A simplified classification scheme of landslides applied in Taiwan.
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Multi-Drug Resistance (MDR) is a global concern that is having a very bad impact on health care. Microbes are getting resistant to antibiotic therapies due to the constant exposure of antimicrobial drugs. In the past decade, microbial infections have raised enormously and this has led to an increased amount of resistance [1]. Multi drug resistance is the phenomenon in which pathogenic organisms are resistant to multiple chemotherapeutic agents [2]. The emergence of MDR rises the mortality and morbidity rates for which they are known as ‘Superbugs’. It is said that MDR is a very natural process among microorganisms but the increasing amount of this process is due to several reasons like the use of undefined antimicrobial agents, unhygienic sanitary conditions, poor health care facilities. The omnipresent threat of antibiotic-resistant pathogens entails having very few antimicrobial agents for other infections [2, 3].
Many different definitions for multidrug-resistant (MDR), extensively drug-resistant (XDR) and pandrug-resistant (PDR) bacteria are being used to characterize the different patterns of resistance. Was defined as acquired non-susceptibility to at least one agent in three or more antimicrobial categories, XDR was defined as non-susceptibility to at least one agent in all but two or fewer antimicrobial categories (i.e. bacterial isolates remain susceptible to only one or two categories) and PDR was defined as non-susceptibility to all agents in all antimicrobial categories. MDR is a frequently encountered phenomenon in
Classification of MDR.
Before studying the resistance of
Multi drug resistance mechanism.
Drug Efflux Pumps are one of the major ways for the MDR mechanism. ABC transporters (ATP Binding Cassette) are membrane proteins which are commonly defined as drug efflux pumps that specifically helps in the transport of the drugs in the cell. The P-glycoprotein or multi-resistant protein (MRP) damages the permeability and influences the ATP-dependent efflux of the drugs which is responsible for decreasing the intracellular concentrations [7, 8, 9].
The genetic determinants of resistance to many antimicrobial agents are believed to have evolved prior to the era of antibiotic chemotherapy. Processes such as phosphorylation, glycosylation, acetylation whose inactivation or chemical transformation is the major cause of the MDR. The schematic diagram shows the possible ways of causing antimicrobial resistance (Figure 3) [1, 4, 10, 11, 12].
Schematic diagram of antimicrobial resistance.
Methicillin-Resistant
A schematic diagram of SCCmec element. The SCC
Plasmids are capable of carrying the resistant genes and also several toxic genes. In a recent study, it has been observed that when an
There is also support for the notion that some resistance determinants in staphylococci are derived from genes present in antibiotic-producing organisms. The
Due to the high resistance against methicillin and after the failure of the drug, Vancomycin was playing a major role in treating most MRSA infections. Isolates of
In case of Penicillin, R plasmids encode the enzyme called as
Transmission of MRSA infections can take place from person to person who is contaminated with such infections. Proper hygienic condition is required to maintain infection from getting spread. Although the mode of transmission of infections mainly relies upon direct contact but contact with contaminated fomites can also transmit the infection. Several other factors of the host such as immunocompromised patients, defects in neutrophils, or destruction of the skin barriers can also give rise to the infections.
Drugs that are discussed to be used for MRSA infections are Daptomycin and Linezolid. Daptomycin is a synthetic drug that is the class of antibiotics that destroy the cell membrane ability by a calcium-dependent binding phenomenon which leads to bactericidal activity in a concentration-dependent way. So, one of the widely used antibiotics and which shows good efficacy even more than methicillin and vancomycin. Therefore, for any MRSA bacteremia, Daptomycin is considered to be very effective [38, 39, 40]. There were many topical drugs used against the MRSA strains. These anti MRSA drugs were quite effective. Mupirocin, is one of the anti MRSA topical drug which is applied on the skin for curing skin infections caused by
Similarly, Linezolid which belongs to the oxazolidinones class predominantly inhibits the protein synthesis in the 50S ribosome of the cell. Linezolid shows a good amount of efficacy against several toxin-producing strains such as toxic shock syndrome toxin, Panton-Valentine leukocidin, α-hemolysin [38]. But the resistance against Linezolid was also observed. So, the combinatorial theory was taken into account. Combinatorial theory helps to mix multiple compounds to balance the inadequate conditions of other compounds and increase efficacy of drugs. The combinatorial theory started with Vancomycin and it shows synergistic interaction with β-lactams widely. Studies cleared that the capacity of clearing the MRSA infection-causing strains was not high in amount when the patients were only subjected to Vancomycin but in combination with β-lactams the clearance efficiency was much higher in amount. Combination with Vancomycin shows a specific effect named as Sea-Saw Effect where if the susceptibility of the vancomycin is decreased which results in decrease of transcription of the
Few drugs are in development such as Dalbavancin, Oritavancin, Tigecycline. Tigecycline inhibits protein synthesis and it shows broad-spectrum antibiotic activity. These are the possible treatments upon which the work is going on to reduce the resistance against the invasive MRSA. The prospect of the medication for
According to the future perspective, there is an immense need for an alternative strategy for treating the resistance against
The major limitation or failure that rises is intrinsic mechanisms of bacterial resistance and the target-specific antibiotics or drugs have disappointed to come up with any useful product. Another unique novel approach has come forth which combines the genomic information on the drug target and undergo chemical modifications along with efficacy testing [50].
We acknowledge the support of SRM University and C4D of SRM University for the help.
There is no Conflict of Interest in working with this chapter.
Taiwan has a land area of 36000 m2. 26.68% of the land areas are covered by plain region, whereas 27.31% are hilly and 46.01% are mountainous. By official definition for the purpose of land conservation management, hilly lands refer to the area under 100m but with a slope more than 5% or the area between 100m and 1000m. Mountainous lands refer to the area with an altitude above 1000m. Therefore, 73.32% of the areas are under conservation management. The complicated landscape of Taiwan is characterized by small drainage basins, highly fractured rock, high relief, and steep stream gradients. Frequent earthquakes due to the collision of Eurasian Plate and Philippine Sea Plate in eastern Taiwan further loosen the top surface of the land. Rock formations are highly fractured and jointed. Therefore the lands are particularly sensitive to episodic events such as typhoons and earthquakes, and various types of anthropogenic disturbance.
\n\t\t\tIn addition, Taiwan is located in tropical and sub-tropical zones, often suffering from heavy rainfalls, especially in the summer seasons with typhoons. The average annual rainfall of Taiwan is 2500 mm which is about three times the world average. Landslides are easily induced by the heavy rainfall come along with typhoons. These physiographic settings make Taiwan a fragile land, especially vulnerable to rainfall-induced landslides. The consequence is the sedimentation of the reservoirs. And the turbidity of the water in reservoirs becomes a major factor impacting the sustainable operation of water supply reservoirs in Taiwan. Landslides have to be recovered and their hazards have to be mitigated. The necessity of landslide survey is obvious.
