indirectly by many others and in many sectors, including land use planning, agriculture, forestry, nature conservation, amenity land management, and so on, and we include all these in our approach. The term ‘landscape’ used here is also broad and includes much more than ‘the appearance of the area of land which the eye can see at once’ (Chambers, 1993). Landscape is an evolving cross-disciplinary area, which draws contributions from art, literature, ecology, geography and much more (Benson and London, 2000). As the legal basis of the various planning and instruments partly name natural resources as sensitive receptors, partly landscape functions, it should be possible to access landscape planning information structured both by natural resources and landscape functions.
Landscape-ecological planning is a specialization within landscape planning that focuses on spatial planning, the organization of uses and relationships of land uses to achieve explicit goals (e.g. habitat improvement, sustainability). While the landscapeecological planning approach is characterized by a focus on the linkage of ecological patterns and processes, it also includes the actions and values of humans, and social and economic dimensions (Hersperger 1994). Finally, landscape-ecological planning adopts the landscape as the principle spatial unit of research and planning recommendations (Ahern, 2006).
Landscape analysis has a significant function in the process of decision making for the future land use, organization of the space, nature protection, and rational use of the nature resources. Basic problems and tasks of the landscape analysis and planning are located in discovering and solving the conflicts among the development of the society and very complex task in nature assignment. The development covers more intensive engagement of space and more intensive land use, organization and arrangement. The features of the land and space with their entire natural and produced substratum are the significant categories for determination of future development. In this context, landscape analysis and planning appear as an activity of primary task to connect developing possibilities and tendencies for certain space (Pecova, 2000). These strategies, in essence, define the planning context with respect to the macro-drivers of change in a given landscape and the strategic nature of the planners’ response. Defining these strategies also helps to place the planning activity within a broader context, which is particularly important when planning methods are transferred or adopted for use in different locations, contexts or for different applications (Ahern, 2006). Landscape planning methods can also be classified and understood according to their strategic orientation: protective, defensive, offensive or opportunistic (Ahern 1995). Planning methods can also be understood and classified according to their resource or goal orientation. The abiotic–biotic–cultural (ABC) model is useful to describe the specific goals addressed in planning and the level of integration between these goals (Ahern, 1995) (Figure 1). In this model, abiotic goals include water resources, soil and air quality. Biotic goals focus on biodiversity in general, including individual species and habitat protection and ecological restoration. Cultural goals are human-based and include: transportation, land use, recreation, historic preservation and economic goals (Ahern, 2006).
Figure 1. presents an array of planning types graphically organized within a triangle that represents the ABC model. In this diagram a number of planning sectors or themes are located according to their emphasis and level of integration within the ABC resources. The figure shows that an evolution is occurring towards a more integrated planning perspective as represented by the central circle (Ahern, 2006).
Figure 1.
The abiotic, biotic and cultural resource-planning continuum (Ahern, 2006).
The combination of different landscape factors and the interactions between the natural resources are also significant for performance and ability to function. Apart from the description and assessment of the landscape functions (current condition and development potential), statements for specific areas are made on the sensitivity of distinct (sub)landscapes to impacts as well as on the ability to restore their performance and functional capability (Haaren, et al., 2008). This can be achieved by appropriate linking of standard text units in digital landscape plans. It is also advisable not only to be able to select specific individual cartographic areas but also to easily find and collate text statements on special landscape units. This service will assist the administrations or project sponsors in consolidating the respective relevant area descriptions for comments or environmental assessments. The effort spent on these preparations pay off because the information is so conveniently accessible and is easier to integrate in other planning and instruments (Haaren, et al., 2008). In landscape planning the existing condition of nature and the landscape is determined and assessed on the basis of legal and functional objectives and standards, which also include landscape planning objectives at a higher level. To this end the available data and information is collated and, where necessary, it is supplemented and updated by additional surveys. The fundamental information on the soils, geology, bodies of water, air and climate, fauna and flora is used to deduce statements regarding the performance and functions of the individual natural resources and/or the balance of nature and the landscape overall (Haaren, et al., 2008).
2. Inventory and analysis of the landscape
2.1. Terrain analysis
Topographical maps of Greek origin Topos (place) and graphics (lines formed figure) is formed from the word, used for lines shows created in the forms. These are the natural and cultural aspects of the land, the horizontal and vertical cases, show a horizontal plane and under a certain scale. Soil maps and other maps are used in making topographic maps as a base map (Figure 2). Soil experts determine the elevation and slope curves, positions the most benefit from relief.. Physiography deals with the physical conditions of the surface of the land. The broad physiography of an area can be determined by the knowledge of the physiographic region in which it lies. The important aspects of physiography are elevation and slope. Slope, soils, geology, hydrology, microclimate, plants, and animals may be strongly related to elevation. This means that elevation is an important feature in analyzing landscapes. Topographic contour shape on the map, the location, frequency, taking into account in an area of alluvial fans, colluvial skirt lands, valleys, mountainous-hilly areas, old terraces, alluvial flood plains, river paths, meanders, lagoons and sand dunes landforms such as defined. (Figure) Any contour of the land surface structure identifies the most understandable form. Contour curves, in order to produce a realistic and reliable planning decisions important.
Figure 2.
Grading curves for an example area
First of all, check whether the map is up to date. If the current deficiencies detected during the observations, can be added to the map. Or new information obtained from different sources (water sources, wells, dams, ponds, bridges, new uses, etc.) can be placed on the map. Horizontal and vertical cross-section for the map to be more easily interpreted. Thus, the difference in level between the high and low parts of the land surface structure and can be detected (hills, peaks, valleys, valley bottoms, slopes) (Figure 3). This is due to differences in harmful situations, measures to prevent them must be determined. The base terrain, steep slopes, private entities, such as delta, flood deposits, flood areas identified as sensitive areas. The cross sections obtained terrain, concave (depressions, valleys, depressions and so on.) And convex (ridges, hills) allows to perceive how it has developed to take shape. It is also important heights, large and small valleys, flood beds and borders determined and displayed. Accordingly, both precipitation and drainage of surface water streams flow distribution to be determined. Surface water flow intensive - is less dense areas should be considered.
Figure 3.
Elevation for an area
According to Kienast, 1993; for many countries topographic maps in the scale of 1:20’000 to 1:30’000 provide reliable monitoring data over the last 100 to 150 yrs (Hodgson and Alexander, 1990). Additionally, there are often rich collections of detailed plans in larger scales (1 :5000 to 1500) that date back to the late 18th and the early 19th century (Di.int, 1990; Miiller, 1990). For the present study the main data sources are two series of topographic maps in the scale of 1:25 ’000 dating back to the 1880’s (Figure 4). Since topographic maps are perceptions of the environment and often rather a ‘text than a mirror of reality’ (Harley, 1989), definition of specific landscape elements or habitat types may vary considerably over time and the location of features is often less accurate in earlier map editions compared with today’s standards (Hodgson and Alexander, 1990). Some of these misinterpretations are impossible to correct. However with a strict crossdating and a comparison of the historic maps with data from independent sources, most incompatibilities were eliminated. This was accomplished by consulting the written protocols and field notes of the topographers.
Figure 4.
Dijital elevation model (for spatial analyst)
Elevation maps are easily constructed by selecting intervals from the base maps. Altitudes can be represented by coloring spaces between topographic intervals. Elevation changes are depicted in shades of browns, yellows, or grays with felt markers, colored pencils, crayons, or through the use of computer technology, becoming lighter or darker as elevation increases
In addition, topographic maps allow visibility analysis is made from.Visibility analysis based on viewsheds is one of the most frequently used GIS analysis. This analyst ise used. This analysis is used in many studies of landscape architecture (Figure 5).
Figure 5.
Visibility analysis (observer offset:120 m. and target offset:100 m.)
Contour curves are some of the features (Karadeniz, 2010):
All points on a contour curve above are the same height above sea level,
Each contour curve closes in on itself. Even with the edge of the map, the map will continue and close neighbors,
A closed contour curve shows the peak or pit.
Contour curves do not cut each other.
The slope increases, the contour lines pass as close to each other
In the cross-sections in order to compare the characteristic profile shapes with each other is possible to make topographic maps. (Figure
Contour maps of curves that can be obtained; Height groups, slope, aspect maps (except that 3-D images);
Height Groups
Curved contour map is obtained by the separation of certain groups heights (Figure 6).
2.2. Slope
Slopes may be subdivided according to steepness and direction. Slope direction is referred to as aspect, or orientation (Figure 7). Steepness may be important for such activities as agriculture or the construction of buildings, while the direction of slopes is an important factor for such activities as siting housing for solar energy collection. Slope composition and related lithology needs to be determined. Lithology is “the soil and rock material that comprise a slope” (Marsh 1998, 80) or the physical characteristics of sedimentary materials. As with the elevation map, the division of slope categories will depend on the study.On the map using the horizontal and vertical distances are obtained and analyzed in separate groups of different units (Kienast, 1993).
Figure 6.
Land profile graph (made by DEM)
Figure 7.
Slope analysis
2.3. Aspect
Aspect map, facing at the surface indicates in which direction. Aspect of a landscape, the climate, especially temperature and rainfall affect the amount of that place. In general, the S, SE, SW, and W aspect "sunny aspects,» is called. N, NE, NW, E aspects, "the shady aspects,» has been described as (Figure 8). The shaded look still water evaporation from the soil temperature is less than would be even less. Therefore, the shady aspects, the same rainfall conditions, sunny look still have a more favorable water economy
Figure 8.
Aspect analysis
2.4. Climate analysis
Climate is the set of meteorological conditions characteristic of an area over a given length of time. It is defined as the study of extremes and long-term means of weather. The regional (or macro) climate is the big picture, the meteorological conditions and patterns over a large area. Macroclimate is affected by physical conditions such as mountains, ocean currents, prevailing winds, and latitude. It in turn affects the formation of the physiographic region through the weathering of the terrain and the amounts of precipitation that fall on the landscape. Climate states the average of the air for a long time in a specific place. Macro, meso and micro are the 3 types of climate. Long-term measurements, temperature, precipitation, air humidity and air movement and extreme values determined for the average for the region, which is characterized by a wide range of climate macroclimate is called. Climate characteristics of macro-states of different characteristics of land in the air near the surface have been described as microclimate. Shape of the face of the land, such as elevation and aspect Relief for small spaces created by the characteristics of the type of climate is called the mesoclimate. This is located between the macro and microclimate. Plant growth and biological analyzes to people about the climate are crucial in terms of comfort, they are temperature and precipitation analysis. Relative humidity, time of sun and cloud cover, wind direction and nature, frost days, and etc. factors have a significant effect on the bioclimatic comfort. Precipitation is the most important factor limiting regeneration of vegetation. The total annual rainfall is 600 mm but not enough for the formation of a local forest as a result of research that indicated
Heat and temperature are very different concepts. Despite the potential value of objects based on the heat, the temperature of this value emerged as a kinetic form. Therefore, the air temperature is not heat.
