Land cover classes considered and their description [71].
\r\n\t
",isbn:"978-1-80356-966-6",printIsbn:"978-1-80356-965-9",pdfIsbn:"978-1-80356-967-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"f86a9f720cc3ac0f1c385d0367ea89b9",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11624.jpg",keywords:"Agricultural Waste, Reuse, Reduction, Soil Health, Recycling, Agriculture and Environment, Modelling and Simulation, Agro-Industrial Waste, Bioresource Processing, Processing and Management, Crop Residue, Forest Waste",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 8th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Fiaz Ahmad is a researcher in the field of Agricultural Engineering with fifteen years of field and academic experience, currently in charge of the Agricultural Machinery Design Laboratory at Bahauddin Zakariya University. 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He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and completed his postdoctorate in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He was awarded a fellowship from the Higher Education Commission of Pakistan for Ph.D. studies and from the Chinese Government for post-doctoral studies. He earned a BSc and MSc (Hons) in Agricultural Engineering from the University of Agriculture, Faisalabad, Pakistan, in 2004 and 2007, respectively. He is the author of more than fifty journal and conference articles. He has supervised six master’s students to date, and is currently supervising six master and two doctoral students. 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He was an awardee of MEXT and JASSO fellowships (from the Japanese\r\nGovernment) during Ph.D. and Postdoc studies, respectively. He also did a Postdoc as\r\na Canadian Queen Elizabeth Advance Scholar at Simon Fraser University (Canada) in\r\nthe field of Mechatronic Systems Engineering. He worked for Kyushu University\r\nInternational Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years.\r\nCurrently, he is working on 4 research projects funded by the Higher Education\r\nCommission (HEC) of Pakistan. He has completed six projects in past in the field of\r\nagricultural engineering. He has supervised 10+ M.Eng. and Ph.D. thesis and 10+\r\nstudents are currently working under his supervision. He has published 120+ journal\r\narticles, 100+ conference articles, 13 book chapters, and 6 books. 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These changes have intense implications at the regional and global scales for global loss of biodiversity, distresses in hydrological cycles, increase in soil erosion, and sediment loads [1]. At the local level, changes in the use of land and its cover affect watershed runoff, microclimatic resources, processes of land degradation and landscape-level biodiversity, soil erosion, and sediment loads [2]. All these have direct impacts on livelihoods of local societies.
The Shire River in Malawi, southern Africa, is among the areas where land use land cover change (LUCC) has become more prevalent in recent years resulting into severe soil erosion and causing heavy siltation downstream [3, 4, 5, 6, 7, 8, 9]. The river is an important source of livelihood to many people, using the water for agriculture, domestic purposes, and the generation of electricity [6, 8, 10]. One of the most important structures across the Shire River is the Nkula B Hydroelectric Power Station situated in the middle section of the river. The dam at Nkula Falls that supplies water into the power station has, in recent times, been threatened with massive siltation, some studies attributing this to increased human population and agricultural activities [5, 6, 8]. The conceptual setting of this study originates from a strong link that exists between land use change and soil erosion [8, 11, 12, 13, 14, 15]. Land use and management practices are important factors in determining the extent of soil erosion [8, 15]. Good vegetation cover promotes infiltration of water into the ground and soil retention, while deforestation results into increased runoff than infiltration occurring during periods of more precipitation [16, 17, 18]. Increased runoff consequently leads to stronger soil erosion usually in areas with poor vegetation cover [8, 19, 20]. Erosion of soil under continuous cultivation is the most serious form of resource degradation occurring in Malawi [3, 8, 19, 21, 22, 23]. The rate of soil loss in Malawi is currently estimated at 29 t/ha/year [24], which is higher than the previously reported 20 t/ha/year [21]. In the middle Shire River, estimated soil loss between the year 2000 and 2014 ranged from 0.1 to 21.1 t/ha/year [24, 25]. According to the Malawi Government Report (2015), the middle Shire River catchment has many bright spots (areas experiencing high soil loss but declining trends over time), for example, Neno and Ntcheu in the west and Zomba and Chiradzulu in the eastern side of the river.
The question regarding land use changes over time, and its driving forces in the middle Shire River catchment nevertheless remain unresolved [4, 6]. Such knowledge is critical to the development of policies and action plans necessary for changing current LUCC trends in the area as it has been observed in other places [26, 27, 28, 29, 30]. Furthermore, problems of LUCC are global and serious in many developing countries where increasing population has resulted into excessive pressure on natural resources [8, 30].
The study was carried out to understand the impact of land use and land cover changes on the Nkula Dam in the middle Shire River catchment, Malawi. The LUCC drivers analyzed in this study include biophysical changes (e.g., climate change) and human activities (e.g., population, poverty, land policies, and GDP growth) [3, 4, 6]. Climate and socioeconomic data were compiled to analyze the drivers of LUCC in the study area. Geographic information systems (GIS) and remote sensing techniques which are gaining increased recognition globally as rapid methods of acquiring and analyzing up-to-date information over a large geographical area were used in the study [30, 31, 32, 33].
The Shire River is the largest river in Malawi, originating from Lake Malawi which supports vast agricultural and socioeconomic activities in its catchment (Figure 1) [34]. The river is divided into three sections, namely, the upper, middle, and lower Shire [34, 35]. This study focused on the catchment of the middle section of the river which includes the Shire Plain which is bounded by mountains on both sides and the Nkula Dam downstream [34, 36]. The plain is more extensive to the west of the river than it is to the east (Figure 1). The middle section of the Shire River has eight administrative districts, supporting a population of about 5 million people (Figure 1).
Map of Malawi (left) showing the middle Shire River and its catchment (right). Eight administrative districts are located in the study area.
Climate in the middle Shire River catchment area varies due to differences in altitude with annual average precipitation ranging from 750 to 2500 mm [35, 37]. Highlands receive more rain which begins in November and ends late in April [6, 37]. Annual average temperature of the area is around 23°C, with highlands in the east experiencing cooler temperatures than plains in the west [6, 35]. The rocks in the study area are mainly composed of Precambrian basement complex and igneous rocks [37]. Amphibolite and granulite facies are dominant in the western and eastern side of the Shire River, respectively, while soils in the river’s catchment are dominated by Cambisols [6, 24, 37].
The following procedures were followed in order to answer the study questions: firstly, six Landsat images for the dry seasons (to avoid cloud cover effects) of 1989, 1993, 2000, 2006, 2011, and 2015 were downloaded from the United States Geological Survey (USGS, http://glovis.usgs.gov/) at Level 1 T using different paths and rows (167/070, 167/071, 168/070, and 168/071). All images had a spatial resolution of 30 m which is large enough to visualize changes in land use [38] from Landsat 5, 7, and 8. Secondly, meteorological, topographical, and socioeconomic data from 1989 to 2015 were collected from the Malawi Department of Meteorological Services and Statistics [24, 36]. The third stage was the processing of the Landsat images and, finally, classification of land use which was followed by analysis of different land covers. Statistical analysis was done on data for the topography of the catchment area, temperature, rainfall, population, and GDP in order to determine drivers of LUCC.
Landsat images were processed using ENVI 5.1 Software to study information on the types of land use and their spatial patterns. To analyze these spatial patterns, the following steps were followed: firstly, relative radiometric correction was done on each band to eliminate errors arising from radiation caused by weather conditions; secondly, multiband combination of Landsat images was done in preparation for research spectral characteristics of various types of land use; thirdly, geometric correction of remote sensing images was done using Malawi DEM, Universal Transverse Mercator Projection, Arc 1960, and UTM Zone 36S, based on 1:50,000 topographic map scale so that it fits with the Landsat images [38, 39]. This helps to eliminate position errors of Landsat images which the terrain, position of the sun, and angle sensor may produce. A mosaic of required images was prepared and a single image generated. Atmospheric Landsat images were then corrected by ENVI 5.1 FLAASH module.
After processing the Landsat images, identification of different land use classes was done where some visual designs like texture, tone, and the effect zones were used [38]. The land in the study area was classified according to its use or description such as cultivated land, water, forest (indigenous and plantations were combined), etc. When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it’s among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38, 39, 40, 41, 42, 43, 44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41, 45]. Representative zones for each desired class were located in the image with adequate number of pixels covering the known classes to reduce the image noise [38]. Secondly, training area number and percentage were identified in order to classify several training and test areas. These results were compared with supporting ground data so that the new training statistics could be derived. Thirdly, a statistical file known as spectral signature was created by the image processing software for each class because each and every pixel can only be assigned to one spectral class. Lastly, each pixel was allocated to the most likely class based on the maximum likelihood algorithm where each pixel is assigned to the spectral class that has the greatest probability density function for the multispectral values of the pixel. Maximum likelihood algorithm is the most commonly used algorithm in which a pixel is classified into the corresponding class [38, 43, 46]. Land cover types were then classified into the following eight main classes according to Anderson et al. [47]: (1) forest, (2) shrubland, (3) grassland, (4) cultivated land, (5) bare land, (6) water bodies, (7) wetland, and (8) artificial surfaces (Table 1).