\n\t\t\tAerial photo interpretation has long been adopted for landslide inventory (Liu et al., 2001). This conventional method is based on visual perception of colour tone and geomorphometric features of landslides on the aerial photographs. Both manual interpretation and automatic recognition of satellite images are also used. Most of the recent automatic classification methods of landslides using images are based on spectral features other than topographic features. Therefore, landslides cannot be correctly recognized. A recent study is to establish an interactive approach with a software interface for assisting visual interpretation of landslides (Lau et al., 2006). Both spectral and spatial parameters are employed for the inputs of the software to assist the interpreter/operator to correctly recognize and delineate landslides. Automatic recognition of landslides solely on basis of spectral information of digital images is efficient in terms of time consumption, whereas the results usually can not meet the requirements for taking engineering measures (Parise, 2001). Nevertheless, manual interpretation is too slow to meet the requirements for emergency response. A hybrid approach is to combine the advantages of automatic processes with manual interpretation. The extraction of gemorphometric parameters from airborne LiDAR data is thus considered for integrating in the interactive interface to assist the interpreter.
\n\t\t\tAirborne LiDAR is the state-of-the-art technology for efficiently taking high density and high resolution elevation data for a wide area. This feature is also suitable for emergency response or quick assessment of landslide disasters. Hsiao et al. (2005& 2006) shows that the integration of multi-temporal airborne LiDAR and aerial photography can give detailed change information of large-scaled deep-seated landslide as demonstrated by the Jiu-fen-er earthquake landslide. For establishing an interactive interface for assisting visual interpretation of landslides, morphometric parameters derived from LiDAR are required for setting the internal defaults (Lau et al., 2006). In this interface, four primary parameters are selected, namely the greenness, the slope angle, the object height model, and surface roughness. Normalized Vegetation Index (NDVI) is taken for denoting the greenness if colour IR digital aerial photography is applied.
\n\t\t\tFor these purposes, surveys were carried out with airborne LiDAR and digital camera to obtain digital terrain models (DTM) and digital surface models (DSM) of 1m grid and colour orthophotos of 50cm grid. DTM, DSM and orthophotos are georeferenced and transformed into the local coordinate system with Taiwan Datum 1997 (TWD97). Subsequently, the geomorphometric features of the landslides are analyzed. In this study, the geomorphometric characteristics of three selected events will be examined and these will be taken as reference values for setting the defaults in the software interface.
\n\t\tRainfall-induced landslides are in majority shallow-seated in the high relief terrains of Taiwan. Techniques of stereoscopic airphoto interpretation have been adopted for landslide inventory in Taiwan since 1973 when an aerial survey team was established under Agricultural Council of the government. Though it is labour intensive, it is believed to be reliable. The core spirit of this approach is the synergy of human perception to include both 2D and 3D features of the target and its environment. Any automated attempt should take this into account. Therefore, geomorphometric features of landslides constitute important ingredients in the automation process.
\n\t\t\tFor practical applications in the physiographic environments of Taiwan, the classification scheme of landslides developed by Varnes (1978) is simplified into five major categories, namely rock falls, shallow-seated landslides, deep-seated landslides, dip-slope and wedge slides, and debris flows. Thus, types of landslides can be differentiated by their physical appearance. It is especially useful for practical applications using remotely-sensed images.
\n\t\t\t\t\n\t\t\t\t\t\t\t | Type of Materials | \n\t\t\t\t\t\t|||
Bed rock | \n\t\t\t\t\t\t\tEngineering Soils | \n\t\t\t\t\t\t|||
Debris | \n\t\t\t\t\t\t\tSoils | \n\t\t\t\t\t\t|||
Falls | \n\t\t\t\t\t\t\tRock falls | \n\t\t\t\t\t\t\tShallow-seated slide | \n\t\t\t\t\t\t||
Topples | \n\t\t\t\t\t\t||||
Slide | \n\t\t\t\t\t\t\tTranslational | \n\t\t\t\t\t\t\tDip-slope and wedge slide | \n\t\t\t\t\t\t||
Rotational | \n\t\t\t\t\t\t\tDeep-seated slide | \n\t\t\t\t\t\t|||
Flows | \n\t\t\t\t\t\t\t(not applicable) | \n\t\t\t\t\t\t\tDebris flow | \n\t\t\t\t\t\t\t(not applicable) | \n\t\t\t\t\t\t
A simplified classification scheme of landslides applied in Taiwan.
There are 270 events of natural disasters in Taiwan in 50 years from 1958 to 2007 including categories of typhoons (71.1%), flooding (15%), earthquakes (8.5%), torrential rainfalls (2.2%), wind-storms (1.5%), mountain flooding (0.7%), and landslides (0.7%) (NFA, 2008). As shown in Figure 1, the frequency of natural disasters is in a trend of increasing. In total, 89% of the events are concerning with rainfall hazards and 97% of them are directly or indirectly concerning with landslides. Rainfall-landslides become a critical issue in managing natural distasters.
\n\t\t\t\tStatistics of natural disasters in Taiwan from 1958 to 2007
Remote sensing has been an important tool for landslide inventory. The physical appearance of landslides is the basis of the recognition of the boundary and the type of a landslide. However, the displaced materials of a rainfall-induced landslide are usually washed away from steep slopes. It remains only the fresh scars of the rupture surface. The fresh landslide scars emplacing at various slope gradients and various slope locations would normally include landslide types such as rock falls, debris slides, channel bank failures, and debris flows. In this study, the landslides concerned will cover all these types except debris flows. The exception is due to the reasoning that debris flows are triggered by a different mechanism with more contributions from flowing-water instead of gravity itself. In other words, debris flows can be treated as a transformation of other shallow-seated landslides when high concentration of rainfalls and liquefaction of displaced materials take place.
\n\t\t\tAir photo interpretation (API) is a process of understanding to associate shapes and pattern and other characteristics on vertical images with real features or phenomena on the ground. Interpretation by aerial photographs has been the most efficient and realistic way for identifying landslide topography in a wide area. Currently, researches in automatic extraction of landslides using images and digital elevation data become important topics (Barlow et al., 2003, Chang & Liu, 2004, Fernandes et al., 2004, Parise, 2001, Liu et al., 2008, Mantovani et al., 1996). However, visual interpretation by well-trained personnel is still believed to be more accurate and reliable than by computers. Interpretation process needs high skill and the results largely depend on the expertise of the interpreter. Sense of perception of a specific feature such as landslide can be acquired by practices and by an interpretation key describing visual signature characteristics of the object, including size, shape, pattern, tone, association, and texture. To minimize subjective factors of individual interpreters, cross checks should be implemented for a case covering a wide study area such as a few hundreds of aerial photographs. And, map making should be performed very carefully with, not only aerial photographs, but also site investigation.