Bioclimatic limits of comfort with the body temperature of 15 ° C between 37 ° C ambient unless otherwise stated. Temperature of 20-24 C in the summer, optimum indoor temperature of 21 C values of the optimal bioclimatic comfort. At night, the relative humidity of 40-70% and 18-24 C temperature limit values.
Climatic elements in preparing the vegetation ecological environment, climate, type of vegetation and animals living in the rural and urban areas are the limiting factor.
Analysis, planning to put up a field of plant breeding the human race in the comfort of home to learn about the conditions to create a suitable environment is important for people in planning will be done.
Therefore, the climatic comfort of people trying to manifestation of the analysis depending on the season. Bioclimatic comfort level and the amount of which should be of climatic elements allowing research on human beings endure as a medium between the lowest and highest temperatures bioclimatic comfort limits.
Meteorological elements change vertically and horizontally within short distances. Small-scale variations are brought about by changes in slope and orientation of the ground surface; soil type and moisture; variations in rock, vegetation type and height; and human-made features. These different climates found within a small space are grouped together under the general description of microclimate. The term topoclimate is used when the effects of topographic variations of the land on the microclimate are considered. Generally, topoclimate is an extension of microclimate into the higher layers of the atmosphere and over landscapes, depending on the relief of the land. Therefore, topoclimate can be considered to occupy an intermediate level between macroclimate and microclimate. It is important to understand microclimate and topoclimate for many of the same reasons that macro, or regional, climate is important. These finer layers, however, relate more directly to building and open space design (Kienast, 1993).
2.5. Geology
Geology is the study of the earth. This study involves both what has happened in the past, or geological history, and what is happening on and in the earth today.
This analysis was prepared by institutions engaged in geological studies consists of the interpretation of maps and reports. As a result of this review maps and reports, forms the bedrock of physical and chemical state of the soil, water retention properties of the mass due to the hydrogeological characteristics of sedimentary or igneous efficiency, earthquake status (seismicity), the main rock formations are learned.
The inventory of a place requires an understanding of the geological history and processes of the region. Such understanding can begin with a geological\n\t\t\t\t\tmap, which is “a graphic representation of the rock units and geological features that are exposed on the surface of the Earth. Accordance with the determination of the geological structure of the area in which such use is put forward. Functional characteristics of a field analysis of geological bedrock exhibits the aesthetic features can be evaluated. Underground long and wide fissures (fault lines) goes on and on. These lines are extremely sensitive to the occurrence of the earthquake areas. These areas should be on residential areas, more use should be included in nature conservation purposes. In addition to showing different types and ages of rocks, most geo- logical maps depict geologic features, such as faults, folds, and volcanoes. The relative timing of events can usually be determined from a geologic map, usually by the application of these principles: superposition (younger layers are above older ones); original horizontality (layers formed from deposition of sediment were originally flat); crosscutting relationships (younger features crosscut older ones; a fault will be younger than the layers or contacts it cuts); and inclusions (a rock unit is younger than the layers from which the inclusions it contains came).
Summary of geologic inventory elements; regional geographic history, depth to bedrock, outcrops, bedrock types and characteristics, cross sections, columnar sections, surficial deposits (regolith): kames, kettles, eskers, moraines, drift and till, mineral resources, major fault lines, earthquake zones, and seismic activity, rock slides and mud slides (Kienast, 1993)
2.6. Geomorphological analysis
Geomorphological structure, depending on the geological structure of different geological times (Era), composed of the structure of the land. Geological times, the base of the valley bottom and the plain plains, alluvial cones, low and high benches, low, medium, and high plateaus, hills, hilly and mountainous areas and etc. geomorphological formations emerge. Generally, plains, hills, high hills and highlands plateaus are around. Maps, brown hills, valleys, yellow and green, and etc.. are shown in colors. Geomorphological structure studies, microclimate, ground water, agriculture, transport, housing and construction issues during the elections helps. For example, the hills are areas that allow private construction. Dense urban developments planned, slope and valleys, parks and woods considered. Relief: mountains, hills, plains, valleys and plateaus of the surface forms of the earth\'s crust. Soil formation processes such as erosion and drainage efficiency of distribution and orientation plays a significant role in relief.
The following information can be produced as a result of an examination of relief
the height above sea level
according to an altitude of objects around the comparison and detection of patterns,
Relief, the shape of the land belonging to a landscape on a flat, recessed - ribbed, curved, is a phrase that allows the introduction of phrases such as low or high. Relief characteristics of a landscape, "altitude", "examination", "slope of the land", "shape of the earth» factors are introduced. Relief of a place, climate, vegetation and soil characteristics have a significant impact on.
2.7. Hydrological analysis
According to (Kienast, 1993); Water is essential for all forms of life. It is also a finite resource. Most water in the hydrosphere is salt water (97.20 percent). Water in the polar icecaps and other frozen areas accounts for 2.15 percent of the resource. This means only 0.65 percent of the water in the world is fresh, and its distribution and quality is uneven (Tarjuelo and de Juan 1999). As a result, water is an essential factor to consider in planning Water resources in conjunction with the natural function of the aquatic ecosystem at every stage of life, are needed by all living beings. Hydrological characteristics of the structure is damaged in any way connected to it will affect the life of the living.
The hydrologic cycle expresses the balance of water in its various forms in the air, on land, and in the sea. As the hydrologic cycle and water budget illustrate, hydrology deals with the movement of water through the landscape both on the surface and in the ground. Groundwater is water that fills all the unblocked pores of materials lying beneath the surface. Surface water is water that flows above the ground. Depth to water table, water quality, aquifer yields, direction of movement, and the location of wells are important groundwater factors.
Hydrological survey planned in the field;
surface drainage analysis
analysis of groundwater
surface water analysis
bottom water analyses can be performed in four different fields.
The skeleton of the geological structure formed by elements of the vitality of plants and animals, which have gained ground, first of all exhaustible not protected, there is no place, no longer works in the life of human nutrition, accommodation, and so on. a substance that has taken over the function of many. Soil, the water and items such as a living source. Soils occupy a unique position in the lithosphere and atmosphere. Sustainable soil use is indispensable for the development of a country. They are a transition zone that links the biotic and abiotic environments. Soil is a natural three-dimensional body on the surface of the earth that is capable of supporting plants. Its properties result from the integrated effect of climate and living matter acting upon parent material, as conditioned by relief over periods of time. Many processes are linked within the soil zone, so soils often can reveal more about an area than any other natural factor. The soil survey includes the information necessary:
To determine the important characteristics of soils
To classify soils into defined types and other classificational units
To establish and to plot on maps the boundaries among kinds of soils
To correlate and to predict the adaptability of soils to various crops, grasses, and trees, their behavior and productivity under different management systems, and the yields of adapted crops under defined sets of management practices
The principal purposes of the soil survey are:
To make available all the specific information about each kind of soil that is significant to its use and behavior to those who must decide how to manage it
To provide descriptions of the mapping units so the survey can be interpreted for land uses that require the fundamental facts about soil
Summary of soils inventory elements: Soil series, permeabilit,texture, profiles, erosion potential, drainage potential, soil associations and catenas, cation and anion exchange, acidity–alkalinity
Soil maps, land capability classes and these classes determine the soil physical, chemical and biological properties of soils, as well as training courses are available. In addition, usage patterns according to these properties are stated and symbols are shown on the map.
Soil landslide, fluidity, displacement characteristics of survey plans or maps are very important in terms of processing uses reach a decision on the erroneous.
Soil, landscape planner is one of the important ecological factors affecting the decision. Should not be the decision of any plan without understanding the properties of the soil as it should be.
Soil, landscape planner is one of the important ecological factors affecting the decision. Different climatic conditions, a wide range of rocks of different plant designs at different elevations formed land, water and air, one of the indispensable elements of life. Soil is a limited natural resource. In Turkey, most of the worlds to represent the soil types have different soil types.
2.9. Vegetation
Plants are important to study for many reasons. They may have economic and medicinal value. They provide habitat for wildlife. They have significant influence on natural events like fires and floods and may reduce the human consequences of such events. Plants are beautiful and contribute to the scenic quality of landscapes. Plants are the source of oxygen, which humans need to survive.
Survey analysis of landscape vegetation is one of the most important works. Flora and vegetation survey done by several methods. The flora of an area of all plant species, surveying, site observations and collected in the form of plants can be identified.
Flora cannot work without knowing the vegetation. Stratification, vitality (naturalness) and the other based on qualitative and quantitative characteristics of the plants are grouped and named all of the vegetation. Vegetation surveys, in the form of detailed investigation, Braun /Blanquet\'s method and the values determined by the quality and quantity of work done with the vegetation maps we developed this method in many countries.
The vegetation plots will be determined by dividing the area of the study of flora as simple as possible. Sample plots can be created in various sizes. For example, a plot of 20x20 m in size of all trees, shrubs, herbaceous plants is determined by the type and size. Plots more detailed surveys, 4x4 m and 1x1 m in size by dividing the sub-plots of plant species can be detected. These plots are given numerical values are determined by density of plant species.
Vegetation, vegetation can be classified as natural and cultural. In vegetation surveys should be examined in both studies. Vegetation can be determined by aerial photographs. Vegetation remote sensing system is also an important method used in the search. The vegetation surveys, and maps are used in planning the elections of most plant material.
Summary of vegetation inventory elements; Plant associations and communities, vegetative units, species list, species composition and distribution, physiognomic profiles, ecotone and edge profiles, rare, endangered, and threatened species, fire history (Kienast, 1993).
2.10. Fauna
Fauna, terrestrial and aquatic fauna can be studied in two groups. Land fauna of mammals, birds, frogs and reptiles, insects and invertebrates. These populations, habitats, and is determined in terms of activities. Results are reported with a map of a state of wild life. Within the scope of this report, the general condition of the fauna (endemic, rare, endangered, extinction, etc.). Indicated. Broadly, wildlife is considered to be animals that are neither human nor domesticated. Insects, fish, amphibians, birds, and mammals are more mobile than plants. While closely linked to vegetative units for food and shelter, wildlife often use different areas to reproduce, eat, and sleep. Like vegetation, wildlife have not been extensively inventoried except where the animals have some commercial value. Because animals are mobile, they are even more difficult to inventory than vegetation. Planners are paying increased attention to wildlife. They observe that in addition to enhancing the quality of life, wildlife protection is important for ethical and moral reasons, for recreational benefits, and for economic and tourist values.
