Overview of the PO improved by the PP approaches.
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
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\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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He is a member of various national and international professional societies.",institutionString:"Government College University, Faisalabad",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"29",title:"Agronomy",slug:"agronomy"}],chapters:[{id:"73630",title:"Introductory Chapter: Recent Advances in Rice Biotechnology for Abiotic Stress Tolerance",slug:"introductory-chapter-recent-advances-in-rice-biotechnology-for-abiotic-stress-tolerance",totalDownloads:402,totalCrossrefCites:0,authors:[{id:"185476",title:"Dr.",name:"Mahmood-ur-Rahman",surname:"Ansari",slug:"mahmood-ur-rahman-ansari",fullName:"Mahmood-ur-Rahman Ansari"},{id:"252871",title:"Ms.",name:"Munazza",surname:"Ijaz",slug:"munazza-ijaz",fullName:"Munazza Ijaz"},{id:"309846",title:"Dr.",name:"Roshina",surname:"Shahzadi",slug:"roshina-shahzadi",fullName:"Roshina Shahzadi"},{id:"332231",title:"Dr.",name:"Shazia Anwer",surname:"Bukhari",slug:"shazia-anwer-bukhari",fullName:"Shazia Anwer Bukhari"},{id:"332232",title:"Prof.",name:"Akmaral U.",surname:"Issayeva",slug:"akmaral-u.-issayeva",fullName:"Akmaral U. 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Vehicles",doi:"10.5772/intechopen.99576",slug:"recent-developments-in-path-planning-for-unmanned-aerial-vehicles",body:'In recent years, unmanned aerial vehicles (UAVs) have become a powerful tool for diverse missions including polymerase chain reaction (PCR) samples transportation between hospital and laboratories [1], UAV-based healthcare system to control COVID-19 pandemic [2], infectious diseases containment and mitigation [3], traffic condition analysis in co-operation with deep learning approaches [4], and human behavior understanding via multimedia data analytics in a real-time [5], to name a few. Currently, UAVs integration with the emerging technologies such as block chain, internet of things, cloud computing, and artificial intelligence can pave the way to serve mankind effectively compared to the recent past [6]. Further, the peculiarity of UAVs in terms of performing operations in 3D (dull, dirty, and dangerous) environments, they can play a vital role in realization of the smart cities. Furthermore, UAVs are inevitable tool during emergency planning and disaster management due to their abilities to perform missions aerially. Besides the UAVs applications and use cited above, they can be highly beneficial for military purposes including information collection and analysis, border surveillance, and transporting warfare items. The role of UAVs in agriculture from multiple perspectives have already been recognized across the globe. Recently, world’s leading commerce company (i.e., Amazon) has started using UAVs for delivering their products to customers. Generally, the use of UAVs is expected to rise in many emerging sectors in the near future. We present actual and innovative use of the UAVs during the ongoing pandemic in Figure 1. Majority of the applications given in Figure 1 employed multiple UAVs in order to accomplish the desired tasks.
Innovative applications of the UAVs during the ongoing pandemic (adopted from [
Although UAVs are highly beneficial for mankind through their innovative applications, but there exist plenty of challenges that can hinder their use at a wider scale. For example, payload constraints and power issues can limit their carrier abilities. Similarly, decision making during flight to ensure UAVs safety by avoiding obstacles with sufficient accuracy is a non-trial task mainly due to no human-onboard control. Furthermore, communication from long distances, and co-ordination among multiple UAVs to perform complex tasks jointly are main barriers in the true realization of the UAVs technology. Besides the challenges and issues given above, many issues concerning software and hardware also exist that need rigorous developments and testing. Many solutions have been proposed to address these issues via cross disciplinary approaches. Meanwhile, extensive testing and analysis of these solutions is yet to be explored, especially in urban environments. In this chapter, we mainly focus on the ‘navigation’ that is one of the core challenges in the UAVs technology. The navigation quandary is classified into three cases: (i) where am I now?, (ii) where do I go?, and (iii) How do I get there?. The first two cases belong to the localization and mapping, and the third case is about path planning (PP) [8]. In this work, we cover third case comprehensively, and provide concepts and developments in this regard. We present a comprehensive overview about changing dynamics of the UAV applications in recent times, challenges of the UAV technology, recent developments in the UAV technology, and future research trends in the PP area in Figure 2. With this concise overview, we aim to aid researchers in extracting the contents enclosed in this chapter conveniently.
Overview of changing dynamics of the UAV applications, challenges, recent developments, and future research trends in the PP area.
The rest of this chapter is structured as follows. Section 2 discusses the basic concept of the path planning, and categorizes the path planning approaches based on the information available about underlying environment, and UAV used for the aerial mission. Section 3 describes the three essential components of the PP. Section 4 critically analyzes various approaches that were proposed to lower the computing time of the PP for UAVs. The future prospects of the research in the PP area are discussed in Section 5. Finally, this chapter is concluded in Section 6.
PP is to find a safe (i.e., collision-free) path between two pre-determined locations (e.g., source and destination, denoted with
Generally, there are three possibilities about the availability of information regarding environment where UAVs tend to operate. The operating environment can be fully known in advance (e.g., obstacles’ geometry information is known.), it can be completely unknown, and/or it can be partially known (e.g., few portions are known, and some portions are explored and modeled during the flight.). Based on the degree of information about environment, PP approaches are mostly classified into two categories, local PP (LPP) and global PP (GPP). In LPP, the environment is not known, and UAVs use sensors or other devices in order to acquire information about the underlying environment. In GPP, PP is performed in a fully known environment, meaning all information about environment is known in advance. Based on the availability of the information regarding underlying environment, GPP approaches have lower complexity compared to the LPP approaches. Recently, some PP approaches have jointly employed LPP and GPP concepts in order to find a path for UAVs [9]. In literature, GPP and LPP approaches are also classified as offline and online PP approaches, respectively. Based on the extensive review of the literature, we present a categorization of the PP approaches based on information about environment in Figure 3. We refer interested readers to gain more insights about the LPP approaches in the previous studies [10, 11].
Categorization of the PP approaches based on the availability of information about operating environment.
Apart from the categorization provided above, environment can be classified into rural and urban environments. The tendency of UAVs applications were high in the non-urban environments in the past. Moreover, due to the significant development in control domain, UAVs are increasingly employed in the urban environment these days. For instance, in urban environments, they can be used to monitor people compliance with the social guidelines given by the respective governments in order to control the COVID-19’s spread.