\n\t\t\t\tThe procedures of the conventional API adopted for a wide area of landslide inventory usually include steps as follows:
\n\t\t\t\tAcquisition and preparation of aerial photographs of the study area.
. Aerial photograph interpretation (identifying landslide topography) – A stereoscope is used to pick up accurate landslide topography from aerial photographs. The scale of the panchromatic aerial photographs taken by the Aerial Survey Office of Forestry is about 1:20,000. Since 1976, about 20000 aerial photographs are taken every year. Photo index can be used for choosing the particular cloud-free photographs. Landslides with more than 50m in length were identified and their scarp, moving mass, internal structure, and moving direction are drawn with coloured pencils on the paper-printed photographs. A standard legend should be established.
Tracing the identified features on the topographic map – Tracing the features of landslides onto the topographic map by comparing identical landforms both on the photographs and the map. An original map of landslides is thus created.
(Digitization and drawing the final map – The landslide features are then digitized. Subsequently, landslide scarps and lineament structures are compiled and printed with a backdrop of conventional contour map in a GIS environment. These maps were examined and revised by the researchers.
Field check and update the attribute table from field records.
Ancillary materials for interpretation.
Final presentation and backups.
The second step of the API procedures is the most critical one where stereoscope is usually used to perceive the sense of 3D features and a well-trained interpreter should be acquainted with interpretation key for the study area.
The perception of landslides from a bird-eye view of aerial photographs is also largely depending on the scale or spatial resolution of the photographs. Landslides can not be mapped properly when they are smaller than a minimum mapping unit such as 5mm on the paper prints. Before 2008, the aerial photographs taken by Aerial Survey Office had been the conventional panchromatic photographs in a scale around 1:20000. Therefore, the minimum mapping unit of the landslides will be larger than 100m in the real ground. In general, four factors affect the quality of the mapping results, namely the scale, the time lag between the landslide event and the aerial photography, the type of film used, and the overall quality of the photographs. Table 2 shows the criteria used for the recognition of landslides on aerial photographs. The general feature of a rainfall-induced landslide is characterized by the fresh landslide scars in elongated shape and located in a relatively steep slope. It takes place in any kind of geology so long as there are some weathered overburdens. Features on aerial photographs include the bright tone, the bare surface, and the features shown in Table 2. Manual interpretation uses both 2D and 3D features of the landslides for recognition. The 2D features include tone, location, and shape. The 3D features include location, direction, slope, and shadow effects. A sound consideration of the automation of landslide recognition should be able to take care of all these aspects.
\n\t\t\t\tFeature | \n\t\t\t\t\t\t\tDescription | \n\t\t\t\t\t\t\tDiscrimination rule | \n\t\t\t\t\t\t
Tone | \n\t\t\t\t\t\t\tLight, grey light | \n\t\t\t\t\t\t\tBrightness"/Threshold | \n\t\t\t\t\t\t
Location | \n\t\t\t\t\t\t\tNear ridges, cut-off slopes, road-sides | \n\t\t\t\t\t\t\tTrigger events and buffer zone of the feature | \n\t\t\t\t\t\t
Shape | \n\t\t\t\t\t\t\tSpoon-shaped, elongated-oval, dentritic, rectangular, triangular | \n\t\t\t\t\t\t\tLocation-specific and topography-specific | \n\t\t\t\t\t\t
Direction | \n\t\t\t\t\t\t\tThe drop direction of the landslide is the gravitational vector on the ground surface. | \n\t\t\t\t\t\t\tRoughly perpendicular to the streams and t opography-specific | \n\t\t\t\t\t\t
Slope | \n\t\t\t\t\t\t\tDepend on types of landslides. E . G . S hallow-seated landslides "/ 45% ; Deep-seated landslides ~40% ; Debris flows ~10-20% . | \n\t\t\t\t\t\t\tSlope "/ Threshold | \n\t\t\t\t\t\t
Shadow | \n\t\t\t\t\t\t\tDepend on whether the l andslides are in shadow -side or sunny-side | \n\t\t\t\t\t\t\tSolar azimuth in related to slope aspect | \n\t\t\t\t\t\t
The criteria for the recognition of rainfall-induced landslides.
Obviously, geomorphometry has been applied in manual interpretation. Geomorphometry, the science of quantitative land surface analysis is also known as geomorphological analysis, terrain morphometry, terrain analysis, and land surface analysis (Hengl & Reuter, 2009). The aims of geomorphometry are to extract surface parameters and objects using input digital terrain models. Pike (1988) listed a dozen groups of parameters used as terrain descriptors using manually digitized digital terrain models and he used a resulting "geometric signature or topographic signature" to categorize terrain characteristics and suggested the degree of danger from landslides. Topographic signature of life and their processes are deemed to be strongly influenced by biota (Dietrich & Perron, 2006). Guth (2001& 2003) took terrain fabric as measures of a point property of the digital terrain models and the underlying topographic surface. This study is also known as topographic fingerprints (Densmore & Hovius, 2000) for characterizing the location of a landslide on the slope. The state-of-the-art technology of high resolution satellite images, digital aerial photography, and airborne LiDAR opens a new era in the automation of landslide recognition, especially the possibility of applying geomorphometrics. And, the extraction of land surface parameters becomes more and more attractive for both stochastic and process-based modelling, making use all the level of detailed digital terrain models.
\n\t\t\t\tIt is shown that the topographic-based analyses can be used to objectively delineate landslide features, generate mechanical inferences about landslide behaviour and evaluate relatively the recent activity of slides (McKean & Roering, 2004, Glen et al., 2006). Especially, surface roughness derived from LiDAR DTM allows an objective measurement of landslide topography. Eigenvalues of surface normals can be an effective parameter for differentiating shallow landslides and debris flows (Woodcock, 1977).
\n\t\t\t\tFor establishing an interactive interpretation software interface to assist the interpreter, it is clear that expert knowledge of the morphometric properties of landslides is required. And, data acquisition with the new sensors of aerial digital camera and LiDAR becomes feasible. Therefore, the general properties of slope angles, OHM and roughness of rainfall-induced landslides are included in this study.