Summary of wildlife inventory elements; Species list, species-habitat matrix, animal populations, habitat value map, habitat of rare, endangered, and threatened species
Animal migration is connected to both the ecological and genetic factors need to be taken into consideration surveying point. Migration event, both vertically and horizontally maintained. Regular vertical migration by plankton, the night side of the sea or lakes go deep into the water again in the morning takes the form of strokes. Horizontal migration of many invertebrate and vertebrate animals conducted by the immigration. Vertebrate animals are more regular migrations. Some animals are effective spread plant species. For example, birds, fruits, taking them to various places after eating the seeds of these plants are to help them develop those areas. In addition, the fauna that need to fertilize plants.
In addition, creating a major impact on hunting fauna. Hunting generating stations was established, hunting periods, must be done regularly. Effects on fauna, there are cultural practices, improper applications, the reductions in fauna, migration, and etc. causes problems.
2.11. Analysis of socio-culturel resources
Landscape architecture, planning activities, as well as natural resources, cultural resources present in the area is important in the determination of planning decisions to be taken for these resources. Planning was done by the people for the people by the people are the most important criterion in planning.
In the selection of a cultural use of natural resources in an area to determine the effects of this field, if necessary, the effects on natural resources, cultural resources, and these effects should be known to be determined.
Surveys, social situation, demographics, migration and urbanization, communication and transportation, education, health and nutritional status, settlement and housing status, issues such as public-state relations under investigation. In surveys, the economic situation, land ownership, party products, credit facilities, cooperatives, marketing and market relations, forms of business power, the income levels of the families studied.
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House",doi:"10.5772/intechopen.103953",slug:"autonomous-update-of-a-dataset-for-anomaly-detection-services-in-elderly-care-smart-house-1",body:'
1. Introduction
According to the United Nations [1], in 2025, there would be a total of about 1.2 billion people over the age of 60. By 2050 this number would increase to 2 billion with 80% of them living in developing countries. The population growth has increased for older persons than for the rest of the population. In 1950 the total number of people over 60 years old was 8 percent. By 2007, this percentage had grown to 11 percent. By 2050, this number is projected to be at 22 percent. This kind of population growth comes with various challenges of its own [2].
This increase in the elderly population means more people would require assistive living care than they were before. Thus, Smart houses could play a key role in attending to this huge elderly population that needs assistive living care. A smart house is a special type of house that has automated services delivered by that house. Smart houses are of diverse types based on their purpose. Some of the common types of smart houses include (a) healthcare-oriented, (b) entertainment-oriented (c) security-oriented, and (d) energy-efficiency-oriented smart houses. In this research, we present a smart house model that is based on specific requirements for elderly citizens. With an emphasis on the needs of assistive technologies (AT), we shall recommend a smart house design. The design shall satisfy three major requirements which are vital for senior citizens. These three requirements are; (a) ATs services, (a) privacy requirements, and (c) security services. We are aiming to develop a smart house system which consists of several monitoring services This system should also enable modularization and allow easier replacement of components.
With the vast improvements in medicine and quality of living, many people are now living longer lives than previously was possible. This has been a result of vast investment in research that will improve quality of life. In trying to improve the quality of life, several researchers have attempted to provide a solution to care for senior citizens [3]. These solutions need enhancement to build completely novel solutions to deal with the growing demand for senior citizen care soon. The purpose of this work is to explore how this problem can be controlled using assistive technologies. To help assist with this issue we shall have to perform three experiments. We need to create a system that can be able to tell when an anomaly has occurred in the senior’s smart house. This requires knowing what is and what is not an anomaly. We have data that is recorded from the activity in the smart house using a conventional sensor such as those in mobile phone sensors or smartwatches. We shall require label data to determine if the data tread is normal or abnormal. This can be achieved by using the labeling used in the previous experiment dataset. Several publicly available datasets exist. Among the common dataset include Sisfall, MobiAct, Ucihar, Unifall, and Unimab datasets [4, 5, 6]. These datasets provide acceptable benchmarks to determine the classification of ADLs and falls. These could be used in the classification of data, or to assess a system’s accuracy.
In a smart house, assistive technologies are installed to detect abnormalities in human activity or environmental parameters. This is achieved using several methods. Three of the common methods are threshold, heuristics, and machine learning [7]. Threshold systems used specified rules in which a dataset is evaluated on those rules. Based on these rules a censored dataset can be labeled as a fall or not a fall. Using machine learning a different approach is used. A network is created which has node relationships that can be able to determine whether an activity is a fall or not has occurred. ML works similarly to a Blackbox solution as the rules are not logically deductible easily in the network c. Several machine learning classification algorithms exist in the labeling of subject data. Nine of the common classical ML algorithms are k-Means, Linear discriminant analysis (LDA), Naïve-Bayes, K-nearest neighbor (KNN), Vector support machine (SVM), Artificial neural network (ANN), Random Forest, and Decision trees [8].
Moreover, when collecting personal data, security and privacy should be considered. For example, cameras might capture more private information than smartwatches, some of this information can violate privacy. When using the toilet, the activity is not appropriate to record on camera while a smartwatch record of toilet activity might be more acceptable. Hence the choice of sensor method is especially important in developing this system. However, take note that it may be easy for detecting activity with a camera than with smartwatches. Therefore, a compromise needs to be considered in such cases.
The rest of this article is organized as follows; In Section 2, we describe some of the previous works related to ours by other researchers. In Section 3 we describe the methodology of what would be performed and how it would be performed. The section describes the flow of the algorithm and the dataset used. We then discuss the results of the experimental works in Section 4. In Section 5 we discuss issues that are related to our results and the future directions of this research. In Section 6 we present our findings and conclusion from this research and what is next.
2. Related research
Several people have attempted to solve this problem. The following are the most interesting of the research works which are of interest to this article’s research aims.
According to the article [3], a solution is proposed where a group of agents work together to sense communicate and interpret sensor data. The agents are seven types which consist of communication, sensor, refining, reconstruction, interpretation, prevention, and cognitive agents. The agents are separated into two groups. The group of agents each processes the sensor data and then aggregates the result to form a concrete decision. The first group of agents was prediction activities in the smart house. The performance was tabulated as 72.00% for machine learning, 88.00% for expert-knowledge agents, and 91.33% for meta-prediction agents. The last group of agents was prevention agents. This was a simulation and achieved 100% accuracy. However, the last group was not real-life but simulative experiment.
The researcher in the article [9] presents a monitoring system for senior citizens. If an anomaly is identified, then the system will send an alarm to a caregiver. Some activities monitored include waking up in the morning, preparing food, having breakfast, reading, working on a computer, having lunch, napping, or reading. An example of an anomaly is where the system detects that she woke up and starts walking around the house at 2 pm. This is an anomaly because this time is an awkward time for walking around the house. An alarm is evoked as this is not part of the normal schedule. A mock apartment was designed for use in this experiment.
The study in the article [10], graphical presents a comparison and heuristic technique was utilized in detecting falls. A publicly available suit GBAD test suite was defined. It selects the best subgraph or pattern and then compares it with the sensor data. Each graph is compared to the full graph using the formula. An abnormality can be identified from the graph in the suit presented.
2.1 Datasets with classification labels
In article [11] a dataset (Sisfall), and a fall detection algorithm are presented. The algorithms have five stages which are included in this order; sensor data, pre-process data feature extraction fall detection, and finally, call for help when a fall is detected. Four algorithms were used, and these are decision tree (DT), Logistic regression, k-nearest neighbor (KNN), and support vector machine. The dataset that was used is called Sisfall. Sisfall contains 15 types of falls and 19 types of ADL. These were performed by 23 young people aged 19 to 30 years old and 15 elderly people aged 60 to 75 years old. The recording frequency was 200 HZ for sampling. The sensors used were two accelerometers and one gyroscope. The accuracy was derived from the relationship between true positive (TP), true negatives (TN), false positive (FP), and false negatives (FN). Accuracy is defined as in Eq. (1) below.
Accuracy=TP+TNTP+FN+TN+FPx100E1
The accuracies recorded were DT at 99.02%, LR at 99.38%, KNN at 99.91%, and SVM at 99.98%. The most accurate results are the SVM classifier. SVM was found to have performed not only better in this experiment but also better than selected previous benchmark works of previous performances.
In article [12], a dataset (MobiFall) is presented. An experiment was conducted to standardize a dataset that can be used to determine if there is or no fall in a sensor. Datasets are used in machine learning to benchmark and identify specified activities. They compared two fall detection systems. One threshold-based system and another machine learning-based system. The machine learning system had a higher accuracy level. In this dataset, four kinds of falls were studied. Forward-lying, front-knees-lying, sideward-lying, and back-sitting-chair. Apart from fall detections, ADLs were also studied. These were nine which include standing, walking, jogging, jumping, starting up, stair down, sitting in a chair, car-step in, car step out. The sensor data used was from three types of sensors, the accelerometer, gyroscope, and orientation signals. The Size of displacement defined by the slope (SL) was measured using the formula SL given as in Eq. (2) below.
SL=maxx−minx2+maxy−miny2+maxz−minz2E2
Where the X stands for X-axis displacement, Y is the Y-axis displacement and Z is the Z-axis displacement.
Accuracy in fall detection was at 98% and in fall classification was at 68% using the 10-fold cross-validation. However, in another method where two-thirds are for training and one-third for testing, the accuracy was fall detection at 98.74 and fall classification at 68%. The dataset used is called MobiFall. This dataset is publicly available at Hellenic Mediterranean University (HMU) in Crete, Greece.
In article [13] a dataset (Ucihar) and six classifiers are used to detect falls. These involve distinguishing falls from ADLs. The six classifiers are the k-nearest neighbor (k-NN), least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), artificial neural networks (ANNs), and dynamic time warping (DTW), Fourteen people performed the experiment for data acquisition. The trial had 20 falls and 16 ADLs. The formula used to determine the total acceleration is given in Eq. (3) below.
AT=Ax2+Ay2+Az2E3
where Ax is acceleration in the x-axis, Ay is in the y-axis, and Ax is in the z-axis.
A database was created containing fall activities and ADLs. All the six algorithms performed at around 95% with K-NN and LSM being the most accurate. The researcher suggests using these two algorithms for live data stream detections. This dataset is accessible at the University of Irvine Machine Learning Repository.
2.2 Types and placement of sensors
For the recording and collection of data, specific sensors are acquired. Several types of sensors exist. Wearable sensors are sensors that can be placed on the body of interest. Environmental sensors are sensors that are not embedded in a body of interest. Data collection should be done at a regular frequency. However, we shall assume that the sensors wherever they would be placed would collect the same type of data at the same frequency. This is on the pretext that, if the location of the sensor is changed the frequency and the quality of data might as well change.