Based on the mission’s type, either one or multiple UAVs can be employed. The scenarios in which only one UAV is deployed are referred as single agent PP problem. In contrast, those scenarios in which multiple UAVs are used are called multiple agent PP problems. PP for multiple agents is relatively complex since UAVs need to avoid collision with the companion UAVs, and obstacles present in an underlying operating environment. In addition, allocating target areas for coverage and optimizing throughput also remain challenging, especially while operating at lower altitudes in urban environments.
Generally, there are three essential components of the PP: (i) modeling of the environment with geometrical shapes by utilizing the obstacles/free spaces knowledge provided by a real-environment map, (ii) task modeling with the help of graphs/trees keeping source and target locations in contact, and (iii) applying search algorithm inclusive of the heuristic function to determine a viable path.
In the first step, a raw environment map is converted into a modeled one, in which obstacles are represented with the help of geometrical shapes. For example, poles information provided by a real environment map can be modeled with the help of cylinders in the modeled map. Similarly, buildings can be modeled with the help of rectangles or polyhedron. In some cases, UAVs do not model the whole environment map, and utilize sense and avoid (SAA) abilities to operate safely in the airspace. We present an example of environment modeling, and well-known obstacles’ representation techniques used for the PP in Figure 4. Each obstacles representation technique has different complexity and accuracy in terms of real environment obstacles representations. In addition, each representation can be adopted considering the UAV operating environment. For example, polygons can be used to model an urban environment populated by various buildings.
Overview of environment modeling and obstacles’ representation techniques.
After modeling environment with the help of geometrical shapes, the next step is task modeling (e.g., generating network of paths with a graph/tree or selecting a desired portion to be modeled). For example, road-map approach is a well-known task modeling approach for the PP, in which a graph is constructed from the starting location to destination location by capturing the connectivity of free spaces and obstacles’ corners. Apart from it, cell-decomposition and potential field are promising solutions for the task modeling. We present most widely used task modeling methods in Figure 5.
Overview of the famous task modeling methods used in the PP adopted from [
Recently, trees-based task modeling methods have been widely used for the task modeling due to their quick convergence in the final solution. We present an overview of the task modeling with the help of tree in Figure 6. Furthermore, in some cases, more than one methods are jointly used to model the tasks on a provided map. In addition, some approaches use task modeling and path searching simultaneously [12].
Overview of task modeling with a random tree.
In the last step, a search algorithm is employed on the graph/tree to find a viable path. During the path search, a heuristic function usually accompany the path search. For example, in the A* algorithm, the low-cost nodes are determined leveraging distance as a heuristic function. Similarly, the heuristic function can be energy consumption or smoothness depending upon the scenario. In literature, many techniques have been suggested to find reliable paths. The path search algorithms, such as differential evolution [13], firefly algorithm [14], ant colony optimization [15], genetic algorithms [16], artificial bee colony [17], particle swarm optimization [18], fuzzy logic [19], central force optimization [20], gravitational search algorithm [21], simulated annealing [22] and their advanced variants are used in the PP. Every algorithm has numerous distinguishing factors over others regarding conceptual simplicity, computational complexity, robustness, and convergence rates etc. We categorize the existing path search methods into five categories, and present representative methods of each category in Figure 7.
Categorization of path searching methods/algorithms.
Every PP approach tends to optimize one or more performance objectives (PO) while finding a viable path for UAVs. The PO can be related to hardware and software. These PO are considered in the previous three components (i.e., environment modeling, task modeling, and path searching) related to the PP. For instance, in order to lower the PS computing time, only some portion of a map can be modeled and a sparse tree/graph can be constructed/used while finding a path. Similarly, memory can be preserved by exploring some portions of a graph/tree rather than loading and exploring whole graph/tree at a same time. The selection of PO solely depend on the nature and urgency of the mission. For example, in search and rescue missions, the PO can be path computing time in order to reach the affected regions quickly. In contrast, in normal circumstances, the PO can be the path length in order to reach the target location in a most economical way by preserving UAV’s resources. We describe various most commonly used PO in Table 1.
PO | Concise description |
---|---|
Computing time | It denotes overall time required to find a path using a graph/tree. |
Path length | It denotes the Euclidean distance between two locations. |
Energy | It denotes amount of energy required/consumed while reaching to target from source. |
Turns | It denotes number of turns (infeasible curvature) a path has in total. |
Smoothness | It denotes a turns in a path with a feasible curvatures. |
Memory | It denotes amount of memory used while computing a path. |
Path nodes | It denotes set of nodes that a UAV follows during flight. |
No. of obstacles | It denotes set of obstacles to be processed during path search. |
Accuracy | It denotes accuracy of obstacles modeling or path clearance from obstacles. |
Problem size | It denotes size of problem on which path is determined. |
Graph size | It denotes size of graph (no. of nodes, edges) employed to find a path. |
Convergence rate | It denotes how quickly a feasible solution can be obtained. |
Constraints handling | It denotes the effective resolution of constrains UAV faces during mission. |
Completeness | It denotes availability/non-availability of solution in a finite time. |
Flexibility | It denotes efforts/time required to make a solution usable for different missions. |
Path re-configuration | It denotes efforts/time required to gain the control of a lost path. |
Path following | It denotes the ability to keep following a path despite disturbances. |
Path safety | It denotes the ability to avoid collisions with static/dynamic obstacles. |
Hyper parameter | It denotes the number and variety of parameters to find a path. |
Obstacle avoidance | It denotes the ability to avoid static/dynamic obstacles with low-cost. |
Generalization | It denotes the ability of a method to be applicable for different types of UAVs. |
Application-speciality | It denotes the ability of a method to yield superior performance in some context. |
Endurance | It denotes the ability of a UAV to fly for a long period of time with low-cost planning. |
Overview of the PO improved by the PP approaches.
Some PO are positively co-related. For example, finding path with less turns can save energy.
Improving two negatively co-related PO (speed and time) require optimization of another PO (problem size).
These PO are usually considered during PP irrespective of the environment whether it is known or unknown. Furthermore, plenty of techniques have been proposed to improve these PO with innovative techniques or employing cross-disciplinary concepts. In addition, many PP approaches have targeted optimizing multiple objectives rather than one/two for practical UAVs application. These PO can be expressed as a functional model while finding a path
In this section, we discuss various PP algorithms that were proposed to lower the time complexity of the PP process. We selected various algorithms that were proposed in last five years (i.e., 2016–2021), and have somewhat identical concepts in terms of space restrictions and problem size reduction etc. We provide brief overview, and technically evaluation of all algorithms and highlight their deficiencies. Consequently, this analysis can pave the ways to improve PP algorithms for future UAVs’ applications.