\n\t\t\t\n\t\t\t\tFigure 2 shows some typical rainfall-induced landslides in Taiwan. Landslides are bare in high relief terrains with densely-vegetated surroundings. Typical modernized aerial survey system nowadays is equipped with a digital camera and a LiDAR sensor. The procedures of landslide inventory are subjected to change to adopt the new types of high resolution digital data. Thus, an interactive system for manual interpretation under a digital environment is required. Standard products generated by the new survey system include orthophoto, DTM and DSM. In addition to the functions for data management and manipulation in the interactive system, algorithms for automatic recognition of landslides are also required to assist or guide the interpreter for improving the efficiency.
\n\t\t\tTypical rainfall-induced landslides in Taiwan,
On basis of the experiences in airphoto interpretation and national landslide inventory, a man-machine interface is developed using windows software development tools including Visual Studio.NET, Borland C++ Builder, and OpenGL. Figure 3 is the flowchart of the interactive system which includes three data entries and four parameters. The entries and parameters will be modifies when more standard products are available. Parameters of roughness, OHM and Slope are derived from LiDAR data. Parameter 4 the greenness is derived from color orthophoto. These four parameters are used for highlighting potential areas of landslides by default settings of threshold for the parameters. Another option is to manually define training areas to obtain the threshold from the training sample.
\n\t\t\t\tThe visualization on the screen shows both 2D and 3D perspectives of the results (Figure 4). Final setting of parameter thresholds can be optimized visually. And finally, the interpreter can further edit the results of automated detection. Or, the interpreter can even carry out all the interpretation discarding the automated results. Finally, ground truth can be imported to compare with the results for accuracy assessment.
\n\t\t\t\tFor practical reasons, only four major parameters which can be easily derived from the standard aerial products available by a national agency are used for the automatic back-processing in the interactive system (Figure 3). Simple thresholds are used to highlight the potential landslides. For example, roughness < 5m, OHM < 10m, slope > 40 degrees, and greenness < -0.40. Default settings of thresholds are set on basis of geomorphometric analysis of rainfall-induced landslides for the specific area in related to physiographic conditions and the triggering event. Another option is to obtain the thresholds from the training sample. In this system, a landslide seed is located on the screen by the interpreter. The values of 25 pixels extracted from a 5x5 window centred at the assigned seed are used to calculate statistical means and standard deviations. Three times of the standard deviations are taken as the thresholds. Any pixel with a value within three standard deviations of the means will be assigned as a pixel of landslide. Thus, the omission and commission errors of landslide recognition can be minimized. In addition, the thresholds can be tuned interactively to see the correctness of matching between the landslide feature on the colour orthophoto and the highlighted area (Figure 4).
\n\t\t\tBecause rainfall-induced landslides of natural slopes are mostly covered by densely-vegetated surroundings, vegetation index will be critical for indicating the areas of bareness. The most popular one is the NDVI (Normalized Vegetation Index).
\n\t\t\t\twhere R stands for the grey value of Red band, and NIR stands for grey value of Near Infrared band. Theoretically, if the image digital values are calibrated to stand for the reflectance of the target, the NDVI can be widely applicable. However, the digital numbers of Red band and NIR band of digital aerial camera are not calibrated for this purpose. Therefore, the NDVI value is a relative indicator of biomass. NDVI can be applied for recent digital aerial cameras which usually includes an NIR band. If the colour aerial photographs include only RGB bands, an alternative of greenness parameter can be used. Greenness is also a relative indicator, of which the radiometric values are not normalized.
\n\t\t\t\twhere G is the grey value of Green band, and R is the grey value of Red band. The range of the values of NDVI and Greenness is between -1 and 1. Nevertheless, the range for those of landslides may change with natural weather, terrain conditions and type and settings of the camera sensor. A relative low value implies that the area of the pixel is low-vegetated or bare.
\n\t\t\t\tFlowchart of the interactive system.
Screen shots of the interactive system. (Left) Parameter settings; (Right) Accuracy assessment by comparing classified result with ground truth.
Three parameters are derived from airborne LiDAR DTM and DSM, namely the slope, the object height model (OHM) and the surface roughness. The factors in the mechanism of slope stability usually include slope angle, strength of materials, and pore water pressure (Turner & Schuster, 1996). If the slope gradient is high, the slope can be unstable. Slope is thus selected as the first parameter due to its importance and that it can be easily derived from DTM. There are two surfaces which can be easily defined by LiDAR-derived data. One is the digital terrain model (DTM) standing for the bare ground surface. The other is the digital surface model (DSM) standing for the upper envelope of all the objects above the bare ground surface. For an area of rainfall-induced landslide, the difference between these two well-defined surfaces can be minimal. Therefore, the OHM defined as the difference of these two surfaces can be a good parameter for automatic landslide recognition. It is straightforward that, due to the wash out or sliding, the surface of landslides in nature should be smoother than their surroundings. Surface roughness has been proved to be an objective and useful measurement of landslide topography (McKean & Roering, 2004, Woodcock, 1977, Glen et al., 2006).
\n\t\t\t\t(a) Slope
\n\t\t\t\tSlope angle of a landslide is the angle between the horizontal and the ground surface of the longitudinal axis of the landslide. Slope angle for each of the landslides can be determined by the slope angles derived from LiDAR DTM. If the surface of the ground is
\n\t\t\t\tthe slope (in radian) can be defined as.
\n\t\t\t\tIn common practice, the DTM is stored in grid form. The slope of a grid element such as Z5 in Figure 5 is computed by using a 3x3 moving window.
\n\t\t\t\tSlope calculation by a kernel of 3x3 moving window.
If the fluctuation of local height becomes too large due to the nature of LiDAR data or due to the nature of local relief, the resulted slope angles will be subjected to heavy noises with discontinuities of slope angles. It is therefore necessary to introduce an image processing method to resolve the problem. The first order of differentiation is applied for convolution operation with DTM. In the principle of image processing, a 2D (x, y) convolution is equivalent to two passes of 1D convolution of both (x) and (y). This simplification can be implemented more efficiently (Sharpnack & Akin, 1969, Parker, 1997). For example, formula (5) is a 1D Gaussian function and formula (6) is the first order of its derivative. Therefore, the slope formula in (4) can be implemented by convolution operations in both x and y directions with DTM grid.
\n\t\t\t\t(b) OHM
\n\t\t\t\tOHM is obtained by simply subtracting DTM from DSM for describing the height of objects above the bare ground. DTM is also referred to nDSM, i.e. normalized DSM, denoting the significance of the surface is tightly related to DSM. DTM is the bare ground surface excluding all objects above the ground. In forestry land, the difference between DSM and DTM can be referred to CHM (Canopy Height Model), denoting the general heights of the trees. The surface objects especially in forests are generally depleted in areas of landslides. Therefore, a minimal value of OHM can be expected in landslide areas.