No. | Land cover class | Description |
---|---|---|
1 | Forest | Woodland open general (15–65%) with herbaceous layer. Broadleaved deciduous trees, closed >(70–60)%. Vegetative cover is in balance with the abiotic and biotic forces of its biotope |
2 | Shrubland | Closed to open (thicket) (15–100%) scattered trees |
3 | Grassland | Herbaceous closed vegetation (15–100%) with some trees, shrub Savannah, and permanent marsh |
4 | Cultivated land | Areas where the natural vegetation has been removed or modified and replaced by other types of vegetative cover of anthropogenic origin. All vegetation that is planted or cultivated with intent to harvest is included in this class |
5 | Bare land | Bare rock and/or coarse fragments. Areas that do not have an artificial cover as a result of human activities. These areas include areas with less than 4% vegetative cover |
6 | Water bodies | This class refers to areas that are naturally covered by water, such as lakes, rivers, snow, or ice |
7 | Wetlands | Areas that are transitional between pure terrestrial and aquatic systems and where the water table is usually at or near the surface or the land is covered by shallow water |
8 | Artificial surfaces | Areas that have an artificial cover as a result of human activities, such as construction (cities, towns, and transportation), extraction (open mines and quarries), or waste disposal |
Land cover classes considered and their description [71].
A total of 165 training sites (sampled portions of the scene, purposely selected, for the derivation of the training statistics) were chosen for each image to ensure that all spectral classes constituting each land use and land cover categories were adequately represented in the training statistics to classify the entire scene [48]. Classification was done using ground checkpoints, digital topographic maps, vegetation cover map, and the researchers’ knowledge of the study area [49, 50]. A total of 156 sampling points (GPS + photograph) were collected out of the 165 training sites during the dry season to avoid cloud cover effects which is more common in rainy season. Land use types at the sampling sites were evaluated according to field surveys (photographs + GPS) where photographs were taken using a camera and coordinates of the spot were taken using GPS. Accuracy of the supervised classification methods was checked by a confusion matrix of accuracy (Table 2) [38, 44, 51] to ensure that various measures, such as error-rate, accuracy, specificity, sensitivity, and precision, were checked.
Actual type | Classified type | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Forest | Shrubland | Grassland | Cultivated land | Artificial surfaces | Wetland | Water bodies | Bare land | Actual sum | Accuracy | |
Forest | 9 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 82% |
Shrubland | 0 | 14 | 1 | 1 | 0 | 0 | 0 | 0 | 16 | 88% |
Grassland | 0 | 1 | 20 | 1 | 0 | 1 | 0 | 1 | 24 | 83% |
Cultivated land | 0 | 0 | 1 | 21 | 1 | 0 | 0 | 0 | 23 | 91% |
Artificial surfaces | 1 | 1 | 1 | 2 | 34 | 0 | 0 | 0 | 39 | 87% |
Wetland | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 0 | 9 | 89% |
Water bodies | 0 | 1 | 0 | 0 | 0 | 1 | 32 | 0 | 34 | 94% |
Bare land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Classified sum | 10 | 18 | 25 | 25 | 35 | 10 | 32 | 1 | 156 |
Confusion matrix of accuracy evaluation in middle Shire River catchment in 2015.
Landsat image classified type results were compared with the field survey results to evaluate their accuracy and then calculated using confusion matrix evaluation table (Table 2).
LUCC drivers were mainly analyzed using descriptive methods due to inavailability of spatial socioeconomic data from the government database. Pearson correlation coefficients between socioeconomic data and land use types were analyzed in SPSS for Windows version 10.
The overall classification accuracy ranged from 82 to 94% (Table 2). The western side of the Shire River covers an area of approximately 3353 km2, while the eastern side is 2770 km2 comprising 55 and 45% of the total area, respectively. Regions were defined by slope of less than 10o as plain/flat area. According to Table 3, total plain/flat area covers 2417 km2 which is lesser compared to highlands (with slope ranging from 10o to 90o) covering 3706 km2. Eastern and western plain/flat areas cover 988 and 1429 km2, representing 41 and 59% of the total plain/flat area of the study area, respectively (Table 3).
Area/coverage | Plain (≤10°) | Highlands (10–90°) | ||
---|---|---|---|---|
Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | |
Western side | 1429 | 59 | 1075 | 29 |
Eastern side | 988 | 41 | 2631 | 71 |
Total catchment area | 2417 | 100 | 3706 | 100 |
Distribution of plains and highlands in eastern and western side of the middle Shire River.
The middle Shire River catchment is dominated by shrubland, grassland, cultivated land, and forestland, which accounted for 36, 28, 22, and 12% in 1989, respectively (Figure 2).
Land use and land cover changes from 1989 to 2015.
Findings (Table 4) show significant land use and land cover changes in the middle Shire River catchment over the 26-year period.
Land cover type | Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | |||||||
Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Forest | 739 | 12.07 | 679 | 11.08 | 545 | 8.90 | 479 | 7.82 | 481 | 7.86 | 662 | 10.80 |
Shrubland | 2201 | 35.95 | 1986 | 32.44 | 2264 | 36.97 | 2043 | 33.37 | 1835 | 31.85 | 2040 | 32.97 |
Grassland | 1719 | 28.07 | 1838 | 30.02 | 1451 | 23.69 | 1692 | 27.63 | 1617 | 24.53 | 1255 | 20.52 |
Cultivated land | 1367 | 22.33 | 1538 | 25.12 | 1745 | 28.50 | 1814 | 29.64 | 2067 | 33.76 | 2073 | 34.09 |
Artificial surfaces | 26 | 0.43 | 28 | 0.45 | 33 | 0.54 | 37 | 0.60 | 39 | 0.64 | 43 | 0.71 |
Wetland | 35 | 0.57 | 23 | 0.38 | 56 | 0.91 | 19 | 0.31 | 38 | 0.63 | 20 | 0.34 |
Water bodies | 31 | 0.51 | 30 | 0.49 | 20 | 0.33 | 30 | 0.49 | 36 | 0.58 | 22 | 0.44 |
Bare land | 4 | 0.06 | 2 | 0.03 | 9 | 0.15 | 9 | 0.15 | 9 | 0.15 | 8 | 0.13 |
Area (km2) and percentages of different land cover types from the year 1989 to 2015.
Artificial and cultivated land increased by 65 and 52%, respectively, in the 26-year period, while forest cover, grass, and shrubland decreased by 35, 27, and 7%, respectively. Other land classes such as wetlands and water bodies show fluctuations (Figure 2 and Table 4). Spatially, in 1989, total cultivated land in the western side was 694 km2 which increased to 1226 km2 by the year 2015, representing 21 and 37% of the total land in the western side, respectively (Table 5).
Location/district | Year | ||||||
---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | ||
Western side | Balaka | 335 | 556 | 627 | 655 | 688 | 853 |
Mangochi | 59 | 51 | 41 | 80 | 47 | 91 | |
Neno | 25 | 41 | 49 | 38 | 28 | 53 | |
Ntcheu | 275 | 298 | 219 | 226 | 219 | 228 | |
Total area | 694 | 946 | 935 | 999 | 982 | 1226 | |
Eastern side | Blantyre | 359 | 264 | 362 | 381 | 244 | 278 |
Chiradzulu | 33 | 9 | 19 | 17 | 18 | 23 | |
Machinga | 184 | 247 | 264 | 263 | 135 | 368 | |
Zomba | 96 | 71 | 165 | 155 | 122 | 194 | |
Total area | 673 | 591 | 810 | 816 | 520 | 862 |
Changes in cultivated land area (km2) in districts of the middle Shire River catchment.
This suggests an increase of 16% of cultivated land in the western side between 1989 and 2015. In the eastern side, cultivated land increased from 673 to 862 km2 within the same period, representing 24 and 31%, respectively, of the total land area indicating a 7% change. In 1989, the western side of the Shire River catchment mainly consisted of shrubland, grassland, and forestland which accounted for 35, 33, and 10%, respectively. In the eastern side, shrubland, grassland, and forestland accounted for 37, 22, and 15%, respectively. The western side (Balaka, Neno, and Ntcheu) and eastern side (Zomba) are the main districts where forest, shrubland, and grassland decreased the most. For example, in Balaka District, forest area reduced from 11% in 1989 to 2% in 2011 before increasing to 3% in 2015, while shrubland decreased from 38% in 1989 to 18% in 2011 and then increased to 23% in 2015. Forestland in Neno District decreased from 10% in 1989 to 1% in 2011 and then increased up to 5% in 2015, while shrubland decreased from 35% in 1989 to 19% in 2015 and grassland from 27% in 1989 to 17% in 2015 with some fluctuations in between the years. In Ntcheu District, grassland decreased from 35% in 1989 to 15% in 2015. Forest cover in Zomba district declined from 19% in 1989 to 7% in 2006 and then started to increase from 2011 reaching 12% in 2015. Shrubland decreased from 41% in 1989 to 27% in 2015 in the same district.