In article [14], two types of sensors are defined. Vision-based and sensor-based. The vision sensors use cameras of diverse types. These sensors however are not very much acceptable to the intended beneficiaries of the system. Another type is sensor-based, which includes wearables ambient sensors, and sensors on an object. This is the most used type as it is considered less intrusive and is more acceptable by potential beneficiaries. An accelerometer and gyroscope are two examples of wearable sensors. In article [15] three sensory systems are defined. These are wearables, vision, and ambient sensors which are a combination of visual with sound and location sensors. The visual sensors become more data accessing with the addition of location sensors and sound sensors. The human voice can also be used as input. A representation of the three types of sensors is shown below in Figure 1.
Figure 1.
Types of sensors in the system design.
In article [16] a system of sensors is proposed. Environmental and wearables sensors are combined to identify the location and the motion of the subject person. Each of these has achieved a significant level of activity classification accuracy. It is declared that using only an accelerometer the accuracy is 54.19%, while if you combined an accelerometer with environmental sensors the accuracy is 97.42%.
In research [17] the authors say the approach is intrusive as it requires active dressing, and it could be monitoring specific categories and not extensive categories of activities. It also might require the continuous wearing of the sensors throughout the day to enable data sensing. Therefore, researchers argue that environmental sensors are much better than wearable sensors. They provide experiments using a vision-based sensor. The activities involved include sitting, standing, walking, sleeping, getting assistance, and using the bedside commode and background. The camera has two kinds of data frames. A thermal defame and depth frame. They adjust each frame to its best resolution for better results. Although the authors acknowledge the intrusive and costly nature of vision sensors, they insist that more can be done with visual sensors to be used in the detection of activities in houses of seniors.
In article [18] a voice is used as input for a smart house. The smart house must interpret this voice according to the training and evoke some devices to perform a particular action. The purpose of the article was to provide voice input with a secure connection to a smart house with IoT Network. In article [19] the author mentions that training is a difficult part of a detection system. This is because the training dataset can become obsolete when the individual trained on it changes their usual pattern. The mentioned in cases when senior develops diabetics or drift concept. With advanced age, this leads to body deterioration which results in changes in gait characteristics of seniors [20]. A summary of sensors and classifiers from related works is shown below in Table 1.
No
Cited
Type of sensors used in experiment
Classifications models utilized
Dataset
Accuracy
1
3
Accelerometers
Random Forest, Hidden Markov Model, Support vector machine, Decision Tree (C45)
Lab Simulated
72.00%
2
9
Wearable sensors, Vicon system (PIR), camera
Dynamic Bayesian Network
Lab Simulated
99.00%
3
10
Infrared sensors, thermometers, object identifies, burning sensors, door sensors
Graph-Based Anomaly Detection (GBAD tool)
Kyoto dataset (CASAS-400)
n/a
4
11
Accelerometers, and a Gyroscope
Support vector machine, Decision Tree, Logistic Regression, K-Nearest Neighbor
Sisfall
99.98%
5
12
Accelerometer, Gyroscope and Orientation signal
K-Nearest Neighbor, Naïve Bayes, Decision tree-J48, Random Forest, Support vector machine
MobilFall
98.41%
6
13
Accelerometer, gyroscope, and magnetometer/campus
K-Nearest Neighbor, Support vector machine, BDM, Decision Tree, ANN
University of Irvine Machine
95.00%
7
15
Accelerometers and a Gyroscope
CNN-Long short-term memory (LSTM)
UMA Dataset
86.63%
8
16
Thermometer, Humidity sensor, Light sensors, Orientation and Motion sensors
Random Forest, Naive Bayes, Bayes Net, Logistic Regression, MLP, Radial basis function (RBF), Decision Table, Decision Trees(J48), Random Tree
Listing of sensors and classifier methods in selected previous research.
3. Research methodology
In this section, we shall describe the tools and the methods we shall use. We shall discuss the sensors used, the datasets used, the algorithms utilized and the floor plan of the apartment that will be used for the senior’s smart house.
3.1 Implementation procedure, software, and hardware
Since the research is about seniors, the dataset had to be slashed to only utilize data that applies to seniors. The age of participants was the main factor used to extract records. Some activities were removed as we considered them not necessary for seniors. Based on this In the Sisfall of 56,786 records, only 14,000 have seniors. In the MobiFall dataset on the 62,259 records, only 16,598 have seniors. In the Ucihar dataset on the 7352 records, only 2018 has seniors. After extracting these records, seven classifier algorithms were used to train the model on all three datasets and then validate the models.
The classification was for two separate tasks. The first task was fall detection. The MobiAct and Sisfall were used in this task. Each dataset has its subsection used in the experiment. The performances of the algorithms were compared to the dataset subclass from these two datasets. The best algorithm was identified. The second task was to identify if the ADLs were Laying or not. In this task, only the Ucihar dataset was used. The eight classifiers were again used to detect if Laying was the activity performed or not. This task was then linked to some other external tasks that tell the location where Laying was occurring if Laying was identified as the current ADLs.
The experiment was conducted using Python 3 software package. The computer system had the following specification: Processor: Intel(R) Core (TM) i7-4510U, CPU: 2.00GHz, 2 cores, RAM Memory: 4 GB DDR3 1600 MHz, OS: Windows 64 bits.
3.2 Sensors
The sensors we shall use in collecting data are a gyroscope and accelerometer for the XYZ plane. Nowadays These are readily available in mobile phones and smartwatch devices, which are usable for human activity recognition. The sensor using cameras and environment sensors has the advantage that they do not require excessive preparation and arrangement for senior citizens. However, the sense that the senior is under surveillance might be a discomforting feeling for most seniors. Therefore, this discomfort has made us decide to use an accelerometer and gyroscope other than cameras. Therefore, in this work gyroscope and accelerometer which are embedded in smart are utilized. This sensor data can be analyzed and processed in separate locations. The data we collected from the sensors is not labeled. Therefore, to enable labeling we have mapped our data to the data labeled by previous researchers. The labeled data will be obtained from publicly available data sets.
3.3 Datasets
Several datasets exist for human activity recognition (HAR) and fall classification. These can be used to classify test data as either a fall or an ADL. In our research, we shall use the publicly available datasets; SisFall, MobiAct, and Ucihar dataset.
In research [11, 21] the Sisfall dataset is generated in which most of the subjects are between the ages of 20 and 47 years. The primary research was to create a prototype dataset for fall detection. The second dataset is MobiAct in research [12, 22]. In the MobiAct dataset, the subjects’ ages are from 40 to 47 also doing both the falls and the ADL. This Sisfall and the MobiAct dataset are used primarily for the exploration of fall detection. The third dataset is the Ucihar [13, 23]. This dataset does explore the identification of ADL. There is no fall detection in Ucihar. We shall use this in ADL identification where appropriate as shall require a labeled dataset that could define ADL. In our research we focus on senior citizens hence we prefer data for people about 60-year-old and above. However, this data is not readily available hence shall show infer it in various datasets. Below is Table 2 which shows the composition of the dataset to be used in the experiment.
No.
Dataset
Human activity recognition samples
Fall detection samples
Total number of data samples
1
Sisfall
44,795
11,991
56,786
79%
21%
100%
2
MobiAct
50,188
12,071
62,259
81%
19%
100%
3
UCIHAR
7352
0
7352
100%
0%
100%
4
Total
102,335
24,062
126,397
81%
19%
100%
Table 2.
Composition of the selected datasets used in the experiment.
The experimental devices must be held compatible with mobile devices that were sensing the data. For fall detection we shall classify falls as dangerous activities. Therefore, we should need to send a warning message if a fall occurs unlike when an ADL occurs. The data has been labeled by the above public libraries (Sisfall, MobiAct, and Ucihar). We shall use the accelerometer, which records the speed of objects. And using this speed we could tell the presence or absence of a fall. The gyroscope would be used for rotational movements which is another parameter in the detection of a fall by the senior citizen. The accuracy is the efficiency of the system. We shall use selected sections from the three sample datasets, which are more appropriate per our requirements.
After preprocessing the raw data, a training and test dataset is derived which has a smaller number of records. The total data fields are nine both in MobiAct and Sisfall as shown in Figures 2 and 3 respectively.
Figure 2.
Sample contents of the Sisfall dataset.
Figure 3.
Sample contents of the MobiAct dataset.
Ucihar is for the activity classification classifier. Unlike the above dataset, the original dataset has a total of 548 columns. While Figure 4 below shows only 14 columns that have the largest value in importance for classifier purposes.
Figure 4.
Sample contents of the Ucihar dataset.
In the sample datasets, there are various scenarios of the data record. However, in this research, only the scenarios that are best suited to our purpose were used. The human activities include that were studied include, Standing, Sitting, Laying, walking fast, walking slow, walking downstairs, and walking upstairs [24]. We divided these activities into two groups per dataset. Group 1 contains ADLs and group 0 contains falls. There are four types of fall activities and six types of ADLs. For data preprocessing, an operation is performed to divide records into two groups. In the fall detection procedure, MobiAct and Sisfall datasets will groups ADLs as zeros and all the several types of falls as ones. In the activity recognition procedure, the data in the Ucihar dataset’s group consisting of Laying activity (sleeping) is labeled as 1, and all other activities are grouped under label 0. By creating these two groups we can thus use a simpler classifier, binary classification, instead of multiple classifications.
3.4 Algorithms selection from common algorithms
Several algorithms exist for anomaly detection systems. For the execution of detection falls, we shall utilize eight different machine learning algorithms [25]. These algorithms would be compared, and the best should be used in the application of the smart house. Eight algorithms are selected for this experiment as follows. Logistic regression, Linear discriminate analysis (LAD), k-nearest neighbors (k-NN), decision tree classifier, Gaussian naive Bayes, Support Vector Machine (SVM), Random Forest, and xgboost algorithms. The best performing among these algorithms is to be utilized. The algorithm’s performance would have to be weighed by the following parameters.
Optimization—eliminate the worst performing algorithms
Completeness—eliminate some other solution where a result is returned
Accuracy and precision—the degree of accuracy attained and required
Execution time—period of performance the classification
Resource consumption—memory and processor usage
The combination of human activity detection and the fall detection algorithm is detected based on these above four factors utilized.
3.5 Design of the floor plan of the smart house
Figure 5 below which shows the simulated floor plan of the smart house. The sample house has the following floor plan. A1 and A2 are bedrooms for sleeping. B1 and B2 are corridors. C is the toilet; D is the Bathroom and E is the kitchen for cooking.
Figure 5.
An illustration of the sample smart house floor plan design.
3.6 Remainder alarm for medicine taking routine and camera/pressure mat
There would be a remainder device. This becomes the third Component of the system. The first is fall detection, the second is activity classification and the third is alarm detection and camera. Given the apartment above, we must use the fall data and the human activity recognition. We should make these assumptions.
Non-serious fall is considered a fall. No alarm but warning recorded
Sleeping occurs in room A1 or A2. Send alarm if sleeping anywhere else.