\n\t\t\t\t(c) Surface Roughness
\n\t\t\t\tSurface roughness can be described by either the variance of DSM or OHM in a local window area. In this study, roughness is defined as one standard deviation in a 5x5 moving window on OHM for describing the relief variation in the local area. This can partly diminish the effects of landscape undulation. A 5x5 window is used for extraction the variance of the OHM values in the moving window and then the value of one standard deviation is used to stand for the surface roughness of the central pixel. In the areas of rainfall-induced landslides, the roughness will be lower than other areas due to the depletion of surface materials.
\n\t\t\tBecause slope angle, OHM, and roughness are generated from DTM, they are subject to the change of DTM grid-size. This poses a requirement to understand the possible scale effect due to the change of DTM grid-size for landslide areas (Claessens et al., 2005). A contraction of 1m grid is carried out to obtain grids of 5m, 10m, and 40m for comparison. A pixel on the grid will cover a larger area when the scale is smaller. There are two approaches for the contraction, namely pixel thinning and pixel aggregation. With pixel thinning, every nth pixel is kept. With pixel aggregation, the new pixels represent averages of the n pixels specified by the contracting factor. In Taiwan, DTMs of 5m, 10m and 40m grids are created on bases of photogrammetry. Therefore, pixel aggregation approach is used in this study for its comparability to image matching.
\n\t\t\tThe landslides induced by rainfall events in Shimen, Alishan and Ilan of northern, middle and eastern Taiwan are selected for this study. Figure 6 is the location map of the three study areas and the landslides of these areas due to the relevant events. Surveys were carried out with both sensors of airborne LiDAR and digital camera to DTM and DSM of 1m grid and orthophotos of 50cm grid. DTM, DSM and orthophotos are georeferenced, co-registered and transformed to the local coordinate system with Taiwan Datum 1997 (TWD97) for the analyses of the induced landslides.
\n\t\t\tThe location map of the study areas and the landslides of these areas.
Aerial surveys were conducted after rainfall events as shown in Table 3. Although the maximum rainfall in the period of Typhoon Longwang in Shimen was as small as 208 mm, this event was the one followed three larger events in three months of the same year, i.e. Haitang (504mm) on July 16, Matsa (818mm) on August 3, and Talim (384mm) on September 1. The event in Alishan was just a concentrated torrential rainfall. On 9th June 2006, the cumulative rainfall had reached 811mm in 24 hours and 1200 mm in 48 hours. Enormous amount of debris flows and slides took places. LiDAR data and aerial photographs were taken right after the event on 22nd June of 2006. There had been no records of heavy rainfall events one year prior to this event. The landslides observed with these datasets can be solely attributed to this rainfall event. Typhoon Kalmaegi on July 17 took place nine month after Typhoon Krosa on October 4 of 2007 in Ilan area. The rainfall took place after a dry and hot summer season. The occurrences of the three selected study areas are different.
\n\t\t\tName and size of s tudy area | \n\t\t\t\t\t\tDate of data acquisition | \n\t\t\t\t\t\tRainfall event | \n\t\t\t\t\t\tDate of the event | \n\t\t\t\t\t\tMaximum rainfall (mm) | \n\t\t\t\t\t
Shimen (48 sq. km) | \n\t\t\t\t\t\tJun. 17, 2006 | \n\t\t\t\t\t\tTyphoon Longwang | \n\t\t\t\t\t\tSept. 30, 2005 | \n\t\t\t\t\t\t208 | \n\t\t\t\t\t
Alishan (36 sq. km) | \n\t\t\t\t\t\tJun. 22, 2006 | \n\t\t\t\t\t\tTorrential rainfall | \n\t\t\t\t\t\tJun. 9, 2006 | \n\t\t\t\t\t\t1200 | \n\t\t\t\t\t
Ilan (4 sq. km) | \n\t\t\t\t\t\tNov. 4, 2008 | \n\t\t\t\t\t\tTyphoon Kalmaegi | \n\t\t\t\t\t\tJul. 17, 2008 | \n\t\t\t\t\t\t1100 | \n\t\t\t\t\t
Rainfall events related to the study areas
The orthophotos were then generated by the aerial photographs taken by direct-georeferencing technique and ortho-rectified by LiDAR DSM without using ground control points. Photography and laser scanning are synchronized. Because airborne LiDAR is equipped with GPS and IMU, an event mark is given when photography system triggers a transistor-transistor logic pulse. Thus, the instantaneous GPS and IMU information can be used to resolve the exterior orientation of the photo frame, i.e. x, y, z, ω, ψ, κ. Subsequently, the true-ortho ground surface model, i.e. LiDAR DSM, is used for the ortho-rectification of the central projected photograph.
\n\t\t\tLeica ALS50 airborne LiDAR system used in this study is consisted of 2 major parts, i.e. a laser scanning assembly and a Position and Orientation System (POS). The former one is for triggering laser pulses, controlling the range, the swath, the FOV, the scan rate and the pulse rate. These parameters decide how fast we can make a complete coverage of the survey area. The second part is critical to the positioning accuracy.
\n\t\t\tPoint density is an important indicator for the spatial resolution of LiDAR DTM and DSM. An understanding of the forest closure and crown density can be obtained by preliminary inspection of the point-density distribution of point clouds (Means et al., 2000; Naesset, 2002). In Alishan study area, the point density in average is around 2.3 points/m2 with ground point density of 0.6 points/m2. The upper envelope of the point clouds is interpolated to form DSM of 1m grid, whereas the point clouds that hit the bare ground or that are filtered to eliminate off-ground points are interpolated to form DTM. In other words, DTM denotes the bare ground surface. The accuracy of the DTM and DSM can be varied due to the change of land-cover types and density of vegetation. For assuring the accuracy, ground survey with total stations was carried out for 347 selected sample points. The RMSE is 0.82m, and mean error is 0.73m (Table 4). The error actually is a bias verified in the field check because this is due to the dense low bushes underneath the tree-canopies. This over-estimation of DTM is noteworthy especially for tropical and sub-tropical forest. In general, the accuracy of bare grounds is about 0.15m. Similarly, Shimen and Ilan areas were flown with looser point density of 1.5 points/m2 with ground point density of 0.45 points/m2.
\n\t\t\t\n\t\t\t\tFigure 7 is an example of a blown up of 1 square km of the Alishan study area. It is clearly shown that the landslide area can be enhanced on the OHM image where the landslide areas are with low OHM values.