Results indicate some fluctuations in the amount of rainfall received in the area within the 26-year period that might be due to climate change as a result of land use and land cover changes due to human activities (Figure 3).
Annual rainfall and temperature for the middle Shire River catchment from 1989 to 2015. Circles represent flood years, while rectangles represent drought years (Source: Malawi Meteorological Department).
Rainfall in the catchment area declined continuously from 1989 to 1993, culminating into the drought of 1992 and 1993 (Figure 3) [52, 53]. Malawi is regularly affected by drought and floods [53]. The country (including the study area) was affected by heavy floods in 1989, 1998, 2000, 2001, and 2015, destroying crops and displacing many people (Figure 3) [53]. Earlier studies indicate that rainy season in Malawi is dominated by tropical and extratropical influences with links to the El Niño-Southern Oscillation (ENSO) [54, 55]. Actually, this is reported for the whole of Southern Africa [56].
The population of Malawi which includes districts under study on the western (Mangochi, Balaka, Ntcheu, and Neno) and eastern sides of the middle Shire River (Blantyre, Zomba, Machinga, and Chiradzulu) has been increasing steadily since the 1980s (Figure 4).
Population of districts in the middle Shire River catchment area and GDP (US$) for Malawi from 1989 to 2015 [
Increased population is more pronounced in urban areas. For example, in 2015, Blantyre and Zomba cities had 3006 and 2240 people per km2, respectively [34, 53, 57]. There has been a general increase in the GDP over the past 26 years especially between 2006 and 2011 and falling between 1993 and 2003 (Figure 4).
Rainfall affects LUCC in the middle Shire River catchment. Drought and floods in the western side of the river, therefore, have resulted into low crop yield. As a survival mechanism, people resort to cutting down of trees to earn income, causing forest degradation [58, 59]. This may, therefore, explain the concurrent low rainfall received against a sharp decline in forest areas between 2006 and 2011 (Figures 2 and 3). Results in this study agree with an earlier report for the upper Shire River catchment [60] indicating a direct link between poor rainfall (drought/floods) and cutting down of trees.
Rapid population growth is one of the drivers of LUCC in the western side of the middle Shire River earlier reported by [60, 61]. Population increase in the western part of the middle Shire River is mainly attributed to the influx of refugees fleeing the civil war from Mozambique from the 1990s. Population growth leads to urbanization, increase in cultivated land, and residential area [3, 8]. The high population density in Malawi with an estimated growth rate of 2.8% is putting increasing pressure on its natural resources, leading to expansion of farming on marginal lands and forests as well as encroachment into protected forest reserves/parks. Results in this study show a transition of land use from forest, shrubland, and grassland to cultivated land and buildup areas (Tables 4 and 5). These changes mainly occurred between 1989 and 2011 (Figure 2 and Table 4) probably due to increasing anthropogenic pressure on natural forests. Results also show a drastic change in forest/grassland/shrubland between 1989 and 2011 in three out of the four districts (Balaka, Neno, and Ntcheu) in the western side of the middle River Shire. Large proportion of shrubland, grassland, and forestland (84%) in the western part of the river were converted to cultivated land, buildup areas, and/or bare land. This confirms earlier assertion that increasing population results into a decrease in forest area (Figure 5).
Changes in forest, cultivated land in the catchment area, and siltation volume in the Nkula Dam from 1989 to 2015.
The rate of forest decline experienced by Malawi [61] and the Shire River catchment in particular [59], due to heavy dependency on wood for energy, is alarming. Most people around the middle Shire River catchment rely on firewood and charcoal for their daily living [58, 62, 63]. Malawi’s forest cover loss is estimated at 2.6% per annum [64]. The middle Shire River catchment lost, on average, about 4.3% of its forest and shrubland annually between 1989 and 2011 (Table 4), suggesting a negative relationship between population increase and the decline in forest coverage (Figures 4 and 5). Results, nevertheless, showed a recovery in forest cover from 2011 to 2015 (Tables 4 and 5), likely attributed to interventions by the government of Malawi and nongovernmental organizations in strengthening natural resource management policies that started around 2008 up to date [5, 65].
Macroeconomic activities such as increase in manufacturing industries and other businesses which contribute to the growth of GDP often require large areas, which also contributed to the transition of forest/shrubland/grassland into buildup areas. Some of such economic activities include opening of new farms which also require clearing of forest areas (Figures 4 and 5).
National policies in the past have failed to effectively enforce ban of unabated harvesting of forest resources until recently with the introduction of community-based natural resource management groups and intervention of some nongovernmental organizations in afforestation programs. This may explain the increase in forest cover from 2011 to 2015 as earlier indicated (Figure 2 and Table 4). Globally, large expanses of forests are being converted into bare land for domestic purposes and, principally, due to harvesting of timber [66]. In a study carried out between 1989 and 2002 in the upper section of the Shire River, [60] reported impacts of LUCC on the river’s catchment hydrological regime which includes increase in soil erosion. It is reported that agricultural land increased by 18% between 1989 and 2002 [60]. In another LUCC assessment study for Likangala River catchment (a stream from Zomba Mountain which is also a source of several rivers draining into the eastern side of the middle Shire River), woodlands decreased from 135.3 km2 in 1984 to 15.5 km2 in 2013 [67]. These results agree with the present study confirming negative impacts of LUCC. Agriculture is the main source of employment to about 92% of the population in Malawi which lives in rural areas [61, 68]. Increase in agricultural activities leads to cultivated land expansion. Cash crops (e.g., tea, coffee, tobacco, and cotton), subsistence crops (e.g., maize and groundnuts), and animal rearing contribute to the increase in agricultural GDP. Results in the present study agree with a report for the region in which land use change (increase in farming activities) contributed to increase in GDP. Similar findings have also been reported correlating land use to increase in income [67]. The increase in cultivated land and artificial surfaces resulted into a decline in forest and shrubland (Tables 4 and 5).
Furthermore, the country loses about 1.7% of its GDP on average annually due to the combined effects of droughts and floods [69]. Heavy rains received during the 1989 season in the country (Figure 3) were associated with devastating floods that drastically affected the GDP due to crop failure and loss of property as well as human life in the same period but increased in the subsequent year (Figure 4). Although the devastating rainfall in the 1989 season played a role in influencing the GDP, other factors could also be at play due to the fact that drivers of economic growth are diverse and vary in the magnitude of influence. For example, in 1989, Malawi’s economy was associated with high fuel prices due to the war in Mozambique. All fuel transportation routes from the Indian Ocean ports in Mozambique were blocked, and consequently, there was a collapse in commodity prices [68]. Poor sales of tobacco which is the country’s major foreign exchange earner also affected the GDP in 1989 [68]. Increased GDP between 2005 and 2009 has been attributed to stabilization and enhanced income growth, which increased income per capita due to the new economic policies and a stable political environment in 2004 [68].
These study findings show a decline in forests and then an increase over the past 26 years (Figures 2 and 5 and Table 4). Clearing of forests from the catchment of the middle Shire River has subjected the bare soil to erosion which finds its way into the Shire River downstream to the Nkula Dam as a sink. This, thus, may explain the heavy siltation at the Dam which has reduced the volume of water causing problems with normal generation of electricity (Figures 4 and 5). The volume of the Dam at Nkula Falls, which was 3 million m3 at its construction in the 1980s, has recently dropped to nearly half of its original size due to massive siltation which consequently resulted in low production of hydroelectricity, now failing to meet the country’s demand for power. Nkula B Hydroelectric Power Station is the main electricity generation plant in Malawi producing about 124 MW of electricity [70]. The electricity-providing company—the Electricity Supply Commission of Malawi (ESCOM)—is now implementing involuntary power load shedding programs resulting into national frequent blackouts. Consumers now resort to excessive use of firewood/charcoal in place of electricity for cooking and other domestic chores creating a heavy dependency on forest resources.
High soil losses in Ntcheu and Neno Districts could be due to increased population as a result of the refugees’ long time settlement in these areas resulting into removal of forests. The expansion of cultivated land could thus be the cause for increased soil erosion and sediment transport downstream, which consequently accumulate in the Nkula Dam in the middle Shire River (Figure 5). These findings agree with a recent study [6] which confirmed that most of the sediments going into the Shire River and finally depositing at the Nkula Dam originate from the western side of the Shire River. Several studies elsewhere [20, 66] also report the same, linking increased population to deforestation and soil. Loss of forests coupled with agriculture are cause for rapid land use change resulting into increased soil erosion and siltation in the middle Shire River catchment [4, 6, 8] (Figure 5). Malawi, and the middle Shire River in particular, is therefore locked up in a cycle where anthropogenic activities in the river’s catchment meant for a survival alternative to lack of electricity have become a cause for soil erosion and siltation in the river, consequently hampering the generation of the needed electricity.