Laying outside of rooms A1 and A2 should trigger a warning
Activity should not switch abruptly. For example, sleep to walk to walk
Being in the toilet for a lengthy period indicates a problem, hence alarm is evoked,
A medical dispensary is kept in the room (E). A pressure sensor sends a photo when a pill is removed from the dispensary. After 5 minutes after the scheduled time for taking the pill passes, then an alert is sent out to the caregiver.
These six points are used as the heuristic when identified. Once it is identified then an alarm or warning is evoked. We need to conduct two tests before we send an alarm as a way of avoiding false alarms. Identify the activity and then identify the room and the applicable algorithm’s location also establish that Laying is not in an inappropriate room. Furthermore, a delay in the bathroom should trigger a warning. The second algorithm verifies that a fall is not a Laying, and a Laying is not a fall. Once this is established only then can an alarm to send. This could reduce false alarms and increase confidence levels unlike having one algorithm.
3.7 Description of the three algorithms
Three algorithms are derived to execute the above procedures. Below is the description of the three algorithms pseudocode.
Algorithm 1: Update datasets, test, and use them in future training.
01: Retrieve sample records from the datasets #1, #2, and #3.
02: train model using standard algorithms and records of the subject person.
03: If the time elapses sent collected data for analysis by the selected algorithms.
04: end if.
05: if the algorithm accuracy is top two use the best and then discard the rest.
06: end if.
07: process and identify the chances of ADL.
08: process and identify the chances of a FALL.
09: if ADL is sleeping but the room is not sleeping quarters send an alert signal.
10: endif.
11: if a Fall is detected sent an alert signal.
12. else save the data and then move into the waiting stage.
13: endif.
14. Repeat the strategy starting from point 01.
Algorithm 2: Sleeping area locator for logical heuristic missed fall prediction.
01: Retrieve sample records from the datasets #1, #2, and #3.
02: train model using standard algorithms and records of the subject person.
03: if the HAR is laying or napping find out which room is activity occurring.
04. If the room is not appropriate for Sleeping quarters, send a warning alarm.
05. If sleep is in the sleeping quarter’s location, then move to the waiting stage.
06: end if.
07. Repeat the strategy starting from point 01.
Algorithm 3: Medical remainder algorithm, to predict skipping of medicine routine.
01: Retrieve from schedule records on times required for taking medication.
02: At each required time check the weight of the medicine pressure.
03: If the weight has been adjusted then its confirmed medicine has been taken.
04: If the weight is still the same then send a warning signal to inform caretaker.
05. else save the data and get a new data sample.
06: endif.
07. Repeat the strategy starting from point 01.
Based on the above algorithms, the alarm is triggered as a response. These responses will differentiate possible similar activities (such as laying and falling) before evoking the alarm. As shown in Table 3, when the results of the algorithm are as provided in Answer (I) then the Alarm is evoked. If the answers are as in Answer (II) then a warning is logged in a database. Three warnings in a sequence also trigger an alarm.
Algorithms
Tests before triggering an alarm
Answer(I)
Answer (II)
1
Falling has been detected or not?
Yes
No
2
Laying has been detected or not?
No
Yes
3
Laying occurring in an appropriate room?
No
Yes
Result
Send alarm to Caregiver
Yes
No
Table 3.
Question to answer before invoking a particular algorithm.
3.8 Updating training dataset
The data collected from the senior citizen’s sensor is originally not labeled. When a detection process is completed the sensor data would then be assigned a label. Once labeled, then the system would save this information with its given label. After the label is authenticated, this record is then moved to the created dataset for extension of the original dataset. At this point, the system would save the labeled data into a new dataset which is the original dataset plus the new record. The record can then be used in training sessions. This new dataset would have an extra record that more closely represent the person involved. In this case, the training would reflect the subject senior citizen. Below is Figure 6 showing the systems’ flow chart.
Figure 6.
Flow chart of creation of custom datasets.
In Article [3] similar research is presented. The authors make a comparison of three datasets and look at the performances of different algorithms. In this work, we have compared results from two datasets for the human activity detection algorithms. The results would be fused to reduce the probability of false positive or false negative.
4. Results
Eight algorithms had their performance studies as indicated in Figure 7 below. Of these eight, some would be discarded in preference for the best-performing algorithm. During the experiment, algorithm ranking was established. The lower-ranked as less effective algorithms are to be eliminated. This reduced number of options increases the efficiency as unfavorable options are not computed. The extra computation would be the worst of resources. The selecting of the best option thus avoids unnecessary use of computing power in analyzing some irrelevant options. The less likely algorithm options are removed immediately when identified. Below is Figure 7 which is a snapshot of the accuracies of various investigated methods.
Figure 7.
Comparison of algorithm performance per dataset.
In Figure 7, Algorithm KNN is more accurate for the Sisfall dataset, and XGBoost is more accurate for both MobiAct and Ucihar datasets. A selection of three of the graphs of the accuracy with the best performance is presented below. The next two graphs are for fall classification and the second is for human activity classification.
The detection accuracy for the Sisfall dataset is indicated as 98.08% for the training session and 97.92% for the testing session as shown below in Figure 8. This is the best accuracy from the list of classifiers in the experiment.
Figure 8.
Results for accuracy simulation using the Sisfall dataset.
The detection accuracy for MobiAct dataset accuracy is indicated in Figure 9 at 99.23% for the training session and 98.84% for the testing session. This is the best accuracy from the list of classifiers in the experiment.
Figure 9.
Results for accuracy simulation using MobiAct dataset.
The detection accuracy for Ucihar dataset accuracy is indicated at 96.85% for the training session and 95.21% for the testing session as shown below in Figure 10. The Ucihar accuracy at 96.85% is the worst accuracy of the usages of XGBoost classifiers.
Figure 10.
Results for the accuracy simulation using the Ucihar dataset.
The Sisfall test dataset has 14,783 ADLs cases and 3775 fall cases; Figure 11 shows the confusion matrix for the Sisfall test dataset during the training session.
Figure 11.
Confusion matrix for XGBoost classifier on Sisfall dataset.
The MobiAct test dataset has 16,562 ADLs cases and 3984 fall cases; Figure 12 shows the confusion matrix for the MobiAct test dataset.
Figure 12.
Confusion matrix for XGBoost classifiers on MobiAct dataset.
The Ucihar test dataset has 1963 ADLs cases and 464 Laying cases; Figure 13 shows the confusion matrix for the Ucihar test dataset.
Figure 13.
Confusion matrix for XGBoost classifier for Ucihar dataset.
From the confusion matrix, we can extract the accuracy, sensitivity, and specificity of our classifier. The higher each of these parameters the better the performance of the classifier. However, accuracy must be considered in conjunction with specificity and sensitivity. A classifier must have a high sensitivity and specificity, to be defined as having superior performance. As seen in Table 4 below, both sensitivity and specificity are above 78% which is high performing case. This shows that the one selected option from the eight models performs quite well and can be used to develop the proposed system. As indicated in Figure 10, the MobiAct dataset accuracy is recorded at 98% for the training and at 99% for the testing which is the best accuracy of our possible classifiers. Below is Table 4 which indicates the performance of the most effective classier(XGboost) in the experiment.
Dataset Extracts
True / False
False / True
Class Total
Final Total
Accuracy
Sensitivity
Specificity
ADL(Sisfall)
14,719
64
14,783
18,739
98.69%
95.42%
99.57%
Fall (Sisfall)
181
3775
3956
ADL(MobiAct)
16,547
15
16,562
20,546
99.73%
98.97%
99.91%
Fall (MobiAct)
41
3943
3984
ADL(Ucihar)
1921
42
1963
2427
94.19%
78.66%
97.86%
Laying(ucih..)
99
365
464
2427
Table 4.
Indicators of classifier efficiency.
The high accuracy indicates that the models were efficient and can be used in detections. However, when executing these algorithms speed and accuracy are a factor in optimization. If given more time an algorithm can perform better. However, this has to be considered with efficiency on time when an anomaly is reported. If it takes too much time to compute a high accuracy decision, it could be that by the time the decision is taken it’s too late for the damage already done. Below is Figure 14 which indicates the time it took for each algorithm to complete a single task.
Figure 14.
Recorded time consumed per tested algorithm.
Moreover, these eight machine learning methods were compared with deep learning. Deep learning has multiple models chained together to enhance performance. However, the computation costs were more than for the machine learning method.
The accuracy deep learning method on the Sisfall dataset Sisfall selection dataset accuracy is recorded at 96.84% for the training session and at 93.55% for the validation session as indicated below in Figure 15.
Figure 15.
Deep learning accuracy on the Sisfall dataset.
Deep learning accuracy on the MobiAct dataset selection dataset accuracy is recorded at 96.97% for the training session and at 100.0% for the validation session as indicated below in Figure 16.
Figure 16.
Deep learning accuracy on the MobiAct dataset.
Deep learning accuracy on the Ucihar dataset Sisfall selection dataset accuracy is recorded at 98.96% for the training session and at 100.0% for validation as indicated below in Figure 17.
Figure 17.
Deep learning accuracy on the Ucihar dataset.
5. Discussions and future works
The results show that the fall detection and activity recognition algorithms are competitive. The fall detection was at 96% minimum accuracy, which is above average for the cited other researchers’ works which had an average rate of 89% as shown in Table 1. We have used cross verification with other dataset records. This is with the hope that the subject senior citizens’ data would have to be recorded and then observed to make the correct adjustments. The activity recognition was the most accurate. It performed at 98.96% for training and 100.0% for the validation session. The general accuracy for the xgboost algorithm was above 96% on each of the three datasets. However, the deep learning method was the most accurate. After a successful classification, we can add the new record and its classification label to the dataset. This thus extends and improves the dataset. We could also include one heuristic to improve accuracy and reduce computational costs. For instance, we should be able to tell that sleeping must have zero chance of occurring in the toilet, hence not consider it a possibility when computing. This allows resources to be concentrated on viable options when performing classification. Eliminating such computation decision cost can fine-turn the system, as it allows only options with high likelihood. The low likelihood options are removed from the allocation of computation resources. This allocation of energy to the option improves efficiency. In the case that one class is larger than another the dataset is unbalanced. In usual cases this system works on unbalanced datasets, hence it was important to have good characteristics in its sensitivity and specificity.
5.1 Edge processing and security
In this work, the data sensing was performed using non-protruding methods which are a mobile phone and a smartwatch data sensor. However, this method is replaceable in the structure of the system. The data can also be collected by using a camera. A labeled dataset is then used to label extracted images. Mobile collects data and then this data is not processed on the phone but sent to some processing point due to limitations of the processing capability of the phone [26]. The training and testing set labels the images. However, in our project, we use an accelerometer and gyroscope as input sensors. The rest of the system would be the same. A systems camera sensor is an advantage in that it would be easier to label.