We present brief overview of the selected algorithms in Table 2. These algorithms have become state-of-the-art for many practical applications of the UAVs in the urban/non-urban environments. They are famous due to their novel working mechanisms, and conceptual simplicity. In addition, they have mainly focused on the UAV applications in urban environments that is focus of research across the globe. Also, the UAVs’ applications in the urban environments are likely to increase in the coming years.
Ref. | Publication year | Environment used | PO improved |
---|---|---|---|
Maini et al. [23] | 2016 | 3D | Computing time and collision-free paths. |
Frontera et al. [24] | 2017 | 3D | Computing speed and solution quality. |
Ahmad et al. [25] | 2017 | 3D | Computing speed and energy-optimized paths. |
Majeed et al. [26] | 2018 | 3D | Computing speed and path quality. |
Han et al. [27] | 2019 | 3D | Feasible paths with reduced time. |
Ghambari et al. [28] | 2020 | 3D | Computing time and memory consumption. |
Majeed et al. [29] | 2021 | 3D | Computing speed and path quality. |
Overview of the latest GPP approaches that were proposed to reduce the computing time of PP process.
All these approaches have used concepts related to search space reduction in order to find time-efficient paths.
In this subsection, we provide concise description of the selected algorithms, and highlight their technical problems. We mainly describe the key steps of the proposed algorithms.
Maini et al. [23] algorithm computes a low-cost path using two-steps approach. In the first step, modified version of the Dijkstra algorithm is used to find an initial path. In the second step, initial path is optimized more by considering the initial path nodes, and reverse path search.
Frontera et al. [24] algorithm computes a low-cost path using three-steps approach. First, the proposed method reduce the search space by considering the obstacles that are on the straight axis between
Ahmad et al. [25] algorithm computes a low-cost path using four-steps approach. Firstly, search space is bounded using obstacles of the straight line only. Later, the bounded space is extended to next level by using the obstacles that hit the boundary of the first bounded space. In the third step, a relatively dense visibility graph is generated from the bounded spaces. In the final step, A* algorithm is employed to find an energy-optimized path.
Majeed et al. [26] algorithm computes a low-cost path using five-steps approach. First, the space is reduced into a half-cylinder form with path guarantees between
Han et al. [27] algorithm computes a low-cost path using three-steps approach. First, critical obstacles are identified through straight-axis between
Ghambari et al. [28] computes a global and local path with the help of four-steps. In the first step, search space is reduced around the straight axis. In the second step, differential evolution algorithm is applied to construct a graph. Later, A* algorithm is used to find a path from a graph constructed in the first step. In the third step, subspace is divided into small portions with alternate routes in each subspace. In the last step, a mechanism is suggested to avoid collision with the dynamic obstacles that may appear unexpectedly during the flight.
Majeed et al. [29] recently proposed a PP method for low-cost pathfinding for UAVs based on the constrained polygonal space and a waypoint graph that is extremely sparse. In proposed approach, search space is restricted into a polygonal form, and its analysis is performed from optimality point of view with the help of six complexity parameters. Later, space can be extended to next level if needed, else a very sparse graph is generated by exploiting the visibility, far-reachability, and direction guidance concepts. The suggested approach computes time-efficient paths without degrading path quality while finding paths from urban environments.
Besides the computing time, these algorithms can indirectly optimize certain PO listed in Table 1. For example, Ahmad et al. [25] PP approach reduces the number of turns also in order to lower the energy consumption. Han et al. [27] PP approach can be applied to the environments with arbitrary shaped obstacles (e.g., there exist no constraint related to the obstacles’ geometries). Hence, it can be applied in different settings (e.g., areas with sparse obstacles or areas with dense obstacles) of the urban environment. Similarly, Majeed et al. [29] PP approach can significantly reduce the problem size, thereby memory requirements can be magnificently lower. Ghambari et al. [28] approach can be used to re-configure paths during the flight when a UAV finds an unexpected obstacle. Hence, this approach can be used in both (i.e., local, and global) environments. Despite the utility of these approaches in many real-world applications, they often yield poor performance due to the local/global constraints. Based on the in-depth review of all studies, we identified potential problems of all approaches that may hinder their use in actual deployment. We describe technical challenges of the existing approaches in Table 3.
Ref. | Technical problems in the proposed approach |
---|---|
Maini et al. [23] | The performance cannot be ensured in each scenario due to heavy reliance on specific maps. Overheads can increase exponential with the problem size. It models the whole map thereby path exploration cost is very high. |
Frontera et al. [24] | Path can collide with the nearby obstacles. In some cases, proposed approach fails to find a path even though it exists. Visibility graph can contain many needless and redundant nodes. Memory consumption is higher due to loading of whole visibility map in the memory. |
Ahmad et al. [25] | Two bounded spaces are used that can increase the computing time of the PP. Visibility graph is constructed using layered approach with many redundant nodes and edges. Visibility check function is expensive since visibility in all directions and nodes is checked. |
Majeed et al. [26] | Path can contain turns due to the strict boundary of the search space. Path optimization cost may increase if initial path has many nodes. |
Han et al. [27] | Path quality cannot be ensured in all scenarios if obstacles’ sizes are large. Path cost can increase exponentially with the point set. Both time and optimality can be impacted if diverse shape obstacles exist in a map. Since this is grid-based approach thereby memory consumption is higher. |
Ghambari et al. [28] | Path computing time can rise with the distance between Recognition and avoiding obstacles in realtime can be costly. Fidelity of the proposed approach were analyzed with limited testing. Since path searching is carried out twice, thereby computing time can rise. |
Majeed et al. [29] | Accurate modeling of the tiny obstacles is not possible. Excessive calculations are performed in space analysis thereby complexity can rise. |
Overview of the technical problems in the proposed GPP approaches.
All these problems have been highlighted by existing studies or reported by the authors.
These challenges lay foundation for the future research in the UAVs area. Furthermore, they can assist researchers to devise better and practical PP approaches in order to address these technical problems. Apart from the challenges provided in Table 3, it is paramount to take into account the local constraints while devising PP methods that have been mostly assumed in the existing approaches.
Majority of the approaches discussed above are the GPP approaches, and LPP approaches have not been discussed. To cover this gap, we discuss various representative LPP approaches in Table 4 along with the methodological specifics.