Findings in this study show significant land use and land cover changes that have occurred in the middle Shire River catchment over the past 26 years which have also affected the Nkula Dam. Forestland and shrubland have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the western side of the middle Shire River. Severe siltation downstream in the Nkula Dam appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and GDP, macroeconomic activities occurring especially in the western part of the river such as manufacturing industries, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources.
To solve these problems, there is a need to review and amend weak policies that encourage noncompliance to regulations of managing forests. For example, all policies that may encourage or result in soil erosion such as river bank cultivation must be amended. Powers should be invested in local authorities to take part in protecting the environment and/or in planting trees, and the government should be able to provide seedlings for the operation. This should be done in a competition manner that the village which will perform well should be given some incentives. There is also need to increase fertilizer use so that land expansion for farming is curbed and yields are improved. In addition to that, population growth can be controlled through increase use of family planning. Encouraging children to go to school to avoid early marriages might also help to reduce poverty which will help to avoid cutting down of trees careless. Deliberate programs should be instituted by the government to curb further effects of climate variability such as droughts and floods. Such programs may include good agricultural practices that conserve soil and protect it from water erosion, discourage river bank cultivation, intensify afforestation programs, and ban the burning of charcoal. Findings in this study and the combination of methods used (application of GIS, remote sensing, and analysis of socioeconomic factors) can possibly be applied in areas where similar environmental problems have occurred. It is preferable to include a conclusion(s) section which will summarize the content of the book chapter.
We thank the State Key Laboratory of Estuarine and Coastal Research and Graduate School of East China Normal University (ECNU) for supporting this study. We also appreciate the valuable comments provided by Professor Christo C.P. Van der Westhuizen of North West University (South Africa), Professor Fang Shen of East China Normal University (China), Dr. Mavuto Tembo of Mzuzu University (Malawi), Ms. Lostina S. Chapola of Catholic University (Malawi), Mr. Tanazio Kwenda from the Department of Surveys (Malawi), Mr. Patrick Jambo from Forestry Department of Mzuzu University (Malawi), Mr. Samuel Limbu of the University of Dar es Salaam (Tanzania), Dr. Naziha Mokadem of North West University (South Africa), and the anonymous reviewers who helped us to polish this manuscript.
No potential conflict of interest was reported by the authors.
Object tracking is defined as a problem of estimating the object’s trajectory, done by means of a video image. There are several tools for tracking objects and are used in various fields of research, such as computer vision, digital video processing, and autonomous vehicle navigation [1]. With the emergence of high-performance computers, high-resolution cameras, and the growing use of so-called autonomous systems that, in addition to these items, require specialized tracking algorithms, increasingly accurate and robust for automatic video analysis, has currently been the target of numerous research on the development of new object tracking techniques [2, 3].
Object tracking techniques are applicable to motion-based reconnaissance cases [4], automatic surveillance systems [5], pedestrian flow monitoring in crosswalks [6], traffic control [7], and autonomous vehicular navigation [8]. Problems of this type are highly complex due to the characteristics of the object and the environment, generating many variables, which impairs performance and makes the application of tracking algorithms unfeasible to real-world situations. Some approaches seek to resolve this impasse by simplifying the problem, reducing the number of variables [9]. This process, in most cases, does not generate good results [10, 11], making it even more difficult to identify the main attributes to be selected to perform a task [12, 13].
Most of the object tracking problems occur in open environments, so-called uncontrolled [14]. The complexity of these problems has attracted the interest of the scientific community and generated numerous applied research in various fields of research. Current approaches, such as the ones that use convolutional neural networks—CNN, deal well with the high number of variables of these types of problems, providing space–temporal information of the tracked objects, through three-dimensional convolutions [15, 16, 17]. This ends up creating an enormous number of learnable parameters, which ends up generating an overfitting [11]. A solution to reduce this number of learnable parameters was combining space–time data, extracted using the optical flow algorithm, used in the Two-Stream technique [18, 19, 20]. However, this technique presents good results only for large datasets, showing itself to be inefficient for small datasets [15, 21].
In recent years, research using machine learning has been applied to tracking problems, gaining notoriety due to the excellent results obtained in complex environments and attribute extraction [21, 22, 23]. Deep learning stands out among these techniques for presenting excellent results to unsupervised learning problems, [24], object identification [25], semantic segmentation [26]. Random trees are also examples of machine learning techniques, and their excellent results, due to their precision and great capacity to handle a large volume of data and low overfitting tendency [27, 28], and widely used in research areas such as medicine, in the prediction of hereditary diseases [29], agriculture to increase the productivity of a given plantation crop and in astronomy, acting on the improvement of images captured by telescopes, in the spectrum electromagnetic radiation not visible to the human eye [30]. The possibilities of applications, and new trends and research related to machine learning techniques, with particular attention to random trees, allow the development of algorithms that can be combined with existing ones, in the case of optical flow algorithms, (belonging to computational field of view) taken advantage of in this way, the advantages of each [31, 32, 33].
Developing an algorithm whose objective is to track objects, using the particular advantages of these techniques in a combined way, justifies creating a tracking algorithm that combines the optical flow technique, adapted in this work in terms of the Gaussian curvature associated with a minimal surface, with a random trees waiting for it to capture on this surface a minimum number of optical flow vectors that characterize the moving object, accurately and with low computational cost, contributing not only in the fields of computational vision but in other branches of science, such as in medicine, it can help in the early identification of infarctions.
Due to the large number of studies related to the technique of object tracking, only a small number surrounding this theme will be addressed. The focus of this project is not to make a thorough study on the state of the art. With this in this item, the main works in the literature, associated with the tracking of objects, will be presented. Among the various approaches used for this context, we highlight those focused on the techniques of optical flow, and others belonging to machine learning, such as those that use identifications of patterns, which allow relating, framing, and justifying the development of this proposal and its importance, through its contribution, to the state of the art.
Object tracking is defined as a process that allows you to uniquely estimate and associate the movements of objects with consecutive image frames. The objects considered can be from one, the set of pixels belonging to a region of the image. The detection of pixels is done by a motion detector or objects, which allows to locate objects with similar characteristics that move, between consecutive frames.
These characteristics of the object to be tracked are compared with the characteristics of a reference object modeled by a classifier over a limited region of the so-called region of interest frame, where the probability of detection of the object is greater. Thus, according to [33], the detector of traced objects, locate several objects on the different parts of the region of interest and performs the comparison of these objects with the reference object. This process is performed for each frame and each object detected, candidate to be recognized as the greatest possible similarity, to the reference object can be represented, through a set of fixed-size characteristics, extracted from this region containing a set of pixels, which can be represented by a numerical array of data.
Thus, mathematically, the region containing a set of pixels belonging to the regions of the object of interest, where the characteristics that allow to test whether the region of the frame, in which the object to be traced is, is given by:
where,
According to the works of [34, 35], learning methods are used to adapt the changes of movement and other characteristics such as geometric aspect and appearance of the tracked object. These methods are usually used adaptive tracked object trackers and detectors. The following will be presented other types of object trackers, found in the literature.
According to [36], a classifier can be defined with a
The classifier aims to determine the best way to discriminate the data classes, on the space of characteristics. The test data form a set containing the characteristics of the candidate objects, which have not yet been classified. The position of the object to be tracked in the frame is defined as the position corresponding to the highest response of the detector of the object to be tracked on the
where the variable
Offline-trained classifiers are generally employed in object detectors designed to detect all new objects of interest that enter the camera’s field of view [37]. The training set
In [38], trackers that use the detection tracking technique deal with object tracking, as a binary classification problem whose goal is to find the best function
In [39], were developed trackers that used detectors of objects to be tracked, formed by classifiers in committee formed by binary classifiers said weak. For [40], a binary classifier is defined as a classifier, used in problems where the class
A classifier is said to be weak, when it has a probability of “hitting” a given data class, only slightly higher than a random classifier. The detector of the object to be tracked must separate the crawled object from the other objects and the environment. Its purpose and determine the position of the tracked object, according to the equations (1)–(3)
For [43], the term monitoring system, refers to the process of monitoring and autonomous control, without human intervention. This type of system has the function of detecting, classifying, tracking, analyzing, and interpreting the behavior of objects of interest. In [44, 45], this technique was used combined with statistical techniques for controlling people’s access to a specific location. It was also observed the use of intelligent monitoring systems, applied to building, port, or ship security [46, 47].