5.2 Replacement of training and testing dataset
When data is collected from the subject because seniors change rapidly for deteriorate help, the trend of that senior would change. Hence the dataset must be properly monitored and adjusted to match the rate of changes in the trend of the senior. Otherwise maintaining the same training data for an extended period would result in an obsolete detection system [20]. Aging can change the gait pattern of an individual hence the importance to update the dataset constantly. Having been limited by the current covid-19 situation we are having difficulty arranging our data collection activities. However, we believe the public datasets, SisFall, MobiAct, and Ucihar have provided good insights into the record we could have managed to collect. We believe these datasets have an unobstructed view of the results we could have obtained. After collecting sensor data and labeling it, the dataset component from Sisfall, MobiAct, and Ucihar databases would be gently removed and replaced with these records. This is a continuous process until the dataset remains pure containing the new record of the senior citizens without the legacy dataset. Erroneous labeled data would continuously be removed to have a robust and current training dataset.
5.3 The medicine routine remainder service
The medicine remainder is a time-based scheduler. The schedule must be executed correctly and if not, then an alarm is sent. The alarm is triggered based on the failure of sending confirmation of executing the medicine schedule by the senior. The senior must send a confirmation once prompted to do so. However, there is a risk that the subject might be able to falsely confirm they took the medicine when in fact they did not take it. This system currently cannot help when the senior is specifically not providing the correct state of medicine routine. It is meant to help in cases where participants are willing to take the medicine. An alert will be sent to caregivers allowing them to prompt the senior on the state of their medicine routine.
6. Conclusion
We commend this system can evaluate and monitor the situation and status of the senior citizen in their apartment. This system has been designed using three main algorithms. First algorithm tests if a fall has occurred or not. Eight different common algorithms are evaluated to see which one is most effective. If a fall is detected, then the caregiver is alerted. The second algorithm is to identify what activity is the senior citizen doing. This algorithm has the purpose of detecting the location where activity is occurring. If an activity such as sleeping or laying is taking place in the wrong location an alert is evoked. The last algorithm is detecting whether the medicine routine prescribed is been executed or not. The system utilizes a dispenser that can record if a pill was extracted from the pill dispenser or not. This is checked at a specified period as scheduled. When there is no change in the medical container after the expected pill dispensation time elapsed, then an alert warning message is sent to caregivers. The medical remainder also enables caregivers to prompt subject seniors to implement the medicine routine, when medicine taking has been skipped.
These three algorithms are the primary functions of this smart house design. The system utilizes the executed algorithms to effect detection. The system must use few resources and utilize the improved performance algorithm. The design would be updated with specific data when training for a specific client. The sample, data is also to be replaced with the change in the pattern of the senior citizen trends. The final efficiency in the algorithms improves from 96–98% in the training session and the validation is at 85–100% for the testing session. Since the customized records are generated from the citizen, the training and validation are guaranteed to improve at every iteration. With a sensitivity minimum of 78.66% and a specificity minimum of 97.86%, the model is performing well as the dataset used is not balanced.
This prototype would allow using the most effective classifier and dynamically determine the most effective classifier. Dynamic evaluation of algorithm efficiency should be integrated, as the accuracy would not always be the best since training data is updated periodically. Using the location variable may also reduce the computing resources needed if integrated into the classifier algorithm. Without a logic heuristic, the computation process would require more resources.
Acknowledgments
This work was supported by IGA/CebiaTech/2021/001, a research project of the Faculty of Applied Informatics, Tomas Bata University in Zlín.
\n',keywords:"artificial intelligence, machine learning, human activity recognition, activity of daily living, fall detection, fall prevention",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82670.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82670.xml",downloadPdfUrl:"/chapter/pdf-download/82670",previewPdfUrl:"/chapter/pdf-preview/82670",totalDownloads:14,totalViews:0,totalCrossrefCites:0,dateSubmitted:"January 4th 2022",dateReviewed:"February 25th 2022",datePrePublished:"July 13th 2022",datePublished:null,dateFinished:"July 13th 2022",readingETA:"0",abstract:"This work proposes a smart system that could be useful in the delivery of elderly care services. Elderly care is a set of services that are provided to senior citizens to help them have a more comfortable and independent life which would not be possible without these services. This proposed system is unique in that it combines the detection algorithm with the automatic update of the dataset. It also uses a heuristic mechanism to reduce false detections. This is on the premise that the AI effort is good, but it could be made better with the inclusion of heuristics. Fall detection accuracy is initially solved by the first classifier, then another classifier evaluates the result with inferences before evoking an alarm. It checks the location of the subject to use in its inferences. Hence the smart house design consists of two machine learning systems. One system performs human activity classification while the other performs fall occurrence detection. Of the eight different classification methods utilized, XGBoost was most accurate with an average of 97.65% during training. A customized dataset is then generated with newly labeled data hence improving system performance.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82670",risUrl:"/chapter/ris/82670",signatures:"Linos Nchena and Martin Tomášek",book:{id:"11197",type:"book",title:"Internet of Things - New Trends, Challenges and Hurdles",subtitle:null,fullTitle:"Internet of Things - New Trends, Challenges and Hurdles",slug:null,publishedDate:null,bookSignature:"Ph.D. Manuel Jesus Dominguez-Morales, Dr. Ángel Jesús Varela Vaca and Dr. Lourdes Miró Amarante",coverURL:"https://cdn.intechopen.com/books/images_new/11197.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-036-6",printIsbn:"978-1-80356-035-9",pdfIsbn:"978-1-80356-037-3",isAvailableForWebshopOrdering:!0,editors:[{id:"144865",title:"Ph.D.",name:"Manuel",middleName:"Jesus",surname:"Dominguez-Morales",slug:"manuel-dominguez-morales",fullName:"Manuel Dominguez-Morales"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. 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Discussions and future works",level:"1"},{id:"sec_15_2",title:"5.1 Edge processing and security",level:"2"},{id:"sec_16_2",title:"5.2 Replacement of training and testing dataset",level:"2"},{id:"sec_17_2",title:"5.3 The medicine routine remainder service",level:"2"},{id:"sec_19",title:"6. Conclusion",level:"1"},{id:"sec_20",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'World Health Organization. Active Aging: A Policy Framework. No. WHO/NMH/NPH/02.8. Madrid Spain: World Health Organization; 2002'},{id:"B2",body:'Sander M, Oxlund B, Jespersen A, Krasnik A, Mortensen EL, Westendorp RGJ, et al. The challenges of human population ageing. Age and Ageing. 2015;44(2):185-187'},{id:"B3",body:'Kaluža B, Mirchevska V, Dovgan E, Luštrek M, Gams M. An agent-based approach to care in independent living. In: International Joint Conference on Ambient Intelligence. Berlin, Heidelberg: Springer; 2010. pp. 177-186'},{id:"B4",body:'Islam MM, Tayan O, Islam MR, Islam MS, Nooruddin S, Kabir MN, et al. Deep learning based systems developed for fall detection: A review. IEEE Access. 2020;8:166117-166137'},{id:"B5",body:'Reyes-Ortiz J-L, Oneto L, Sama A, Parra X, Anguita D. Transition-aware human activity recognition using smartphones. Neurocomputing. 2016;171:754-767'},{id:"B6",body:'He J, Zhang Z, Wang X, Yang S. A low power fall sensing technology based on FD-CNN. IEEE Sensors Journal. 2019;19(13):5110-5118'},{id:"B7",body:'Xu T, Se H, Liu J. A two-step fall detection algorithm combining threshold-based method and convolutional neural network. Metrology and Measurement Systems. 2021;28(1):23-40'},{id:"B8",body:'Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest research trends in fall detection and prevention using machine learning: A systematic review. Sensors. 2021;21(15):5134'},{id:"B9",body:'Zhu C, Sheng W, Liu M. Wearable sensor-based behavioral anomaly detection in smart assisted living systems. IEEE Transactions on Automation Science and Engineering. 2015;12(4):1225-1234'},{id:"B10",body:'Paudel R, Eberle W, Holder LB. Anomaly detection of elderly patient activities in smart homes using a graph-based approach. In: Proceedings of the 2018 International Conference on Data Science. United States: CSREA Press; 2018. pp. 163-169. ISBN: 1-60132-481-2'},{id:"B11",body:'Hussain F, Umair M, Ehatisham-Ul-Haq M, Pires I, Valente T, Garcia N, et al, editors. An efficient machine learning-based elderly fall detection algorithm. In: SENSORDEVICES 2018, the Ninth International Conference on Sensor Device Technologies and Applications, Venice, Italy, 16–20 September 2018. United States of America: Xpert Publishing Services; 2018'},{id:"B12",body:'Vavoulas G, Pediaditis M, Chatzaki C, Spanakis EG, Tsiknakis M. The mobifall dataset: Fall detection and classification with a smartphone. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR). 2014;2(1):44-56'},{id:"B13",body:'Özdemir AT, Barshan B. Detecting falls with wearable sensors using machine learning techniques. Sensors. 2014;14(6):10691-10708'},{id:"B14",body:'Bouchabou D, Nguyen SM, Lohr C, LeDuc B, Kanellos I. A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning. Sensors. 2021;21(18):6037'},{id:"B15",body:'Wisesa IWW, Genggam Mahardika. Fall detection algorithm based on accelerometer and gyroscope sensor data using recurrent neural networks. In IOP Conference Series: Earth and Environmental Science. Vol. 258, No. 1. United Kingdom: IOP Publishing; 2019. p. 012035'},{id:"B16",body:'Jin M, Zou H, Weekly K, Jia R, Bayen AM, Spanos CJ. Environmental sensing by wearable device for indoor activity and location estimation. In: IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society. United States of America: IEEE; 2014. pp. 5369-5375'},{id:"B17",body:'Luo Z, Hsieh J-T, Balachandar N, Yeung S, Pusiol G, Luxenberg J, et al. Computer vision-based descriptive analytics of seniors’ daily activities for long-term health monitoring. Machine Learning for Healthcare (MLHC). 2018;2:1'},{id:"B18",body:'Venkatraman S, Overmars A, Thong M. Smart home automation—Use cases of a secure and integrated voice-control system. Systems. 2021;9(4):77'},{id:"B19",body:'Mahfuz S, Isah H, Zulkernine F, Nicholls P. Detecting irregular patterns in IoT streaming data for fall detection. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). United States of America: IEEE; 2018. pp. 588-594'},{id:"B20",body:'Delahoz YS, Labrador MA. Survey on fall detection and fall prevention using wearable and external sensors. Sensors. 2014;14(10):19806-19842'},{id:"B21",body:'Sucerquia A, López JD, Vargas-Bonilla JF. SisFall: A fall and movement dataset. Sensors. 2017;17(1):198'},{id:"B22",body:'Vavoulas G, Chatzaki C, Malliotakis T, Pediaditis M, Tsiknakis M. The MobiAct dataset: Recognition of activities of daily living using smartphones. In: International Conference on Information and Communication Technologies for Ageing Well and e-Health. Vol. 2. Portugal: SCITEPRESS; 2016. pp. 143-151'},{id:"B23",body:'Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL. Energy efficient smartphone-based activity recognition using fixed-point arithmetic. Journal of Universal Computer Science. 2013;19(9):1295-1314'},{id:"B24",body:'Liu L, Hou Y, He J, Lungu J, Dong R. An energy-efficient fall detection method based on FD-DNN for elderly people. Sensors. 2020;20(15):4192'},{id:"B25",body:'Thakur N, Han CY. A study of fall detection in assisted living: Identifying and improving the optimal machine learning method. Journal of Sensor and Actuator Networks. 2021;10(3):39'},{id:"B26",body:'Pan D, Liu H, Dongming Q, Zhang Z. CNN-based fall detection strategy with edge computing scheduling in smart cities. Electronics. 2020;9(11):1780'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Linos Nchena",address:"nchena@utb.cz",affiliation:'