Ref. | UAV used | Technical aspects of the approach |
---|---|---|
Stecz et al. [30] | Multiple | Indicated sensors based LPP approach. |
Wojciech et al. [31] | Single | EO/IR systems and SARs based navigation. |
Siemiatkowska et al. [32] | Multiple | MILP based LPP using EO/IR camera and SARs. |
Hong et al. [33] | Multiple | MILP-based multi-layered hierarchical architecture. |
Hua et al. [34] | Multiple | Multi-target intelligent assignment model based LPP. |
Cui et al. [35] | Single | Reinforcement learning (RL)-based LPP approach. |
Maw et al. [36] | Single | Graph and learning based LPP approach. |
Wei et al. [37] | Single | Improved ACO for LPP. |
Zhang et al. [38] | Single | Markov decision process (MDP) based LPP approach. |
Zammit et al. [39] | Multiple | LPP in the presence of uncertainties. |
Wu et al. [40] | Single | Interfered fluid dynamic system (IFDS) based LPP. |
Bayerlein et al. [41] | Multiple | Multi-agent reinforcement learning (MARL) approach for LPP. |
Jamshidi et al. [42] | Single | LPP based on improved version of Gray Wolf Optimization. |
Yan et al. [43] | Single | Sampling based LPP approach in urban environments. |
Sangeetha et al. [44] | Single | Gain-based dynamic green ACO (GDGACO) LPP approach. |
Sangeetha et al. [45] | Single | Fuzzy gain-based dynamic ACO (FGDACO) LPP approach. |
Choi et al. [46] | Single | Improved CNN based LPP approach for UAV. |
Overview of the latest LPP approaches used for UAVs.
All these approaches have used the unknown environment during the PP.
These approaches perform PP in environments that are mostly unknown, and are complex compared to the GPP approaches. These approaches enable UAVs to perform tasks in complex environments in real time leveraging low-cost sensors, and robust artificial intelligence (AI) techniques. In addition, these techniques have abilities to co-work with the emerging technologies including cloud, edge, and fog computing etc. for variety of applications. The role of UAVs was dominant during the ongoing pandemic in different countries across the globe. To this end, LPP approaches contributed significantly, and enhanced UAVs role in curbing the pandemic spread via online missions. Barnawi et al. [47] proposed an IoT-based platform for COVID-19 scanning in which UAVs were used as a main source of temperature data collection in the outdoor environments. Apart from the COVID-19 scanning, UAVs were extensively used for spraying and disinfecting multi-use facilities and contaminated places. In some countries, they were used for alerting people to wear masks properly, and stay indoors. The true realization of these innovative application is possible through LPP approaches.
Besides the LPP and GPP, another important subtopic of the PP is coverage path planning (CPP) [48]. In the CPP, a path is determined that enables UAV to cover a target area fully with the help of a device/tool mounted on it. The attached tool/device can be a sensor, camera, speaker, and/or a spray tank depending upon the mission. We present overview of the CPP in Figure 8. In Figure 8(a), a target area in the form of a rectangle is given that need to be covered with a UAV. In In Figure 8(b), a coverage path is shown that a UAV follows in order to cover the target area.
Overview of coverage path planning for UAVs in a 3D urban environments.
In the CPP, most of the POs are identical with that of the PP, but path overlapping, and coverage guarantees are two additional POs. Moreover, ensuring consistent path quality with respect to shape of the target area is very challenging. Therefore, shape of the target area is considered while finding a coverage path. CPP can be performed in five steps, modeling of the operating environment, locating target area on the modeled map, decomposition of the target area into disjoint sub parts, task modeling (mainly traversal order of the sub parts) with the help of a graph, and covering each sub-part using motion pattern (e.g., back and forth, spiral, and circular etc.). In recent years, UAVs’ coverage applications in the urban environments have significantly increased, and a substantial number of CPP approaches have been proposed [49].
In the near future, UAVs will be regarded as an inevitable tool for various practical missions, especially in the urban environments. A substantial number of developments are underway to fully realize smart cities, smart infrastructure, and smart buildings, to name a few. Thence, the use and applications of the UAVs are expected to grow significantly in the near future. Recently, many innovative technologies such as block-chain, IoT, 5G/6G technologies, and deep/machine learning approaches have been integrated with the UAVs technology to serve mankind in effective ways [50]. For example, BloCoV6 scheme [51] is one of the wonderful applications of the UAVs in the new normal (e.g., COVID-19 era). Similarly, many such innovative applications are likely to emerge in the near future as a replacement of human beings for complex tasks. Therefore, refinements in the existing PP approaches in relation with peculiarities of the applications/tasks, and development of robust approaches leveraging cross-disciplinary (e.g., biological inspired, AI-powered, and technology-driven) concepts have become necessary. Considering the emerging applications of the UAVs, we list prospects of the research in the near future in PP area in Figure 9. We categorize the avenues of future research in the PP area on four grounds (e.g., UAV application specific PP approaches, optimization of the existing approaches’ PO, integration of the emerging technologies and their issues handling, and developing PP approaches that can cope up with the dynamics of the UAV operating environment.).
Categorization of the avenues of future research in the PP/UAVs area.
The most important research avenues from the optimization point of view are, devising new environment restriction methods to reduce the problem sizes, devising low-cost methods for reducing the task modeling overheads (i.e., graph/tree sizes), and accelerating the PS methods that enable UAV to reach the target location safely with a significantly reduced cost. Furthermore, improving overall cost of the PP process is an important research direction to increase UAVs’ applications in the urban environments. Optimization of multiple objectives rather than single/two is handy in order to preserve UAV’s resources during aerial missions. From applications point of view, low-cost methods that can improve certain POs and can satisfy the applications features at the same time are needed. To this end, identifying each application’s features/requirements and embedding them into the PP process can enhance the UAVs use in the coming year significantly. Therefore, applications-oriented PP methods will be embraced more in the near future considering the UAVs potential in executing tasks at low costs. From environment dynamics point of view, PP methods that can effectively respond to the uncertainties/dynamics emerging from the environment are paramount. For example, in LPP, decision making to avoid obstacles with as least cost as possible can enhance UAV’s endurance in the aerial missions. In this regard, LPP methods that can cope up with the underlying operating environment variations and can ensure UAV’s safety consistently in the practical applications are paramount.