The functions comprised by a monitoring system are so-called low- and high-level tasks. Among some high-level tasks, we highlight the analysis, interpretation and description of behavior, the recognition of gestures, and the decision between the occurrence or not of a threat. Performing high-level tasks require that for each frame, the system needs to perform low-level tasks, which involve direct manipulation of the image pixels [48, 49, 50, 51, 52, 53, 54, 55, 56]. As an example, we highlight the processes of noise elimination, detection of connected components, and obtain information on the location and geometric aspect of the object of interest.
A monitoring system consists of five main components, which are presented in Figures 9. Some monitoring systems may not contain all components. The initial detector aims to detect the pixel regions of each frame that have a significant probability of containing an object to be tracked. This detector can be formed by a motion detector that detects all moving objects based on models of objects previously recorded in a database or based on characteristics extracted offline [40, 41]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
The regions containing the most relevant pixels are analyzed and then classified as objects of interest by the classifier [50, 51, 52, 53, 54]. Objects of interest are modeled and are now called reference objects so that the tracker determines its position frame by frame [55, 56]. The information obtained by the initial detector is processed by an image processor], which will have the function of eliminating noise, segmenting, and detecting the connected components.
A tracker, an integral part of a detector, is defined as a function that allows estimating the position of objects at each consecutive frame, through and defines the region of the object of interest, for each
Main component of a monitoring.
Several techniques that allow the calculation to have been developed in recent years to calculate the optical flow vector [57]. These methods are grouped according to their main characteristics and the approach used for the calculation of the optical flow. Thus, the differential methods performed in the studies in [56], the methods d and calculation of the optical flow through the frequency domain [46] the phase correlation methods [58], and the method of association between regions [59].
The method proposed in [56], allows the calculation of the optical flow for each point around a neighborhood of pixels. In [60], it is also considered a neighborhood of pixels, but in this case, the calculation of the optical flow is performed geometrically. In the work presented by [61] it is adding of the restrictions of regularization. In [62] turn active compare performance analyses were performed between the various algorithms and optical flow present in the literature.
This technique is considered robust for detaining and tracking moving objects from your images, both those captured by fixed or mobile cameras. This gives this technique, but high computational cost makes most practical applications unfeasible. Thus, to reduce this complexity, techniques of increasing resolutions were adopted in [63]. Also, for the same purpose, we used the techniques of subsampling on some of the pixels belonging to the object of interest to obtain optical flow [52].
Other authors also use a point of interest detector to select the best pixels for tracking and calculate the optical flow on these points [52, 64]. The reduction in the number of points to be tracked is associated with a decrease in computational complexity, so in [52] the points of interest were selected using the FAST algorithm [64].
The method developed by Lucas-Kanade [56], it is a differential method and widely used in the literature and having variations modifications. It allows you to estimate the optical flow for each point
where
New variations of the techniques were being proposed to make the calculation of the optical flow faster and faster. In [65] a tracker was proposed based on the algorithm of [56]. The translation of a point represented by a grid of rectangular sized pixels 25 × 25, was calculated and its validity is evaluated by calculating the SSD1 in the grid pixels in
In [51] objects were detected by subtracting the image from the environment and removed the movement of the camera with the calculation algorithm of the optical flow vector proposed by [56]. In the studies carried out in [66, 67], they showed that the reliability of the estimated optical flow reduced the case of some points of the object of interest whose optical flow cannot be represented by the same matrix given by the related transformation
In [67] they also modified Lucas - Kanade’s algorithm [56] by inserting the Hessian matrix in the calculation of the value of the variation of the related transformation
Already in the proposal presented in [68] was the development of algorithm to detect people in infrared images that combines the information of the value of pixels with a method of motion detection. The algorithm forms a relevant pixel map by applying thresholding segmentation. While the camera is still, an image
The method for tracking swimmers presented in [46], uses the information of the movement pattern by the optical flow and the appearance of the water that is modeled by a MoG.2 This allows you to calculate an optical flow vector for each pixel of the video independently of the other, through
In [69], a method was presented that incorporated physical restrictions to the calculation of optical flow. The tracker uses the constraints to extract the moving pixels with a lower failure rate. The calculation can be impaired when occlusions occur or when the environment has low light. The operator defines the physical constraints and selects the points of the
In [70], the points that are tracked with the optical flow are defined by applying the Canny edge detector on the pixels of the reference pixel map. Pixels that produce a high response to the Canny detector are the selected points.
In [43], optical flow is used as a characteristic for tracking the contour of the object. The contour is shifted in small steps until the position in which the optical flow vectors are homogeneous is found.
In [64], they performed an estimate of the translation and orientation of the reference object by calculating the optical flow of the pixels belonging to its silhouette. The coordinates of the centroid position are defined by minimizing the Hausdorff distance between the mean of the optical flow vectors of the reference object and the candidate object to be chosen as the object of interest.
Optical flow is defined as a dense vector field associated with the movement and apparent velocity of an object, given by the translation of pixels from consecutive frames in an image region. It can be calculated from the brightness restriction, considered constant, from the corresponding pixels in consecutive frames.
Mathematically be a pixel
So that equation (7) is called optical flow restriction and where the terms
The number of variables in equation (6) is greater than that of equations, which does not allow estimating components and vector, and determining a single solution for the optical flow restriction equation. With this, Lucas and Kanade proposed a solution to solve this problem. The solution method proposed by them considers the constant flow in a region formed by a set of pixels
Passing the set of equations given by equation (8) to the matrix form we have:
Using the least squares method, in the system of equations (9) in the form of matricial, the same can be solved. Therefore, the optical flow
Where:
Therefore, one has that:
Thus:
This method has a reduced computational cost to determine optical flow estimation when compared to other methods because it is simple, that is, it is since the region in which the variation of light intensity between pixels is minimal has a size
To calculate the optical flow over the size region
Where the terms
In view of the small variations present and accumulated along the vector field associated with the optic flow, which cause an additional error in equation (13), a regularization adjustment was made, given by equation (14):
Thus, combining equations (13) and (14), the error
where
where
and replacing the coefficients
whereas
It is possible to reduce the data system by (17), such as:
where the term
Where
Therefore, isolating terms
The Algorithm 1 is a pseudocode to generate the proposed optical flow vector, through equations (24) and (25) and that allow estimating the speed and position of an object, through a sequence of video images.
Begin
For I = 1…N do
Convert images to a gray tone
Calculate the partial derivatives of 1°and 2°orders of
Calculate constants
Calculate the discretized Laplacians of
Calculating Gaussian curvature
Calculate flow components (
End For
End
Developed by Breiman [63] in the mid-2000s, and later revised in [71] random trees are considered one of the best-supervised learning methods used in data prediction and classification. Due to its simplicity, low computational cost, great potential to deal with a large volume of data, and still present great accuracy of results, currently this method has become very popular being applied in various fields of science as data science [72]. Bioinformatics, Ecology, in real-life systems and recognition of 3D objects. In recent years, several studies have been conducted with the objective of making the technique more elaborate and seeking new practical applications [73, 74, 75].
Many studies were carried out with the aim of narrowing the existing gap between theory and practice can be seen in [58, 76, 77, 78]. Among the main components of random tree forests, one can highlight the bagging method [63], and the criterion of classification and regression called cart
Bagging (a bootstrap-aggregating contraction) is an aggregation scheme, which generates samples through the bootstrap method, from the original dataset. These methods are nonparametric and belong to the Monte Carlos method class [80], treating the sample as a finite population. Still, these methods are used when the distribution of the target population is not specified, and the sample is the only information available. How in this way a predictor of each sample is constructed, so that the decision is made through an average, and is more effective computational procedures to improve the indexable estimates, especially for large sets of high-dimensional data, where finding a good model in one step is impossible due to the complexity and scale of the problem. As for the cart-split criterion, it originates from the CART program [63], and is used in the construction of individual trees to choose the best cuts perpendicular to the Axes. However, while bagging and the CART division scheme are key elements in the random forest, both are difficult to mathematically analyze and are a very promising field for both theoretical and practical research.
In general, the set of trees is organized in the form of {
The methodology employed consisted of combining the optical flow algorithm in terms of Gaussian curvature, developed in this work together with the technique of random forest. The language used for the development of this algorithm was the MATLAB programming language, executed on a 64-bit 8th generation notebook, CORE i7. The input data is a video extension Avi, lasting 5 min of a vehicle and two cyclists, circulating in the vicinity of the beach of Costa Nova, in the locality of Ilhavo, in Aveiro, Portugal. The video was fragmented into a set of frames, analyzed two by two by the algorithm for the generation of the vector field of optical flow. After that, the resulting image associated with the flow and a minimal surface region, given by the Gaussian curvature. Next on this surface, the random trees analyzed which vectors presented important characteristics to characterize in an “optimal” way, the movement of the object (see Figure 2).
Representative model of operation of a random forest.