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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:
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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secondary metabolites are having the great application in human health and nutritional aspect. Plant cell and organ culture systems are feasible option for the production of secondary metabolites that are of commercial importance in pharmaceuticals, food additives, flavors, and other industrial materials. The stress, including various elicitors or signal molecules, often induces the secondary metabolite production in the plant tissue culture system. The recent developments in elicitation of plant tissue culture have opened a new avenue for the production of secondary metabolite compounds. Secondary metabolite synthesis and accumulation in cell and organ cultures can be triggered by the application of elicitors to the culture medium. Elicitors are the chemical compounds from abiotic and biotic sources that can stimulate stress responses in plants, leading to the enhanced synthesis and accumulation of secondary metabolites or the induction of novel secondary metabolites. Elicitor type, dose, and treatment schedule are major factors determining the effects on the secondary metabolite production. The number of parameters, such as elicitor concentrations, duration of exposure, cell line, nutrient composition, and age or stage of the culture, is also important factors influencing the successful production of biomass and secondary metabolite accumulation. This chapter reviews the various abiotic and biotic elicitors applied to cultural system and their stimulating effects on the accumulation of secondary metabolites.",book:{id:"5066",slug:"abiotic-and-biotic-stress-in-plants-recent-advances-and-future-perspectives",title:"Abiotic and Biotic Stress in Plants",fullTitle:"Abiotic and Biotic Stress in Plants - Recent Advances and Future Perspectives"},signatures:"Poornananda M. Naik and Jameel M. Al–Khayri",authors:[{id:"176282",title:"Prof.",name:"Jameel M.",middleName:null,surname:"Al-Khayri",slug:"jameel-m.-al-khayri",fullName:"Jameel M. 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Due to advances in molecular approaches during the last decades, nowadays it is possible to develop economically important transgenic crops that have increased tolerance to stresses. This chapter discusses the oxidative stress and damage to plants. In addition, it reports the involvement of antioxidant enzymes in the tolerance of plants to various stresses.",book:{id:"5066",slug:"abiotic-and-biotic-stress-in-plants-recent-advances-and-future-perspectives",title:"Abiotic and Biotic Stress in Plants",fullTitle:"Abiotic and Biotic Stress in Plants - Recent Advances and Future Perspectives"},signatures:"Andréia Caverzan, Alice Casassola and Sandra Patussi Brammer",authors:[{id:"176303",title:"Dr.",name:"Alice",middleName:null,surname:"Casassola",slug:"alice-casassola",fullName:"Alice Casassola"},{id:"176409",title:"Dr.",name:"Andréia",middleName:null,surname:"Caverzan",slug:"andreia-caverzan",fullName:"Andréia Caverzan"},{id:"176410",title:"Dr.",name:"Sandra",middleName:null,surname:"Patussi Brammer",slug:"sandra-patussi-brammer",fullName:"Sandra Patussi Brammer"}]}],mostDownloadedChaptersLast30Days:[{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:4174,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",book:{id:"8502",slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",title:"Plant Science",fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro"},signatures:"Admasu Moges and Yohannes Moges",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",middleName:null,surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",middleName:null,surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}]},{id:"63148",title:"Domestic Livestock and Its Alleged Role in Climate Change",slug:"domestic-livestock-and-its-alleged-role-in-climate-change",totalDownloads:15946,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"It is very old wisdom that climate dictates farm management strategies. In recent years, however, we are increasingly confronted with claims that agriculture, livestock husbandry, and even food consumption habits are forcing the climate to change. We subjected this worrisome concern expressed by public institutions, the media, policy makers, and even scientists to a rigorous review, cross-checking critical coherence and (in)compatibilities within and between published scientific papers. Our key conclusion is there is no need for anthropogenic emissions of greenhouse gases (GHGs), and even less so for livestock-born emissions, to explain climate change. Climate has always been changing, and even the present warming is most likely driven by natural factors. The warming potential of anthropogenic GHG emissions has been exaggerated, and the beneficial impacts of manmade CO2 emissions for nature, agriculture, and global food security have been systematically suppressed, ignored, or at least downplayed by the IPCC (Intergovernmental Panel on Climate Change) and other UN (United Nations) agencies. Furthermore, we expose important methodological deficiencies in IPCC and FAO (Food Agriculture Organization) instructions and applications for the quantification of the manmade part of non-CO2-GHG emissions from agro-ecosystems. However, so far, these fatal errors inexorably propagated through scientific literature. Finally, we could not find a clear domestic livestock fingerprint, neither in the geographical methane distribution nor in the historical evolution of mean atmospheric methane concentration. In conclusion, everybody is free to choose a vegetarian or vegan lifestyle, but there is no scientific basis, whatsoever, for claiming this decision could contribute to save the planet’s climate.",book:{id:"7491",slug:"forage-groups",title:"Forage Groups",fullTitle:"Forage Groups"},signatures:"Albrecht Glatzle",authors:[{id:"252990",title:"Dr.",name:"Albrecht",middleName:null,surname:"Glatzle",slug:"albrecht-glatzle",fullName:"Albrecht Glatzle"}]},{id:"66714",title:"Biotic and Abiotic Stresses in Plants",slug:"biotic-and-abiotic-stresses-in-plants",totalDownloads:5911,totalCrossrefCites:60,totalDimensionsCites:106,abstract:"Plants are subjected to a wide range of environmental stresses which reduces and limits the productivity of agricultural crops. Two types of environmental stresses are encountered to plants which can be categorized as (1) Abiotic stress and (2) Biotic stress. The abiotic stress causes the loss of major crop plants worldwide and includes radiation, salinity, floods, drought, extremes in temperature, heavy metals, etc. On the other hand, attacks by various pathogens such as fungi, bacteria, oomycetes, nematodes and herbivores are included in biotic stresses. As plants are sessile in nature, they have no choice to escape from these environmental cues. Plants have developed various mechanisms in order to overcome these threats of biotic and abiotic stresses. They sense the external stress environment, get stimulated and then generate appropriate cellular responses. They do this by stimuli received from the sensors located on the cell surface or cytoplasm and transferred to the transcriptional machinery situated in the nucleus, with the help of various signal transduction pathways. This leads to differential transcriptional changes making the plant tolerant against the stress. The signaling pathways act as a connecting link and play an important role between sensing the stress environment and generating an appropriate biochemical and physiological response.",book:{id:"8015",slug:"abiotic-and-biotic-stress-in-plants",title:"Abiotic and Biotic Stress in Plants",fullTitle:"Abiotic and Biotic Stress in Plants"},signatures:"Audil Gull, Ajaz Ahmad Lone and Noor Ul Islam Wani",authors:null},{id:"62573",title:"Introductory Chapter: Terpenes and Terpenoids",slug:"introductory-chapter-terpenes-and-terpenoids",totalDownloads:7635,totalCrossrefCites:29,totalDimensionsCites:56,abstract:null,book:{id:"6530",slug:"terpenes-and-terpenoids",title:"Terpenes and Terpenoids",fullTitle:"Terpenes and Terpenoids"},signatures:"Shagufta Perveen",authors:[{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen"},{id:"192994",title:"Dr.",name:"Areej",middleName:null,surname:"Al-Taweel",slug:"areej-al-taweel",fullName:"Areej Al-Taweel"}]},{id:"62876",title:"Introduction to Phytochemicals: Secondary Metabolites from Plants with Active Principles for Pharmacological Importance",slug:"introduction-to-phytochemicals-secondary-metabolites-from-plants-with-active-principles-for-pharmaco",totalDownloads:5894,totalCrossrefCites:11,totalDimensionsCites:30,abstract:"Phytochemicals are substances produced mainly by plants, and these substances have biological activity. In the pharmaceutical industry, plants represent the main source to obtain various active ingredients. They exhibit pharmacological effects applicable to the treatment of bacterial and fungal infections and also chronic-degenerative diseases such as diabetes and cancer. However, the next step in science is to find new ways to obtain it. In this chapter, we discuss about the main groups of phytochemicals, in addition to presenting two case studies. One of the most important secondary metabolites is currently Taxol, which is a natural compound of the taxoid family and is also known for its antitumor activity against cancer located in breasts, lungs, and prostate and is also effective with Kaposi’s sarcoma. Our case studies will be about Taxol, extracted from an unexplored plant species, and the production of Taxol by its endophytic fungi.",book:{id:"6794",slug:"phytochemicals-source-of-antioxidants-and-role-in-disease-prevention",title:"Phytochemicals",fullTitle:"Phytochemicals - Source of Antioxidants and Role in Disease Prevention"},signatures:"Nadia Mendoza and Eleazar M. Escamilla Silva",authors:[{id:"51406",title:"Dr.",name:"Eleazar",middleName:"Máximo",surname:"Escamilla Silva",slug:"eleazar-escamilla-silva",fullName:"Eleazar Escamilla Silva"},{id:"243304",title:"Ph.D. Student",name:"Nadia",middleName:null,surname:"Mendoza",slug:"nadia-mendoza",fullName:"Nadia Mendoza"}]}],onlineFirstChaptersFilter:{topicId:"41",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82159",title:"Chlorophyll and Its Role in Freshwater Ecosystem on the Example of the Volga River Reservoirs",slug:"chlorophyll-and-its-role-in-freshwater-ecosystem-on-the-example-of-the-volga-river-reservoirs",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.105424",abstract:"The present chapter has the aim to considerate the most significant aspects of chlorophyll (Chl) applications in the ecological study of fresh waters on the example of the Volga River reservoirs. Throughout the cascade of seven large reservoirs, Chl varied in wide range from 2.5–9 to over 100 μg/L with mean values of 16.5–41.2, 6.7–44.0, and 3.6–10.6 μg/L in the Upper, Middle, and Lower Volga, respectively. Mean Chl values that constantly decrease from the Upper Volga to Lower Volga, characterize Ivankovo, Uglich, and Cheboksary reservoirs as eutrophic, Saratov and Volgograd reservoirs as mesotrophic, while Gorky and Kuibyshev reservoirs in some years are mesotrophic or eutrophic. Chl seasonal dynamics in the Rybinsk reservoir that is dynamics of phytoplankton biomass, is characterized by spring, summer, and, in some years, autumn maxima. Water temperature and water regime of the reservoir are the main factors in Chl dynamics. Years with low-water conditions are favorable for the high Chl concentrations and intensive development of algae. Seasonally average Chl that make from 5 to 22 μg/L during 