Recently, many emerging technologies have been integrated with the UAV technology. For example, blockchain, transfer learning, computer vision, federated learning, 5G and 6G technologies, and cloud computing etc. have revolutionized the UAVs’ applications. In this regard, incorporating more emerging technologies in the UAV domain, and extending the current emerging technologies use to more application areas is an important research direction for the future. Furthermore, improving the hardware capabilities of the UAV by integrating latest technologies are important need from technical perspectives. Despite the technical aspects mentioned above, tailoring computer vision applications in the UAV area is a most promising avenue of the research considering UAV abilities to capture images with good resolution [52]. In addition, identifying niche areas (i.e., water quality analysis, target tracking, covering spatially distributed regions, and detection of wildfire smoke, to name a few) where UAVs can perform well compared to humans, and performing cost–benefit analysis of the UAVs versus human is important research direction in the UAVs’ technology. Finally, exploring the possibilities towards joint use of multiple latest technologies in order to serve mankind in an effective way using UAVs is a vibrant area of research. Apart from the PP, devising low-cost CPP methods for UAVs is also an attractive area of research in the near future. Development from hardware perspectives (e.g., battery power, wing-span, payload capabilities, robust decision making abilities, and control aspects) are also a potential avenues for development/research.
In this chapter, we have presented concepts, methods, and future research prospects in the area of path planning (PP) for unmanned aerial vehicles (UAVs). Specifically, we have presented the high-level categorization of the PP approaches based on the availability of information regarding UAV operating environment, and UAV strengths. We have discussed three essential components of the PP approaches that are widely adopted by most of the PP approaches. We have discussed substantial number of performance objectives that are improved/optimized by the PP approaches via new concepts/propositions. Furthermore, we have discussed latest approaches that have been proposed to lower the time complexity of pathfinding and their technical challenges. We have described various PP approaches that are used for the PP in unknown environments (aka local PP). We have briefly described the concepts of coverage path planning (CPP) that is subtopic of the PP. The prospects of future research in the UAVs PP area keeping emerging technologies in the loop have also been discussed. With this concise overview, we aim to provide deep understanding about the PP concepts related to the UAVs, and need of the further developments/research in order to enhance UAVs endurance in the airspace specifically in the urban environments. The contents presented in this chapter can help early researchers to quickly grasp the status of existing developments and potential avenues of the research in this area.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2B5B01002145).
The authors declare no conflict of interest.
“Once one starts to think about the human welfare consequences of economic growth, it is hard to think about anything else” [1]. Economic growth is the basis for increased prosperity, and its importance cannot be overstated. Barro and Sala-i-Martin [2] argue that continuous and sustained economic growth is important for improving the welfare of individuals and that aggregate growth is probably the single most important factor affecting individual levels of income. Due to the importance of economic growth, attainment of high economic growth rates is a major national objective of any country. It is, however, puzzling and at the same time worrisome that the riches of the world are so unequally shared among countries [3].
Over the years, growth performance has varied notably across regions and countries. In some economies, it has experienced major shifts over time. A few developing countries have experienced rapid growth yet some other countries have grown at only a stagnant rate. This discrepancy in economic growth among numerous countries and the dynamics of growth have become provocative research targets. The main questions are why some countries are rich while others are poor, and what determines the rate of growth? Rosa notes that it seems certain that there is no all-encompassing theory of economic growth, but different sources of economic growth can be observed to be relevant for different stages of economic development.
Several reasons have been provided that explain the differences, key among them being the fact that initial conditions differ greatly. Isaksson [4] asserts that some, if not many, of the differences in income per capita are human-created. He asserts that how a society and its production are organized can significantly explain the observed income divergence since the industrial revolution.
In the case of Uganda, the last five decades have been difficult in terms of overall economic growth and stability, let alone the first eight years after independence and the last three decades, when episodes of high yet unstable economic growth occurred, especially from the late 1980s to the late 2000s. Economic growth was impressive for the first eight years after independence, but by 1986, the economy had descended into a deep recession owing to poor governance from the early 1970s to that time. Since 1986, the country has undergone a major transformation from a “failed state” to one of the fastest-growing economies in the world. As early as 1993, Uganda started implementing structural adjustment programmes (SAPS) and other economic policies and programmes such as; economic recovery programme (ERP), medium-term expenditure framework, Plan for Modernization of Agriculture (PMA), and Poverty Eradication Action Plan (PEAP), among others all aimed at poverty reduction and attaining higher levels of economic growth in Uganda. The reforms ushered in relatively high economic growth rates based on incentives for private production. Between 1990 and 2010, GDP growth averaged 7.3 percent per annum, placing Uganda among the fastest-growing economies in the world and creating momentum for take-off. This growth was higher than the Sub-Saharan African growth rate, which averaged approximately 2.1 and was close to that of the East Asian and Pacific region of 7.9 and 6.6 percent, respectively.
To consolidate and accelerate this growth process, the Ugandan government approved the Comprehensive National Development Planning Framework Policy in 2007 which provided the developmental agenda for a 30-year vision to be implemented through three 10-year plans and six 5-year national development plans (NDPs), among other operational plans. However, data shows that Uganda’s growth has been mostly unstable; it has been described as unsustainable because it has been sustained partly by significant aid inflows and only a few tradable commodities, such as coffee, flowers and fish. The government of Uganda, like many other governments elsewhere continues to target improving GDP growth. The key to achieving this improvement has been the careful development and implementation of policies and programmes to improve capital stock, labour stock, price stability and productivity and competitiveness as major drivers of economic growth [5].
Uganda has set its Vision 2040 as a guiding framework for transforming the country from “a peasant to a modern and prosperous country with a per capita income of USD 9,500 from the base figure of USD 506 in the year 2010 by the year 2040”. For the country to achieve this transformation, Uganda Vision 2040 projects that Uganda’s real GDP will have to grow at an average of 8.2 percent, while the IMF forecasts approximately 9 percent growth rate as necessary for the remaining period. However, the achievement of Vision 2040 has been threatened not only by lower-than-targeted rates of annual GDP growth since the inception of the vision but also by a recent slump from the average GDP growth rate of approximately 6.8 percent that was posted in the last half of the 2000s to an average of 4.6 percent between 2010 and 2015.
To achieve the Vision, understanding the determinants of past growth, removing the constraints on present growth and maximizing the prospects for future growth are key. It is important to note that inferring the determinants of growth faces considerable uncertainty due to the existence of multiple overlapping theories that emphasize different channels of growth over time. Therefore, this paper aims at providing more robust and targeted policy interventions to generate higher and more sustainable economic growth by examining the determinants of economic growth in Uganda using the ARDL frameworks.