After finishing the process of analysis of the movement of the objects, the execution times and accuracy of the results obtained by the proposed algorithm were compared in relation to the algorithms of Lucas Kanade, Horn and Shunck, Farneback and Lucas Kanade with or without Gaussian filter, allowing to validate the results obtained. After that, the implementation of the developed algorimo began.
Figure 3 shows the vehicle and the two cyclists that were used to collect the image to which the results proposed in this work were obtained so that the choice was random on the right side. A graphical representation of the vector field of optical flow generated by the sequence of two consecutive frames, over 5 minutes of video is shown.
(a) Left side: vehicle shift between moments
On the right side of Figure 3, the optical flow associated with the movement of the vehicle between the time intervals from
The region with the highest horizontal vector density in Figure 3 is located on the left side, in blue. It is also observed that the number of vectors in this region, despite being spaced, starting from the center to the left, is greater in relation to the number of vectors on the right side. It is also possible, through it, to visually evaluate the movement behavior of the considered objects. This region, containing a higher vector density, corresponds to the current direction in which the object is heading and its predicted displacement. It is also possible to observe that this vector density increases towards the left side, passing through the central part, coming from the right, clearly indicating the direction of movement of the object, that is, the object moves to the left. In Figure 4, this process can be understood more clearly.
Prediction and actual displacement of the object obtained through the optical flow.
In a similar way to the one mentioned in Figure 3, on the right side of Figure 5, the optical flow generated by the displacement of the moving vehicle is represented, between the instants
Object remains on the right side, but with a medium offset to the right and displacement estimate still to the left.
It is possible to observe a small increase in the vector density to the left, but that has a great influence on the determination of the real and predicted position of the object in the considered time intervals. The Object continues with its actual movement to the left, as well as the predicted movement of the object to the left. However, he showed a slight movement to the left (direction where the cyclists are).
In Figure 6, a small variation of the optical flow is observed again in the associated movement between the instants
Object remains on the right side, but with a slight shift to the right and offset estimate to the left.
In Figure 7, there was no optical flow variation in the associated movement between the time intervals
Object moving and keeping on the left for consecutive frames.
In Figure 8, the vehicle can be seen completely overtaking the two cyclists and approaching another vehicle in the opposite direction in the upper part of the image (left). The variation of the optical flow vector field remains the same. This indicates that the vehicle continues its trajectory, on the left side to the cyclists, however without posing a danger of collision for the other vehicle in the opposite direction.
Object with unchanged offset pattern.
This item will show how the performance evaluation of the proposed algorithm and accuracy was performed in relation to the Algorithms of Luca and Kanade, with or without Gaussian filter, Horn and Schunck, and Farneback.
The algorithm allowed to show on the display in real-time the displacement of the object on the right side and the set of vectors capable of representing the movement of the real-time or accumulated indicating the tendency, in this case, of the direction that the object should perform. This process was carried out in a similar way, using the other algorithms to make it possible to compare them. The behavior of the proposed algorithm and the other will be graphically shown.
The technique developed in this work allowed to generate an optical flow considering important geometric properties allowing to identify similar categories of moving objects and same characteristics. These geometric properties are intrinsically associated with the curvature of the object’s surface in three-dimensional space, called Gaussian curvature, in this case in a 2D image.
The modified optical flow, considering these properties, generated a dense optical flow, allowing the generation of a band, describing a track on the 2D plane. This allowed tracking the movement of the considered object. In the same Figure 8, it is possible to observe that at each time interval in which the object was monitored, the dispositions of the vectors for the left and right sides, as shown in Figures 3–7 were responsible for drawing the track associated with the displaced and that allowed tracking the object as it moves.
Figure 9 shows the vehicle that, when moving, generated the optical flow. In Figures 10 and 11, the variations of the optical flow between two-time intervals,
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
In the following items, the implementations of the Lucas and Kanade algorithms without or with a Gaussian filter, Horn and Schunck, and Farneback will be shown, using as input data the same sequence of video images used in the algorithm developed in this work. For each, the performance and accuracy obtained will be verified.
For each of the 5 algorithms, 1 frame is shown containing 4 figures, with 2 upper and 2 lower. In each frame, the figure at the top left shows the variation of the vector field between two frames. The right frame, on the other hand, corresponds to the variation of the object’s movement in real-time. The lower ones, except for the proposed algorithm, correspond to the number of points on the right or the left, and with this, the movement will occur to the side that has the greatest number of points. In the case of the proposed algorithm, the process will take place through the analysis of vector density. So, to the side where there is greater vector density, this is the side to which the movement will be occurring (see Figures 12–20).
Object moving to the right side.
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
Object moving to the right side.
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the left side.
Object moving to the right side.
Comparing the results presented by the algorithms, it is observed that in the developed model, it was possible to see a dense vector trail of the object, with a slight tendency of displacement to the left, as it continues its movement. In the other models, this was not possible, and it is necessary to resort to a score of points, in the lower table. This process is also possible in the proposed model, but not necessary, which means a reduction in computational cost (see Figures 21–28).
Variation of the optical flow of the moving object.
Vehicle movement.
Object moving to the right side.
Object moving to the left side.
Variation of the optical flow of the moving
Vehicle movement.
Object moving to the left side.
Object moving to the left side.
Comparing the results, it is observed that the Farneback algorithm also presents high vector density. But the proposed model, as previously said, presents a well-defined vector trail which suggests the non-use of the point count in the lower frame, which does not occur for the Farneback algorithm, indicating higher computational cost, which can affect the accuracy of this algorithm when compared to the proposed algorithm.
Comparing the Horn and Schunck algorithm, a low vector density is observed when compared to the proposed algorithm, which indicates lower accuracy when compared to the proposed algorithm.
Although the two techniques of Lucas and Kanade, are faster applications, indicating low computational cost when compared to the proposed algorithm, the factor of low vector density results in low precision in relation to the proposed method.
The proposed method presented good results, showing to be accurate and reasonable speed. This allows this application to be used in critical problems, i.e., to real-world problems. However, it presented limitations that could be verified when compared to the model with Lucas and Kanade, with a Gaussian filter, which is faster and presents good accuracy.
The proposed Method reached only approximately 50% execution speed in relation to the Lucas and Kanade Method, which motivates further improvements to the Method. The technique presented can be applied to other fields of research as in cardiology due to presenting great precision when submitted to small region, which is important because it can be applied with the objective of predicting infarctions and as a current contribution, for the state of the art is to characterize the optical flow in terms of Gaussian curvature, that makes it possible to highlight fields of research such as computational vision and differential geometry.