1969–2019, show variations in trophic state of reservoir from mesotrophic (Chl < 10 μg/L), to moderately eutrophic (10–15 μg/L), and eutrophic (15–22 μg/L).",book:{id:"11324",title:"Chlorophylls",coverURL:"https://cdn.intechopen.com/books/images_new/11324.jpg"},signatures:"Natalya Mineeva"},{id:"82027",title:"Underutilized Grasses Production: New Evolving Perspectives",slug:"underutilized-grasses-production-new-evolving-perspectives",totalDownloads:21,totalDimensionsCites:0,doi:"10.5772/intechopen.105375",abstract:"Globally, over-reliance on major food crops (wheat, rice and maize) has led to food basket’s shrinking, while climate change, environmental pollution and deteriorating soil fertility demand the cultivation of less exhaustive but nutritious grasses. Unlike neglected grasses (grass species restricted to their centres of origin and only grown at the subsistence level), many underutilized grasses (grass species whose yield or usability potential remains unrealized) are resistant and resilient to abiotic stresses and have multiple uses including food (Coix lacryma-jobi), feed (Eragrostis amabilis and Cynodon dactylon), esthetic value (Miscanthus sinensis and Imperata cylindrica), renewable energy production (Spartina pectinata and Andropogon gerardii Vitman) and contribution to ecosystem services (Saccharum spontaneum). Lack of agricultural market globalization, urbanization and prevalence of large commercial enterprises that favor major grasses trade, improved communication means that promoted specialization in favor of established crops, scant planting material of underutilized grasses and fewer research on their production technology and products development are the prime challenges posed to underutilized grasses promotion. Integration of agronomic research with novel plant protection measures and plant breeding and molecular genetics approaches for developing biotic and abiotic stresses tolerant cultivars along with the development of commercially attractive food products hold the future key for promoting underutilized grasses for supplanting food security and sustainably multiplying economic outcomes.",book:{id:"10895",title:"Grasses and Grassland - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/10895.jpg"},signatures:"Muhammad Aamir Iqbal, Sadaf Khalid, Raees Ahmed, Muhammad Zubair Khan, Nagina Rafique, Raina Ijaz, Saira Ishaq, Muhammad Jamil, Aqeel Ahmad, Amjad Shahzad Gondal, Muhammad Imran, Junaid Rahim and Umar Ayaz Aslam Sheikh"},{id:"81218",title:"Murburn Model of Photosynthesis: Effect of Additives like Chloride and Bicarbonate",slug:"murburn-model-of-photosynthesis-effect-of-additives-like-chloride-and-bicarbonate",totalDownloads:30,totalDimensionsCites:2,doi:"10.5772/intechopen.103132",abstract:"Oxygenic photosynthesis essentially involves photo-lysis (splitting of water to release oxygen), photo-reduction (formation of NADPH), and photo-phosphorylation (synthesis of ATP) reactions. These reactions use photoactive pigments such as chlorophylls and carotenoids. Z-scheme and Kok-Joliot cycle, the acclaimed and deterministic model of photosynthesis, are founded on the classical enzyme reaction mechanisms that depend solely on affinity-based interactions of enzymes with the substrates at defined active sites, for explaining electron/moiety transfers. In contrast, the new murburn model is built on stochastic collisions between diffusible reactive species (DRS) and other milieu components (including enzymes, substrates and ions). This novel perspective explains fast kinetics and action spectrum, and affords a spontaneously probable/evolvable biochemical system. The murburn perspective proposes that the photo-excitation of pigments in the chloroplast leads to effective charge separation and DRS-formation. DRS are stabilized/utilized by a pool of redox-active components via disordered/parallel bimolecular interactions at the thylakoid membrane interface. Herein, we provide details of how murburn model is a thermodynamically, kinetically, and mechanistically viable mechanism for the formation of ATP, NADPH and oxygen. The murburn model also provides more viable explanations for several classical experimental observations in photosynthesis (Emerson enhancement effect, Jagendorf/Racker experiments, etc.) and the non-specific effects of diverse additives (such as chloride and bicarbonate).",book:{id:"11324",title:"Chlorophylls",coverURL:"https://cdn.intechopen.com/books/images_new/11324.jpg"},signatures:"Kelath Murali Manoj, Nikolai Bazhin, Yanyou Wu and Afsal Manekkathodi"},{id:"81388",title:"Electronic Structure of Chlorophyll Monomers and Oligomers",slug:"electronic-structure-of-chlorophyll-monomers-and-oligomers",totalDownloads:40,totalDimensionsCites:0,doi:"10.5772/intechopen.104089",abstract:"This chapter deals with the electronic structure of chlorophyll molecules and their complexes. Different theoretical and quantum chemical calculation methods are used to study the molecular and electronic structure of chlorophylls. Studied spectral region covers ultraviolet and infrared spectral regions, containing blue side of the Soret band, as also traditional Qy band region. Thus, there are not only focusing on the traditional Qy, Qx, and Soret transitions of chlorophylls but also high-energy transitions (in this region also proteins and nuclei acids absorb light). The aim is to show the effect of molecular conformation on the electronic states and thus on the absorption and emission spectra of monomers and oligomers. In chlorophyll-protein complexes, such conformation effect finetuning the spectral transitions and increases overlap between donor and acceptor states of energy transfer processes. Also, the role of vibronic transition in the shape of absorption and emission spectra of the studied systems will be considered.",book:{id:"11324",title:"Chlorophylls",coverURL:"https://cdn.intechopen.com/books/images_new/11324.jpg"},signatures:"Juha Matti Linnanto"},{id:"81038",title:"Earth’s Energy Budget Impact on Grassland Diseases",slug:"earth-s-energy-budget-impact-on-grassland-diseases",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.99971",abstract:"The change in climate have caused different biotic and abiotic factors to be more prominent when management plan is executed. The increase in temperature have then cause frequent drought that may attract alien species of vectors to spread novel diseases among the native plants. However, the change in climate varies in different countries. Thus, common diseases that threatens food security such as Xanthomonas spp., Pseudomonas spp are in limelight of research. Vectors lifecycle may cause plant diseases to by cyclative. Therefore, to find the break in the vector’s lifecycle will be a method to eradicate harmful population in grassland. Modern days will then call for innovative method and limitations should be considered. Climate change have also impacted pathogens migration and mating pattern. The need for innovative management is constantly on the rise.",book:{id:"10895",title:"Grasses and Grassland - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/10895.jpg"},signatures:"Ang Jia Wei Germaine"},{id:"81107",title:"Can Genus Trichoderma Manage Plant Diseases under Organic Agriculture?",slug:"can-genus-trichoderma-manage-plant-diseases-under-organic-agriculture",totalDownloads:95,totalDimensionsCites:0,doi:"10.5772/intechopen.103762",abstract:"Organic agriculture has been coming up as one of the promising segments of crop production systems in India. There are numerous reasons for it, however; human health, sustainable environment, soil health, etc. are the important ones. As per the latest information, India has about 1.5% of total cultivable land under organic agriculture. The occurrence of plant diseases in this crop production system is one of the limiting factors. For the management of plant diseases in organically grown crops, there are limited resources since there is a restriction on the use of synthetic fungicides. Under such a situation, bio-pesticides have the potency to take care of plant diseases. Although there are certain fungal and bacterial candidates well efficient in controlling diseases, genus Trichoderma has occupied a prestigious position among them. It is capable of managing seed and soil-borne plant diseases. Presently it is available in wettable powder (WP) and liquid formulations in variable concentrations for the application.",book:{id:"11317",title:"Trichoderma - Technology and Uses",coverURL:"https://cdn.intechopen.com/books/images_new/11317.jpg"},signatures:"Kishor Chand Kumhar, Dalvinder Pal Singh and Anil Kumar"}],onlineFirstChaptersTotal:15},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. 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\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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\r\n\tThe environment is subject to severe anthropic effects. Among them are those associated with pollution, resource extraction and overexploitation, loss of biodiversity, soil degradation, disorderly land occupation and planning, and many others. These anthropic effects could potentially be caused by any inadequate management of the environment. However, ecosystems have a resilience that makes them react to disturbances which mitigate the negative effects. It is critical to understand how ecosystems, natural and anthropized, including urban environments, respond to actions that have a negative influence and how they are managed. It is also important to establish when the limits marked by the resilience and the breaking point are achieved and when no return is possible. The main focus for the chapters is to cover the subjects such as understanding how the environment resilience works, the mechanisms involved, and how to manage them in order to improve our interactions with the environment and promote the use of adequate management practices such as those outlined in the United Nations’ Sustainable Development Goals.
\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
\r\n\tWater is not only a crucial substance needed for biological life on Earth, but it is also a basic requirement for the existence and development of the human society. Owing to the importance of water to life on Earth, early researchers conducted numerous studies and analyses on the liquid form of water from the perspectives of chemistry, physics, earth science, and biology, and concluded that Earth is a "water polo". Water covers approximately 71% of Earth's surface. However, 97.2% of this water is seawater, 21.5% is icebergs and glaciers, and only 0.65% is freshwater that can be used directly by humans. As a result, the amount of water reserves available for human consumption is limited. The development, utilization, and protection of freshwater resources has become the focus of water science research for the continued improvement of human livelihoods and society.
\r\n
\r\n\tWater exists as solid, liquid, and gas within Earth’s atmosphere, lithosphere, and biosphere. Liquid water is used for a variety of purposes besides drinking, including power generation, ecology, landscaping, and shipping. Because water is involved in various environmental hydrological processes as well as numerous aspects of the economy and human society, the study of various phenomena in the hydrosphere, the laws governing their occurrence and development, the relationship between the hydrosphere and other spheres of Earth, and the relationship between water and social development, are all part of water science. Knowledge systems for water science are improving continuously. Water science has become a specialized field concerned with the identification of its physical, chemical, and biological properties. In addition, it reveals the laws of water distribution, movement, and circulation, and proposes methods and tools for water development, utilization, planning, management, and protection. Currently, the field of water science covers research related to topics such as hydrology, water resources and water environment. It also includes research on water related issues such as safety, engineering, economy, law, culture, information, and education.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/41.jpg",keywords:"Water, Water Resources, Freshwater, Hydrological Processes, Utilization, Protection"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. 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