\n\t\t\tLocations | \n\t\t\t\t\t\tSample size | \n\t\t\t\t\t\tAverage error (m) | \n\t\t\t\t\t\tRMSE (m) | \n\t\t\t\t\t\tStandard Error (m) | \n\t\t\t\t\t
Tree base | \n\t\t\t\t\t\t219 | \n\t\t\t\t\t\t0.70 | \n\t\t\t\t\t\t0.77 | \n\t\t\t\t\t\t0.33 | \n\t\t\t\t\t
Open Ground | \n\t\t\t\t\t\t128 | \n\t\t\t\t\t\t0.79 | \n\t\t\t\t\t\t0.90 | \n\t\t\t\t\t\t0.43 | \n\t\t\t\t\t
Total | \n\t\t\t\t\t\t347 | \n\t\t\t\t\t\t0. 73 | \n\t\t\t\t\t\t0.82 | \n\t\t\t\t\t\t0.37 | \n\t\t\t\t\t
Accuracy assessment of the DTM in forest lands.
Blown-up of a 1x1 km area of Alishan study area.
Greenness can be extracted from RBG orthophoto where the landslide area exhibits lower value (Figure 8C). Local slope can be calculated using 3rd finite difference algorithm (Figure 8D). OHM is a normalized height of objects above the bare ground surface. Because terrain effect has been removed, OHM exhibits a good appearance of landslides (Figure 8E). The roughness of landslide area is obviously lower than that of the environment (Figure 8F). In other words, the smoothness of landslide area is obviously higher than that of the environment.
\n\t\t\t\tIt also can be observed that the shaded-relief image of DSM gives a better contrast between landslides and their environments than that of DTM due to the contribution of the shading effect of the trees and other above-ground objects (Figure8A and B). In addition, The DSM-shaded image in nature is a true ortho-image, possessing the advantage of no occlusion of object shading when compared with orthophoto of the same area (Figure 7D). It is costly to process an orthophoto to a true orthophoto which needs to incorporate the correction of objects along with the terrain correction. Therefore, if airborne LiDAR survey is carried out alone without an integrated digital camera, the DSM-shaded image can be a good surrogate of panchromatic photograph for manual interpretation.
\n\t\t\tLandslides of the study areas (Figure 6) are obtained by manual interpretation of colour orthophotos of 50 cm grid and DSM-shaded images of 1m grid using the criteria of expert knowledge for conventional aerial photo interpretation.
\n\t\t\t\tThe total number of the rainfall-induced landslides in the 36 km2 in Alishan of middle Taiwan is 106 with a total coverage area around 1.29 km2. The landslide occurrence rate is around 4%. Statistically, 8% of the landslides have a longitudinal length of less than 30m; 36% between 30~60m; 67% less than 100m; 86% less than 150m. If more than 5 pixels are the minimum mapping unit for visual interpretation, usually more than 36% of the landslides will not be mapped using remotely-sensed images in medium resolution. The total number of landslides in Shimen of northern Taiwan is 200 with landslide coverage of 0.76 km2 in 48 km2 of study area. The landslide occurrence rate is around 1.4%, which is only one third of the rate in Alishan although the total number of landslides is more than that in Alishan. This implies that smaller consecutive rainfall events in Shimen area trigger more small landslides than that in Alishan area. This assertion can be further supported by the evidence observed in Ilan area of eastern Taiwan. The total number of landslides in Ilan area is 12 in 2 km2 of study area with landslide coverage of 0.14 km2. The landslide occurrence rate is around 7.0%. The average area of a landslide in Ilan area is also larger than that of Shimen area, yet comparable with that in Alishan area (Table 5).
\n\t\t\t\tResultant images.
Study area | \n\t\t\t\t\t\t\tTotal area (km 2 ) | \n\t\t\t\t\t\t\tTotal landslide area (km 2 ) | \n\t\t\t\t\t\t\tTotla number of landslides | \n\t\t\t\t\t\t\tLandslide occurrence rate (%) | \n\t\t\t\t\t\t\tAverage area of a landslide (m 2 ) | \n\t\t\t\t\t\t
Alishan | \n\t\t\t\t\t\t\t36 | \n\t\t\t\t\t\t\t1.29 | \n\t\t\t\t\t\t\t106 | \n\t\t\t\t\t\t\t4.0 | \n\t\t\t\t\t\t\t122 | \n\t\t\t\t\t\t
Shihmen | \n\t\t\t\t\t\t\t48 | \n\t\t\t\t\t\t\t0.76 | \n\t\t\t\t\t\t\t200 | \n\t\t\t\t\t\t\t1.4 | \n\t\t\t\t\t\t\t38 | \n\t\t\t\t\t\t
Ilan | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t0.14 | \n\t\t\t\t\t\t\t12 | \n\t\t\t\t\t\t\t7.0 | \n\t\t\t\t\t\t\t117 | \n\t\t\t\t\t\t
Average | \n\t\t\t\t\t\t\t- | \n\t\t\t\t\t\t\t- | \n\t\t\t\t\t\t\t- | \n\t\t\t\t\t\t\t4.1 | \n\t\t\t\t\t\t\t92 | \n\t\t\t\t\t\t
Statistics of the landslide distribution of the study areas
Manually-interpreted landslides are overlaid with DTM/DSM derivatives to extract the selected geomorphometric parameters including slope angle of landslides, object height models, and surface roughness. Statistics of the landslides in Alishan area (Table 6) show that the mean slope angle of the areas covered by landslides is 40.99 degrees with one standard deviation of 14.14 degrees. In contrast, the mean slope of the whole study area is 33.97 degrees with a standard deviation of 15.71 degrees. Generally, average slope angle in landslide areas is higher than that of the whole area. Figure 9 shows that the peak of the curve of slopes of landslide areas is higher and when the slopes are more than 31 degrees the faction of landslide slopes is more than that of the general slopes. This tendency holds true for both Shimen and Ilan areas.