The authors of this work would like to thank the Institute of Electronics and Informatics Engineering of Aveiro, the Telecommunications Institute of Aveiro, and the University of Aveiro for the financial, technical-administrative, and structural support provided that allowed the accomplishment of this work.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nIntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
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\n\nAll scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
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Saleh and Amal I. Hassan",coverURL:"https://cdn.intechopen.com/books/images_new/11139.jpg",editedByType:"Edited by",publishedDate:"August 17th 2022",editors:[{id:"144691",title:"Prof.",name:"Hosam M.",middleName:null,surname:"Saleh",slug:"hosam-m.-saleh",fullName:"Hosam M. Saleh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"278",title:"Social Psychology",slug:"social-psychology",parent:{id:"23",title:"Social Sciences",slug:"social-sciences"},numberOfBooks:7,numberOfSeries:0,numberOfAuthorsAndEditors:235,numberOfWosCitations:29,numberOfCrossrefCitations:55,numberOfDimensionsCitations:109,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"278",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"7827",title:"Interpersonal Relationships",subtitle:null,isOpenForSubmission:!1,hash:"ebf41f4d17c75010eb3294cc8cac3d47",slug:"interpersonal-relationships",bookSignature:"Martha Peaslee Levine",coverURL:"https://cdn.intechopen.com/books/images_new/7827.jpg",editedByType:"Edited by",editors:[{id:"186919",title:"Dr.",name:"Martha",middleName:null,surname:"Peaslee Levine",slug:"martha-peaslee-levine",fullName:"Martha Peaslee Levine"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10450",title:"Evolutionary Psychology Meets Social Neuroscience",subtitle:null,isOpenForSubmission:!1,hash:"bd4df54e3fb185306ec3899db7044efb",slug:"evolutionary-psychology-meets-social-neuroscience",bookSignature:"Rosalba Morese, Vincenzo Auriemma and Sara Palermo",coverURL:"https://cdn.intechopen.com/books/images_new/10450.jpg",editedByType:"Edited by",editors:[{id:"214435",title:"Dr.",name:"Rosalba",middleName:null,surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10814",title:"Anxiety, Uncertainty, and Resilience During the Pandemic Period",subtitle:"Anthropological and Psychological 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Alice Vilas Boas"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:7,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"66422",doi:"10.5772/intechopen.85463",title:"Vulnerability and Social Exclusion: Risk in Adolescence and Old Age",slug:"vulnerability-and-social-exclusion-risk-in-adolescence-and-old-age",totalDownloads:1162,totalCrossrefCites:8,totalDimensionsCites:11,abstract:"Vulnerability can be defined as the quality or state of being exposed to the possibility of being attacked or harmed, either physically or emotionally. In this chapter, it is defined as a possible ability of an individual or a group to face, manage, and anticipate a possible problem. This concept of vulnerability is associated with that of risk factor for social isolation, and therefore to situations that can also lead to illness and lack of mental and physical health. It can have its roots in poverty, in social exclusion, in ethnicity, in disability or simply in disease or specific developmental phases in life. All these aspects reflect very important vulnerability factors among biological, psychological, social, and behavioral variables. To date, no one has highlighted together two critical moments in life in which this brain area undergoes important variations: adolescence, in which its development occurs, and old age, in which this area goes into cognitive decline with the relative loss of many higher cognitive functions. This knowledge can help to better understand the forms of exclusion due to vulnerability in order to develop new forms of social inclusion.",book:{id:"8262",slug:"the-new-forms-of-social-exclusion",title:"The New Forms of Social Exclusion",fullTitle:"The New Forms of Social Exclusion"},signatures:"Rosalba Morese, Sara Palermo, Matteo Defedele, Juri Nervo and Alberto Borraccino",authors:[{id:"214435",title:"Dr.",name:"Rosalba",middleName:null,surname:"Morese",slug:"rosalba-morese",fullName:"Rosalba Morese"},{id:"218983",title:"BSc.",name:"Juri",middleName:null,surname:"Nervo",slug:"juri-nervo",fullName:"Juri Nervo"},{id:"218984",title:"MSc.",name:"Matteo",middleName:null,surname:"Defedele",slug:"matteo-defedele",fullName:"Matteo Defedele"},{id:"233998",title:"Ph.D.",name:"Sara",middleName:null,surname:"Palermo",slug:"sara-palermo",fullName:"Sara Palermo"},{id:"266453",title:"Prof.",name:"Alberto",middleName:null,surname:"Borraccino",slug:"alberto-borraccino",fullName:"Alberto Borraccino"}]},{id:"74550",doi:"10.5772/intechopen.95395",title:"School Conflicts: Causes and Management Strategies in Classroom Relationships",slug:"school-conflicts-causes-and-management-strategies-in-classroom-relationships",totalDownloads:2333,totalCrossrefCites:1,totalDimensionsCites:10,abstract:"Conflicts cannot cease to exist, as they are intrinsic to human beings, forming an integral part of their moral and emotional growth. Likewise, they exist in all schools. The school is inserted in a space where the conflict manifests itself daily and assumes relevance, being the result of the multiple interpersonal relationships that occur in the school context. Thus, conflict is part of school life, which implies that teachers must have the skills to manage conflict constructively. Recognizing the diversity of school conflicts, this chapter aimed to present its causes, highlighting the main ones in the classroom, in the teacher-student relationship. It is important to conflict face and resolve it with skills to manage it properly and constructively, establishing cooperative relationships, and producing integrative solutions. Harmony and appreciation should coexist in a classroom environment and conflict should not interfere, negatively, in the teaching and learning process. This bibliography review underscore the need for during the teachers’ initial training the conflict management skills development.",book:{id:"7827",slug:"interpersonal-relationships",title:"Interpersonal Relationships",fullTitle:"Interpersonal Relationships"},signatures:"Sabina Valente, Abílio Afonso Lourenço and Zsolt Németh",authors:[{id:"324514",title:"Ph.D.",name:"Sabina",middleName:"N.",surname:"Valente",slug:"sabina-valente",fullName:"Sabina Valente"},{id:"326375",title:"Ph.D.",name:"Abílio",middleName:"Afonso",surname:"Lourenço",slug:"abilio-lourenco",fullName:"Abílio Lourenço"},{id:"329177",title:"Dr.",name:"Zsolt",middleName:null,surname:"Németh",slug:"zsolt-nemeth",fullName:"Zsolt Németh"}]},{id:"55323",doi:"10.5772/intechopen.68873",title:"Positive Psychology: The Use of the Framework of Achievement Bests to Facilitate Personal Flourishing",slug:"positive-psychology-the-use-of-the-framework-of-achievement-bests-to-facilitate-personal-flourishing",totalDownloads:1748,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"The Framework of Achievement Bests, which was recently published in Educational Psychology Review, makes a theoretical contribution to the study of positive psychology. The Framework of Achievement Bests provides an explanatory account of a person’s optimal best practice from his/her actual best. Another aspect emphasizes on the saliency of the psychological process of optimization, which is central to our understanding of person’s optimal functioning in a subject matter. Achieving an exceptional level of best practice (e.g. achieving excellent grades in mathematics) does not exist in isolation, but rather depends on the potent impact of optimization. This chapter, theoretical in nature, focuses on an in‐depth examination of the expansion of the Framework of Achievement Bests. Our discussion of the Framework of Achievement Bests, reflecting a methodical conceptualization, is benchmarked against another notable theory for understanding, namely: Martin Seligman’s PERMA theory. For example, for consideration, one aspect that we examine entails the extent to which the Framework of Achievement Bests could explain the optimization of each of the five components of PERMA (e.g. how does the Framework of Achievement Bests explain the optimization of engagement?).",book:{id:"5761",slug:"quality-of-life-and-quality-of-working-life",title:"Quality of Life and Quality of Working Life",fullTitle:"Quality of Life and Quality of Working Life"},signatures:"Huy P. Phan and Bing H. Ngu",authors:[{id:"196435",title:"Prof.",name:"Huy",middleName:"P",surname:"Phan",slug:"huy-phan",fullName:"Huy Phan"}]},{id:"55349",doi:"10.5772/intechopen.68596",title:"The Development of a Human Well-Being Index for the United States",slug:"the-development-of-a-human-well-being-index-for-the-united-states",totalDownloads:2049,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"The US Environmental Protection Agency (EPA) has developed a human well-being index (HWBI) that assesses the over-all well-being of its population at the county level. The HWBI contains eight domains representing social, economic and environmental well-being. These domains include 25 indicators comprised of 80 metrics and 22 social, economic and environmental services. The application of the HWBI has been made for the nation as a whole at the county level and two alternative applications have been made to represent key populations within the overall US population—Native Americans and children. A number of advances have been made to estimate the values of metrics for counties where no data is available and one such estimator—MERLIN—is discussed. Finally, efforts to make the index into an interactive web site are described.",book:{id:"5761",slug:"quality-of-life-and-quality-of-working-life",title:"Quality of Life and Quality of Working Life",fullTitle:"Quality of Life and Quality of Working Life"},signatures:"J. Kevin Summers, Lisa M. Smith, Linda C. Harwell and Kyle D. Buck",authors:[{id:"197485",title:"Dr.",name:"J. Kevin",middleName:null,surname:"Summers",slug:"j.