A wide range of studies have investigated the factors underlying economic growth in different countries. Using differing conceptual and methodological viewpoints, studies have identified different factors that explain economic growth world over [6]. However, existing literature has not yet reached a consensus about a typical set of variables that may affect economic growth.
The accumulation of physical capital (investment) is one of the most fundamental determinants of economic growth identified in the literature per the neoclassical and endogenous growth models and much empirical work has been performed on the subject [7, 8, 9]. It has been found to be robust to most specifications and sample size changes [10]. The impact of several types of investment has been studied over time and varying levels of significance have been attached to varying types of investment. Gross capital formation affects economic growth by either directly increasing the physical capital stock in the domestic economy [11] or indirectly promoting technology [12]. Other researchers have investigated the impact of private and public investment on economic growth and have found significant variations. Khan and Kumar [13] found private investment to be more productive than public investment. In this paper, investment is represented by physical capital accumulation and it is expected to have a positive and statistically significant relationship with economic growth.
Exports are another factor identified by both the neoclassical and endogenous growth models in explaining economic growth variations. Awokuse [14] notes that linking exports to economic growth is pied when he found that there is a flow of Granger cause from real exports to real GDP. There is also a strand of studies that find no conclusive evidence of the causal relationship between exports and GDP growth. Ruiz-Nápoles [15] argues that even in cases where increasing exports has a positive effect on production expansion, such an effect may be limited and offset by increasing manufacturing imports displacing domestic production. Fouad Abou-Stait [16] found that time series studies find fewer conclusive associations between exports and growth, whereas cross-sectional studies appear to support the positive relationship.
Closely related to exports is trade openness with mixed results. A large part of the literature find that economies that are more open grow more rapidly [17, 18, 19, 20]. Baliamoune [21] finds that trade openness is closely associated with positive effects in higher-income and negative effects in lower-income African countries. Arezki and Gylfason [22] find that trade openness has a positive and statistically significant impact on non-resource GDP growth. Several scholars have however criticized the robustness of these findings, especially on methodological and measurement grounds (see, [23, 24]). Vamvakidis [24] and Wong [25] find a negative relationship between openness to international trade and economic growth. Fowe finds no significant effect of openness to trade on economic growth in SSA.
Several endogenous growth models and extensions of the neoclassical growth model find human capital and/or knowledge to be a major source of growth [26]. A large number of studies find evidence suggesting that an educated population is a key determinant of economic growth (see [8, 27, 28]). However, other scholars find mixed results while others question studies that have found a positive relationships [29, 30]. Some empirical findings have shown that human capital accumulation plays only a small role in economic growth [31]. Studies by Bils and Klenow [32], Pritchett [29], Easterly and Levine [33] found that the evidence was weak, absent or even pointed to a negative impact.
Population growth rate is another important variable in economic growth literature. The relationship between population and economic growth is mixed and varies between countries [34]. Some empirical studies have found a negative relationship between population and economic growth [35, 36]; and in others there was a positive association with economic growth [37, 38]. Another factor influencing economic growth is population growth rate [36, 39, 40]. High population growth, for example, could have a negative impact on economic growth, influencing the dependency ratio, investment and saving behavior and quality of human capital countries [41]. However, the findings are again inconclusive since there some studies have reported no (strong) correlation between economic growth and demographic trends (e.g., [29, 42]).
Foreign Aid has received renewed political interest in economic growth discourse resulting into numerous studies. There is however little evidence of a significant positive effect of aid on the long-term growth of poor countries [43, 44]. Andersson and Karlsson, C. [45] finds support for the basic idea that an increase in aid flows strengthens economic growth in poor countries when the policy environment is conducive. Collier and Dehn [46] find that well-timed aid alleviates effects of negative export shocks while Collier and Hoeffler [47] find that aid works particularly well in good policy environments a few years after a conflict has ended. Other scholars argue that aid spurs economic growth unconditionally (see, [37, 48]), or in certain macroeconomic environments that it is growth-neutral [49]. In contrast, some studies have argued that aid has historically been ineffective in promoting growth [50, 51]. Rajan and Subramanian [43] provide evidence that total aid is ineffective at promoting growth.
The relationship between government consumption expenditure and economic growth has attracted a great deal of interest among policymakers and economists. Empirical work on this subject has also provided mixed results. On one side, there are Keynesian economists who consider consumption expenditure as a dependable function of income and on the other side there are substantial numbers of economists who believe that higher consumption can stimulate economic growth [52, 53]. Other studies have found that small to moderate government sizes are positively associated with economic growth while large government sizes impede economic growth [54, 55].
It is argued that inflation is a good macroeconomic indicator of how the government manages the economy [55, 56, 57]. Although the empirical evidence has strongly supported a negative relationship between inflation and growth, especially through the impact of inflation on capital intensity [58], other studies have found that inflation exhibits threshold effects on economic growth [59, 60]. Khan and Senhadji, [60] explore this issue and reach several conclusions. In particular, medium and high inflation hamper economic growth due to the adverse impact on the efficient distribution of resources by changing relative prices [57].
Based on the foregoing literature, we assume a Cobb–Douglas production function with labour-augmenting (Harrod-Neutral) technological progress following Mankiw, et al. [27] and Acikgoz and Mert [61].
where
where HC is human capital, and all other variables are defined as before. Following Mankiw, et al. [27], Acikgoz and Mert [61], and Chirwa and Odhiambo [17], the aggregate Cobb–Douglas production function is assumed to take the following form:
where α and β represent the partial elasticity of output with respect to physical capital and human capital respectively. Per the literature, technological progress (
Several efficiency variables have been identified in the literature to provide a link to how policy variables influence the aggregate production function [55, 57]. The variables selected for this study consist of the accumulation of physical capital (investment); human capital (total school enrolment); population; and policy variables (efficiency factors) that include government consumption share in GDP, inflation, foreign aid as a share of GDP and international trade. The efficiency factors, similar to population growth, are assumed to grow exogenously (see [27, 62]).