\n\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | Slope (deg) | \n\t\t\t\t\t\t\tOHM (m) | \n\t\t\t\t\t\t\tRoughness (m) | \n\t\t\t\t\t\t|||
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | Whole area | \n\t\t\t\t\t\t\tSlide area | \n\t\t\t\t\t\t\tWhole area | \n\t\t\t\t\t\t\tSlide area | \n\t\t\t\t\t\t\tWhole area | \n\t\t\t\t\t\t\tSlide area | \n\t\t\t\t\t\t
Alishan | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t33.97 | \n\t\t\t\t\t\t\t40.99 | \n\t\t\t\t\t\t\t14.31 | \n\t\t\t\t\t\t\t4.4 0 | \n\t\t\t\t\t\t\t3.25 | \n\t\t\t\t\t\t\t2.05 | \n\t\t\t\t\t\t
Std. Dev. | \n\t\t\t\t\t\t\t15.71 | \n\t\t\t\t\t\t\t14.14 | \n\t\t\t\t\t\t\t9.69 | \n\t\t\t\t\t\t\t6.3 0 | \n\t\t\t\t\t\t\t2.69 | \n\t\t\t\t\t\t\t2.56 | \n\t\t\t\t\t\t|
Shimen | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t35.15 | \n\t\t\t\t\t\t\t43.79 | \n\t\t\t\t\t\t\t13.23 | \n\t\t\t\t\t\t\t2.15 0 | \n\t\t\t\t\t\t\t2.37 | \n\t\t\t\t\t\t\t1.48 | \n\t\t\t\t\t\t
Std. Dev. | \n\t\t\t\t\t\t\t14.28 | \n\t\t\t\t\t\t\t12.95 | \n\t\t\t\t\t\t\t8.01 | \n\t\t\t\t\t\t\t4.7 0 | \n\t\t\t\t\t\t\t1.87 | \n\t\t\t\t\t\t\t2.11 | \n\t\t\t\t\t\t|
Ilan | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t29.00 | \n\t\t\t\t\t\t\t40.48 | \n\t\t\t\t\t\t\t10.20 | \n\t\t\t\t\t\t\t6.15 | \n\t\t\t\t\t\t\t2.55 | \n\t\t\t\t\t\t\t0.40 | \n\t\t\t\t\t\t
Std. Dev. | \n\t\t\t\t\t\t\t20.14 | \n\t\t\t\t\t\t\t13.14 | \n\t\t\t\t\t\t\t10.81 | \n\t\t\t\t\t\t\t8.32 | \n\t\t\t\t\t\t\t2.82 | \n\t\t\t\t\t\t\t1.32 | \n\t\t\t\t\t\t|
Average of the means | \n\t\t\t\t\t\t\t32.71 | \n\t\t\t\t\t\t\t41.75 | \n\t\t\t\t\t\t\t12.58 | \n\t\t\t\t\t\t\t4.23 | \n\t\t\t\t\t\t\t2.72 | \n\t\t\t\t\t\t\t1.31 | \n\t\t\t\t\t\t
Statistics of the geomorphommetric parameters of the rainfall-induced landslides.
The mean value of OHM of the landslide areas in Alishan is 4.40 m with one standard deviation of 6.3 m; whereas for the whole study area, they are 14.31 m and 9.69 m, respectively. OHM of landslide areas are obviously smaller than that of the surroundings where are vegetated with high forests (Figure 8E). Figure 9 shows that the distribution of OHM for the whole study area is in bi-modal with one additional peak between 10~31 m. The peak in the right side is a forestry peak representing the concentration of trees. The mean OHM of Shimen area is as small as 2.15m denoting a cleaning ground surface of the sliding areas, whereas the mean OHM of Ilan area is 6.15m denoting the landslide areas remain some tree residues above the ground surface.
\n\t\t\t\tStatistics of the three selected parameters of Alishan area.
The mean roughness of the landslide areas is 2.05 m whereas it is 3.25 m for the whole Alishan area. The cumulative curve of roughness shows that 83% of the landslides have a roughness less than 2m and 88% less than 3m. In general, the means of the landslide areas are less than those of the whole areas. This indicates that ground surface of landslide areas are significantly soother than their surroundings, reflecting the truth of Figure 8(F). The mean surface roughnesses for both of Shimen and Ilan areas are smaller than 2.0m which are even smaller than that of the Alishan area.
\n\t\t\t\tThe significance of these three morphometric parameters can also be perceived from the average of the means in Table 6 that the differences of the parameters of the whole test area are substantially different from that of the landslide areas.
\n\t\t\t\n\t\t\t\t\tTable 7 shows the statistics of slope angles, OHM, and roughness of landslides in DTM grids of 1 m, 5 m, 10 m, and 40 m, respectively. Two features can be observed in the table: (1) statistics of 40 m grid are obviously different from others; (2) the roughness in four different grids gives quite different values. The former one reflects the unreliability of the statistics when grid-size is comparable to the lengths of landslides (see also Figure 10). The later one shows that there is a significant relationship between surface roughness and grid-size. In other words, there is a scale effect for this parameter. The value of the parameter is changed along with the grid-size. These can be further observed from Figure 10. When the dimension of landslides is similar to or less than the dimension of DTM grid-size, the computed slope angles become unstable, maybe too big or too small. The OHM shows similar phenomena that in 40 m grid, the pixels become mixed cells, i.e. trees nearby the landslide give contribution to the OHM. Surface roughness exhibits changes in all different grid-sizes.
\n\t\t\t\tIt is noteworthy that there is no cell with a roughness of more than 22m for the curve of 40m grid. 22m is about the half of the 40m-gridsize. This shows that the distribution of roughness is scale-dependent. In short, DTM with a grid size smaller than 40m will not be suitable for analyzing the rainfall-induced landslides which are usually with an area smaller than 40x40m2 as demonstrated in this study (Table 5). Therefore, it should be carefully treated when applying DTM with different resolution for geomorphometric studies.
\n\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t | grid | \n\t\t\t\t\t\t\tgrid | \n\t\t\t\t\t\t\tgrid | \n\t\t\t\t\t\t\tgrid | \n\t\t\t\t\t\t
Slope | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t40.99 | \n\t\t\t\t\t\t\t40.69 | \n\t\t\t\t\t\t\t40.25 | \n\t\t\t\t\t\t\t37.77 | \n\t\t\t\t\t\t
Std. Dev | \n\t\t\t\t\t\t\t14.14 | \n\t\t\t\t\t\t\t13.77 | \n\t\t\t\t\t\t\t13.44 | \n\t\t\t\t\t\t\t13.20 | \n\t\t\t\t\t\t|
OHM | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t4.40 | \n\t\t\t\t\t\t\t4.86 | \n\t\t\t\t\t\t\t5.01 | \n\t\t\t\t\t\t\t6.61 | \n\t\t\t\t\t\t
Std. Dev | \n\t\t\t\t\t\t\t6.30 | \n\t\t\t\t\t\t\t5.99 | \n\t\t\t\t\t\t\t5.85 | \n\t\t\t\t\t\t\t5.90 | \n\t\t\t\t\t\t|
Roughness | \n\t\t\t\t\t\t\tMean | \n\t\t\t\t\t\t\t2.05 | \n\t\t\t\t\t\t\t7.06 | \n\t\t\t\t\t\t\t13.37 | \n\t\t\t\t\t\t\t33.96 | \n\t\t\t\t\t\t
Std. Dev | \n\t\t\t\t\t\t\t2.56 | \n\t\t\t\t\t\t\t4.66 | \n\t\t\t\t\t\t\t7.39 | \n\t\t\t\t\t\t\t7.38 | \n\t\t\t\t\t\t
Statistics of slope angles, OHM, and roughness of landslides in four grids.