-kevin-summers",fullName:"J. Kevin Summers"},{id:"197486",title:"Ms.",name:"Lisa",middleName:null,surname:"Smith",slug:"lisa-smith",fullName:"Lisa Smith"},{id:"197487",title:"Ms.",name:"Linda",middleName:null,surname:"Harwell",slug:"linda-harwell",fullName:"Linda Harwell"},{id:"197488",title:"Dr.",name:"Kyle",middleName:null,surname:"Buck",slug:"kyle-buck",fullName:"Kyle Buck"}]},{id:"56529",doi:"10.5772/intechopen.70237",title:"Well-being and Quality of Working Life of University Professors in Brazil",slug:"well-being-and-quality-of-working-life-of-university-professors-in-brazil",totalDownloads:1682,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"This chapter presents a study about the perceptions on quality of working life (QWL) regarding factors and indicator in two public universities in Brazil. It aimed also to analyze their perceptions about university working conditions. This exploratory study is based on quantitative and qualitative analyses. A sample of 715 university professors participated on the research. Data collection was carried out in two steps: online survey and focus groups. There is a moderate negative correlation between psychological well-being and work-related stress. Emotional charge also presents a moderate positive correlation with work-related stress, as well as physical charge and psychological distress. Work-life balance is negatively correlated with physical charge, emotional charge, work-related stress, psychological distress, and burnout. We observed also that 43.6% of the professors reported high levels of work-related stress in their everyday work. The precariousness of university teaching is associated with three main elements, which we defined as the tripod of the precarization of university teaching work. It consists of academic productivism, excess of administrative work and bureaucratic activities, and inadequate working conditions. The operating dynamics of this tripod effect professors’ well-being, their QWL, and even the quality of the work they develop in public universities.",book:{id:"5761",slug:"quality-of-life-and-quality-of-working-life",title:"Quality of Life and Quality of Working Life",fullTitle:"Quality of Life and Quality of Working Life"},signatures:"Alessandro Vinicius de Paula and Ana Alice Vilas Boas",authors:[{id:"175373",title:"Dr.",name:"Ana Alice",middleName:null,surname:"Vilas Boas",slug:"ana-alice-vilas-boas",fullName:"Ana Alice Vilas Boas"},{id:"196534",title:"Dr.",name:"Alessandro Vinicius",middleName:null,surname:"De Paula",slug:"alessandro-vinicius-de-paula",fullName:"Alessandro Vinicius De Paula"}]}],mostDownloadedChaptersLast30Days:[{id:"74550",title:"School Conflicts: Causes and Management Strategies in Classroom Relationships",slug:"school-conflicts-causes-and-management-strategies-in-classroom-relationships",totalDownloads:2339,totalCrossrefCites:1,totalDimensionsCites:10,abstract:"Conflicts cannot cease to exist, as they are intrinsic to human beings, forming an integral part of their moral and emotional growth. Likewise, they exist in all schools. The school is inserted in a space where the conflict manifests itself daily and assumes relevance, being the result of the multiple interpersonal relationships that occur in the school context. Thus, conflict is part of school life, which implies that teachers must have the skills to manage conflict constructively. Recognizing the diversity of school conflicts, this chapter aimed to present its causes, highlighting the main ones in the classroom, in the teacher-student relationship. It is important to conflict face and resolve it with skills to manage it properly and constructively, establishing cooperative relationships, and producing integrative solutions. Harmony and appreciation should coexist in a classroom environment and conflict should not interfere, negatively, in the teaching and learning process. This bibliography review underscore the need for during the teachers’ initial training the conflict management skills development.",book:{id:"7827",slug:"interpersonal-relationships",title:"Interpersonal Relationships",fullTitle:"Interpersonal Relationships"},signatures:"Sabina Valente, Abílio Afonso Lourenço and Zsolt Németh",authors:[{id:"324514",title:"Ph.D.",name:"Sabina",middleName:"N.",surname:"Valente",slug:"sabina-valente",fullName:"Sabina Valente"},{id:"326375",title:"Ph.D.",name:"Abílio",middleName:"Afonso",surname:"Lourenço",slug:"abilio-lourenco",fullName:"Abílio Lourenço"},{id:"329177",title:"Dr.",name:"Zsolt",middleName:null,surname:"Németh",slug:"zsolt-nemeth",fullName:"Zsolt Németh"}]},{id:"76968",title:"In the Darkness of This Time: Wittgenstein and Freud on Uncertainty",slug:"in-the-darkness-of-this-time-wittgenstein-and-freud-on-uncertainty",totalDownloads:466,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Both Wittgenstein and Freud experienced the crisis of humanism resulting from the first and second world wars. Although they were both considered to be influential figures, they hardly investigated the ways in which people could cope with the consequences of these crises. However, Wittgenstein and Freud did suggest ways of understanding uncertainties caused by real life events, as well as by the nature of human thought processes. This article will explore the therapeutic ways of dealing with uncertainties common to both thinkers and the different concepts facilitating their methodologies. The central contention of this article is that both Wittgenstein and Freud developed a complex methodology, acknowledging the constant and unexpected changes humans have deal with, whilst also offering the possibility of defining “hinge propositions” and “language-games” which can stabilize our consciousness.",book:{id:"10814",slug:"anxiety-uncertainty-and-resilience-during-the-pandemic-period-anthropological-and-psychological-perspectives",title:"Anxiety, Uncertainty, and Resilience During the Pandemic Period",fullTitle:"Anxiety, Uncertainty, and Resilience During the Pandemic Period - Anthropological and Psychological Perspectives"},signatures:"Dorit Lemberger",authors:[{id:"325725",title:"Dr.",name:"Dorit",middleName:null,surname:"Lemberger",slug:"dorit-lemberger",fullName:"Dorit Lemberger"}]},{id:"76565",title:"Introductory Chapter: The Transition from Distress to Acceptance of Human Frailty - Anthropology and Psychology of the Pandemic Era",slug:"introductory-chapter-the-transition-from-distress-to-acceptance-of-human-frailty-anthropology-and-ps",totalDownloads:398,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"10814",slug:"anxiety-uncertainty-and-resilience-during-the-pandemic-period-anthropological-and-psychological-perspectives",title:"Anxiety, Uncertainty, and Resilience During the Pandemic Period",fullTitle:"Anxiety, Uncertainty, and Resilience During the Pandemic Period - Anthropological and Psychological Perspectives"},signatures:"Fabio Gabrielli and Floriana Irtelli",authors:[{id:"174641",title:"Dr.",name:"Floriana",middleName:null,surname:"Irtelli",slug:"floriana-irtelli",fullName:"Floriana Irtelli"},{id:"259407",title:"Prof.",name:"Fabio",middleName:null,surname:"Gabrielli",slug:"fabio-gabrielli",fullName:"Fabio Gabrielli"}]},{id:"77214",title:"The Impact of the COVID-19 Pandemic on the Mental Health of Dentists",slug:"the-impact-of-the-covid-19-pandemic-on-the-mental-health-of-dentists",totalDownloads:394,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Since March 2020, the COVID-19 disease has declared a pandemic producing a worldwide containment. For months, many people were subjected to strict social isolation away from family and loved ones to prevent disease transmission, leading to anxiety, fear, and depression. On the other hand, many had to close down their businesses and stop working, resulting in financial issues. Previous studies have reported that pandemics, epidemics, and some diseases can lead to mental disorders such as fear, anxiety, stress, and depression. Among those most affected, healthcare workers (HCWs), especially those on the front line, often develop mental health problems. Although there is data available on the management and care of HCWs, little attention has been paid to the mental health and well-being of dentists during the COVID-19 pandemic. Therefore, this chapter aims to review the impact of the COVID-19 pandemic on dentists’ mental health and mental health-related symptoms. Finally, to recommend specific measures to avoid consequent potential implications for dentists, dental students, and dental patients.",book:{id:"10814",slug:"anxiety-uncertainty-and-resilience-during-the-pandemic-period-anthropological-and-psychological-perspectives",title:"Anxiety, Uncertainty, and Resilience During the Pandemic Period",fullTitle:"Anxiety, Uncertainty, and Resilience During the Pandemic Period - Anthropological and Psychological Perspectives"},signatures:"Andrea Vergara-Buenaventura and Carmen Castro-Ruiz",authors:[{id:"346660",title:"M.Sc.",name:"Andrea",middleName:null,surname:"Vergara-Buenaventura",slug:"andrea-vergara-buenaventura",fullName:"Andrea Vergara-Buenaventura"},{id:"419814",title:"MSc.",name:"Carmen",middleName:null,surname:"Castro-Ruiz",slug:"carmen-castro-ruiz",fullName:"Carmen Castro-Ruiz"}]},{id:"55323",title:"Positive Psychology: The Use of the Framework of Achievement Bests to Facilitate Personal Flourishing",slug:"positive-psychology-the-use-of-the-framework-of-achievement-bests-to-facilitate-personal-flourishing",totalDownloads:1750,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"The Framework of Achievement Bests, which was recently published in Educational Psychology Review, makes a theoretical contribution to the study of positive psychology. The Framework of Achievement Bests provides an explanatory account of a person’s optimal best practice from his/her actual best. Another aspect emphasizes on the saliency of the psychological process of optimization, which is central to our understanding of person’s optimal functioning in a subject matter. Achieving an exceptional level of best practice (e.g. achieving excellent grades in mathematics) does not exist in isolation, but rather depends on the potent impact of optimization. This chapter, theoretical in nature, focuses on an in‐depth examination of the expansion of the Framework of Achievement Bests. Our discussion of the Framework of Achievement Bests, reflecting a methodical conceptualization, is benchmarked against another notable theory for understanding, namely: Martin Seligman’s PERMA theory. For example, for consideration, one aspect that we examine entails the extent to which the Framework of Achievement Bests could explain the optimization of each of the five components of PERMA (e.g. how does the Framework of Achievement Bests explain the optimization of engagement?).",book:{id:"5761",slug:"quality-of-life-and-quality-of-working-life",title:"Quality of Life and Quality of Working Life",fullTitle:"Quality of Life and Quality of Working Life"},signatures:"Huy P. Phan and Bing H. 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Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. 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He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. 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He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. 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She created the research group in applied biochemistry in 2017 (https://web.ua.es/en/appbiochem/), and from 1999 to the present has made more than 200 contributions to Spanish and international conferences. Furthermore, she has around seventy-five scientific publications in indexed journals, eighty book chapters, and one patent to her credit. Her research work focuses on microbial metabolism (particularly on extremophile microorganisms), purification and characterization of enzymes with potential industrial and biotechnological applications, protocol optimization for genetically manipulating microorganisms, gene regulation characterization, carotenoid (pigment) production, and design and development of contaminated water and soil bioremediation processes by means of microorganisms. 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