Auto Regressive Distributed Lag (ARDL) bounds testing approach developed by Pesaran and Shin [63], Pesaran, Shin and Smith [64] was employed. The modeling approach allows us to capture the short and long run dynamics as well as the speed of adjustment between the independent variables and the dependent variable. The embedded Error Correlation Model (ECM) is a restricted representation that has cointegration restrictions built into the specification so that it is designed for use with non-stationary series that are known to be cointegrated. The ECM specification restricts the long run behavior of the endogenous variables to converge to their cointegrating relationships while allowing a wide range of short run dynamics. Choice of the ARDL model was taken based on the following reasons: (1) the variables were found to be integrated of different orders i.e. (I(0) and I(1) and ARDL can be applied even when variables are not integrated of the same order; (2) ARDL performs better than other co-integration tests in small and finite data samples [65]. The two stage ARDL approach effectively corrects for any possible endogeneity in the regressors [61, 66]; (3) According to [67, 68] the ARDL model also allows for different optimal lags among the different variables to capture the data-generating process as a general-to-specific modeling framework [68, 69], (4) ARDL is known to have information about the structural break in time series data and lastly Pesaran and Shin [63] contented that appropriate modification of the orders of the ARDL model is sufficient to simultaneously correct for residual serial correlation and the problem of endogenous variables. The only drawback being that ARDI approach collapses when variables are integrated of order two (i.e I(2)).
The ARDL representation of the empirical model for this study is expressed as follows:
where
Two steps are involved in estimating an ARDL model. First, the long-run equilibrium relationship between the variables is tested using the upper and lower bounds; then, the short-run and long-run causalities are estimated. The ARDL bounds test is based mainly on the joint F-statistic in which its asymptotic distribution is non-standard under the null hypothesis of no co-integration [70].
In Eq. 4 above, the null hypothesis of no co-integration relationship, defined as
Using the Wald test, the computed F-statistic is then compared with the lower and upper asymptotic critical bounds values, as reported in Pesaran et al. [64]. The lower-bound critical value assumes that all the regressors are l(0), while the upper-bound critical value assumes that they are I(l). We reject the null hypothesis of no co-integration if the computed test statistic exceeds the upper-bound critical value, and we do not reject the null hypothesis if the F-statistic is lower than the lower-bound critical value. The test is, however, inconclusive if the computed F-statistic lies between the lower-bound and upper-bound critical values. In this context, unit root tests should be conducted to ascertain the order of integration of the variables. If all the variables are found to be I(1), then the decision is made on the basis of the upper-bound critical value. On the other hand, if all the variables are I(0), then the decision is based on the lower-bound critical value. To test for the long-run relationship between the variables, we exclude the lagged-level variables from Eq. (4). Once the presence of co-integration is confirmed, we estimate the long-run coefficients of the growth model and the associated ARDL of the ECM for the short-run coefficients.
The ARDL method estimates (p + 1)k number of regressions to obtain the optimal lags for each variable, where p is the maximum number of lags to be used and k is the number of variables in the equation [71]. The model is selected based on the Schwartz-Bayesian criterion (SBC) or the Akaike information criterion (AIC).
ARDL estimation provides both the short run (model) and long run estimation results. The ECM is specified as follows:
where
Annual time series data for the period 1982–2015, that was obtained from World Bank Development Indicators [73, 74] was used in this study. The following variables were used: Real GDP (expressed in 2010 U.S. dollars.) at purchaser’s prices; Investment (proxied by gross fixed capital formation as a share of GDP); Inflation (measured by the consumer price index); General government expenditure as a share in GDP (General government expenditure as a share in GDP); Human Capital (representing knowledge spill over effects) was proxied by Human capital index3, based on years of schooling and returns to education); Demography (proxied by total population); Trade openness (measured by the sum of exports and imports as a proportion of GDP); and Foreign Aid as a proportion to GDP (measured by net official development assistance and official aid received as a share of real GDP). Eviews 9.5 software was used to conduct the empirical analysis.
Unit roots/stationarity tests were conducted because this is a prime requirement for any co-integration and causality tests. The augmented Dickey-Fuller test (ADF: [75]) was used to establish the order of integration. The ADF test results were augmented with the Phillips-Perron (PP: [76]) test. Table 1 presents the results of the unit root tests.
Variables | Augmented Dickey Fuller | Phillips-Perron test |
---|---|---|
Constant + trend | Constant + trend | |
Log Real Gross Domestic Product | −3.48* | −3.48* |
Log Trade Openness | −2.81 | −2.62 |
Log Human Capital | −2.84 | −2.26 |
Log Population Growth | −0.06 | −1.72 |
Log Gross Government Final Consumption (% GDP) | −1.77 | −1.71 |
Log Gross Fixed Capital Formation | −3.95** | −2.89 |
Log Inflation Rate | −5.28*** | −2.22 |
Log Aid | −2.24 | −2.18 |
Log Total School Enrolment, Primary | −1.85 | −1.74 |
Log Imports | 3.41* | −2.43 |
Log Exports | −2.86 | −3.21 |
Log Real Gross Domestic Product | −3.38* | −3.38* |
Log Trade Openness | −4.51*** | −4.37*** |
Log Human Capital | −3.80 | −3.85* |
Log Population Growth | −3.80** | −2.35 |
Log Gross Government Final Consumption (percent) GDP) | −4.92*** | −6.02*** |
Log Gross Fixed Capital Formation | −4.33*** | −5.24*** |
Log Inflation Rate | −2.05 | −2.30 |
Log Aid | −6.41*** | −7.56*** |
Log Total School Enrolment, Primary | −6.91*** | 7.08*** |
Log Imports | −3.77** | −3.40* |
Log Exports | −6.49*** | −6.49*** |
Unit root test at level and in first difference.
***, **, and * denote rejection of the null hypothesis of unit root at the 1 percent, 5 percent and 10 percent significance levels, respectively.
The ADF test results with trend and intercept at level in part A indicate that GDP, GGC, and GFCF were stationary at the 5 percent level of significance, whereas CPI was stationary at the 1 percent level of significance. The researcher thus carried out stationarity tests for all series in first difference with constant and trend, as indicated in part B (ADF test), and the variables, except CPI, became stationary.
The variables were also tested for stationarity using the Phillips-Perron test. The PP test results at level with constant and trend were found to be non-stationary except for GDP and GGC, which were found to be stationary at 10 percent and 5 percent levels of significance, respectively. The variables were tested for stationarity in first difference, and they all became stationary except CPI.
We tested for co-integration among the variables to establish whether they had a long-run relationship. From a statistical point of view, a long-run relationship implies that variables move together over time and that short-term disturbances arising from the long-term trend are corrected. Co-integration is necessary because a valid ARDL requires the presence of a co-integrating set of variables. The ARDL method allows us to test both short- and long-run relationships between the dependent and independent variables in a multivariate framework. The critical value bounds are computed by stochastic simulations using 20,000 replications [66].
The variables are jointly tested if they are equal to zero. That is:
H0: They are jointly equal to zero.