Scale effects of slope, OHM and roughness derived from various grid-sizes
Conventional airphoto interpretation has long been adopted as a standard approach for reliable national mapping of landslides and it is still applied for this purpose in many places of the world including Taiwan. For establishing an interactive interpretation interface to assist the interpreter, expert knowledge of morphometric properties of landslides are required for entries to automatic detection algorithm to highlight the potential areas of landslides in the system. In this study, for understanding these properties, aerial surveys were carried out with airborne LiDAR and digital camera to obtain DTM and DSM of 1m grid and orthophotos of 50cm grid. The landslides induced after torrential rainfalls in middle, northern and eastern Taiwan are selected for this study. It is proved that the morphometric parameters of rainfall-induced landslides are useful in the automatic detection of landslides for highlighting the potential areas in the interactive system. However, they have to be defined in related to local conditions and the specific events triggering the landslides. It is also observed that scale effects are obvious for roughness but not for slope and OHM. The scale effect takes place when the DTM grid is comparable to the average size of landslides, i.e. 40m in this study.
\n\t\tThis study was sponsored by the grant of Council of Agriculture, Taiwan. Project ID is 95COA-12.1.1-S-a1.
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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. In this chapter, we review the factors that contribute to osteoporosis with an emphasis on its multifactorial nature and the therapeutic consequences.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Di Wu, Anna Cline-Smith, Elena Shashkova and Rajeev Aurora",authors:[{id:"339667",title:"Associate Prof.",name:"Rajeev",middleName:null,surname:"Aurora",slug:"rajeev-aurora",fullName:"Rajeev Aurora"},{id:"347366",title:"Mr.",name:"Di",middleName:null,surname:"Wu",slug:"di-wu",fullName:"Di Wu"},{id:"347367",title:"Ms.",name:"Anna",middleName:null,surname:"Cline-Smith",slug:"anna-cline-smith",fullName:"Anna Cline-Smith"},{id:"347579",title:"Dr.",name:"Elena",middleName:null,surname:"Shashkova",slug:"elena-shashkova",fullName:"Elena Shashkova"}]},{id:"75660",doi:"10.5772/intechopen.96487",title:"Bone Quality of the Dento-Maxillofacial Complex and Osteoporosis. 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Therefore, the objective of this chapter is to describe the use of these parameters as an auxiliary mechanism in the detection of low bone mineral density, as well as to characterize the radiographic findings of patients with osteoporosis.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Plauto Christopher Aranha Watanabe, Giovani Antonio Rodrigues, Marcelo Rodrigues Azenha, Michel Campos Ribeiro, Enéas de Almeida Souza Filho, Rafael Angelo Soares Vieira and Fabio Santos Bottacin",authors:[{id:"76171",title:"Prof.",name:"Plauto C. A.",middleName:null,surname:"Watanabe",slug:"plauto-c.-a.-watanabe",fullName:"Plauto C. A. 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Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",doi:"10.5772/intechopen.97416",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:254,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. However, for patients below this range, studies are still lacking.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"José Renan Vieira da Costa Júnior and Sérgio Luchini Batista",authors:[{id:"164388",title:"Prof.",name:"Sergio",middleName:null,surname:"Luchini Batista",slug:"sergio-luchini-batista",fullName:"Sergio Luchini Batista"},{id:"354032",title:"Dr.",name:"José Renan",middleName:null,surname:"Vieira Da Costa Júnior",slug:"jose-renan-vieira-da-costa-junior",fullName:"José Renan Vieira Da Costa Júnior"}]},{id:"76677",doi:"10.5772/intechopen.97760",title:"Introductory Chapter: Osteoporosis Overview",slug:"introductory-chapter-osteoporosis-overview",totalDownloads:176,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Luis Rodrigo",authors:[{id:"73208",title:"Prof.",name:"Luis",middleName:null,surname:"Rodrigo",slug:"luis-rodrigo",fullName:"Luis Rodrigo"}]}],mostDownloadedChaptersLast30Days:[{id:"75660",title:"Bone Quality of the Dento-Maxillofacial Complex and Osteoporosis. 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Therefore, the objective of this chapter is to describe the use of these parameters as an auxiliary mechanism in the detection of low bone mineral density, as well as to characterize the radiographic findings of patients with osteoporosis.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Plauto Christopher Aranha Watanabe, Giovani Antonio Rodrigues, Marcelo Rodrigues Azenha, Michel Campos Ribeiro, Enéas de Almeida Souza Filho, Rafael Angelo Soares Vieira and Fabio Santos Bottacin",authors:[{id:"76171",title:"Prof.",name:"Plauto C. A.",middleName:null,surname:"Watanabe",slug:"plauto-c.-a.-watanabe",fullName:"Plauto C. A. 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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. In this chapter, we review the factors that contribute to osteoporosis with an emphasis on its multifactorial nature and the therapeutic consequences.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Di Wu, Anna Cline-Smith, Elena Shashkova and Rajeev Aurora",authors:[{id:"339667",title:"Associate Prof.",name:"Rajeev",middleName:null,surname:"Aurora",slug:"rajeev-aurora",fullName:"Rajeev Aurora"},{id:"347366",title:"Mr.",name:"Di",middleName:null,surname:"Wu",slug:"di-wu",fullName:"Di Wu"},{id:"347367",title:"Ms.",name:"Anna",middleName:null,surname:"Cline-Smith",slug:"anna-cline-smith",fullName:"Anna Cline-Smith"},{id:"347579",title:"Dr.",name:"Elena",middleName:null,surname:"Shashkova",slug:"elena-shashkova",fullName:"Elena Shashkova"}]},{id:"75742",title:"Osteoporosis and Dietary Inflammatory Index",slug:"osteoporosis-and-dietary-inflammatory-index",totalDownloads:235,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Healthy bones are constantly being renewed and proper nutrition is an important factor in this process. Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:254,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. However, for patients below this range, studies are still lacking.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"José Renan Vieira da Costa Júnior and Sérgio Luchini Batista",authors:[{id:"164388",title:"Prof.",name:"Sergio",middleName:null,surname:"Luchini Batista",slug:"sergio-luchini-batista",fullName:"Sergio Luchini Batista"},{id:"354032",title:"Dr.",name:"José Renan",middleName:null,surname:"Vieira Da Costa Júnior",slug:"jose-renan-vieira-da-costa-junior",fullName:"José Renan Vieira Da Costa Júnior"}]}],onlineFirstChaptersFilter:{topicId:"1414",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,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:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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