H1: They are not jointly equal to zero.
Once the test statistic is computed, it is compared to two asymptotic critical values corresponding to polar cases of all variables being purely I(0) or purely I(1). When the test statistic is below the lower-bound critical value, the null hypothesis is not rejected, and co-integration is not possible. In contrast, when the test statistic is above the upper-bound critical value, the null hypothesis is rejected, and co-integration is indeed possible. Alternatively, should the test statistic fall between the lower-bound and upper-bound critical values, the test results are inconclusive, and knowledge of the co-integration rank is required to proceed further.
The Akaike information criterion was employed to determine the appropriate lag length for the estimated ARDL equation. This method was chosen because it tends to over-fit the model of interest, given that the optimal lag length for the growth model is up to 2 lags. The optimal lag length is chosen based on the number of dynamic regressors included in the model and the sample size. The optimal lag-length selection criteria are based on the lowest AIC obtained. For this growth equation, (regression I), the optimal ARDL model selected was the ARDL (2, 1, 0, 1, 0, 1, 0, 0, 2) model with restricted intercept and trend, while for regression II, the optimal ARDL model selected was the ARDL (2, 0, 2, 0, 1, 0, 0, 1, 0, 2) model with restricted intercept and trend. Table 2 reports the Pesaran et al. [64] bounds test for level relationships for the selected equation.
ARDL bounds test | Regression I | Regression II | ||
---|---|---|---|---|
Included observations: 32 after adjustments | ||||
Null hypothesis: no long-run relationships exist | ||||
Test statistic | Value | k | Value | K |
F-statistic | 5.47*** | 8 | 3.909** | 9 |
Critical Value Bounds | ||||
Significance | I0 Bound | I1 Bound | I0 Bound | I1 Bound |
10 percent | 1.95 | 3.06 | 1.88 | 2.99 |
5 percent | 2.22 | 3.39 | 2.14 | 3.3 |
2.5 percent | 2.48 | 3.7 | 2.37 | 3.6 |
1 percent | 2.79 | 4.1 | 2.65 | 3.97 |
R-squared | 0.845152 | 0.852168 | ||
Adjusted R-squared | 0.699981 | 0.672657 |
Results of ARDL bounds test for co-integration.
***, **, and * denote 1 percent, 5 percent and 10 percent significance levels, respectively.
As illustrated in Table 2, regression I, the computed 𝐹-statistic is 5.47, and it is statistically significant at the 1 percent upper-bound critical value, meaning that the null hypothesis of no co-integration is rejected at the 1 percent significance level. In regression II, the computed 𝐹-statistic is 3.909, and it is statistically significant at the 5 percent upper-bound critical value, meaning that the null hypothesis of no co-integration is rejected at the 5 percent significance level. In summary, the bounds test of co-integration relationships using the Pesaran et al. [64] approach confirms the existence of long-run level relationships between the dependent variable and the set of covariates in both regressions. The study results also reveal that the underlying ARDL model is a good fit, represented by an estimated
Using the ARDL model, the researcher selected the overall best model from the 20 best selected ARDL models. As shown in Figure 1, the selected model in regression I is ARDL (2, 1, 0, 1, 0, 0, 1, 0, 0, 2), and the selected model in the second regression is ARDL (2, 0, 2, 0, 1, 0, 0, 1, 0, 2). These two models were significantly superior to the second-best models in each case [66].
Relative superiority of the selected models.
The short and long run elasticities for the ARDL model were estimated. Table 3, part A presents the short-run ARDL results (including the ECM representation), while part B pre-sets the long-run results of the ARDL models.
ARDL co-integration and long-run form | ||
---|---|---|
Dependent variable: LRGDP | ||
Selected Model: Included observations: 32 | ARDL (2,1,0,1,0,1,0,0,2) | ARDL (2,0,2,0,1,0,0,1,0,2) |
Co-integration Form | Regression I | Regression II |
Variable | Coefficient(Prob.) | Coefficient (Prob.) |
D(Log Real Gross Domestic Product (−1)) | 0.179(0.049)** | 0.279(0.003)*** |
D(Log Trade Openness) | 0.036(0.190) | |
D(Log Population Growth) | 0.481(0.784) | 0.126(0.941) |
D(Log Inflation Rate) | −0.085(0.000)** | −0.086(0.000)* |
D(Log Human Capital) | 0.240(0.507) | 0.619(0.109) |
D(Log Gross Government Consumption) | 0.061(0.001)*** | 0.066(0.000)*** |
D(Log Gross Fixed Capital Formation) | 0.192(0.000)*** | 0.145(0.000)*** |
D(Log Aid) | −0.033(0.022)** | −0.021(0.111) |
D(Log Exports) | 0.027(0.047)** | |
D(Log Imports) | 0.056(0.178) | |
D(Dummy for Structural Adjustment) | −0.008(0.092)* | −0.018(0.007)*** |
C | 0.185(0.001)*** | 2.786(0.000)*** |
Coint Eq (−1) | −0.595(0.00)*** | −0.646 (0.000)*** |
Log Trade Openness | 0.295(0.002)*** | |
Log Population Growth | 1.008(0.090)* | 0.334(0.605) |
Log Inflation Rate | −0.009(0.624) | −0.018(0.361) |
Log Human Capital | 0.293(0.639) | 0.848(0.244) |
Log Gross Government Consumption (percent, GDP) | 0.198(0.001)*** | 0.159(0.007)*** |
Log Gross Fixed Capital Formation | 0.316(0.003)*** | 0.220(0.029)** |
Log Aid | −0.053(0.227) | −0.032(0.399) |
Log Exports | 0.121(0.007)*** | |
Log Imports | 0.090(0.244) | |
D(Dummy for Structural Adjustment) | −0.007(0.003)*** | −0.071(0.003)*** |
Short-run and long-run ARDL results.
***, **, and * denote 1 percent, 5 percent and 10 percent significance levels, respectively.
Part A of Table 3 reports the estimated short-run coefficients, while Part B reports the estimated long-run coefficients. Two different regressions were estimated. Regression I was the “benchmark” regression, while regression II was used for sensitivity/options analysis. Among other variables, regression II used a different proxy for trade openness, which is a fundamental variable for GDP growth, according to the literature. Specifically, instead of using trade openness, we used exports and imports to examine the effect of trade on GDP growth.
As shown in part A, the short-run dynamics and the adjustment towards the long-run equilibrium path are measured by the error correction term (ECT) [77]. In the short run, deviations from the long-run equilibrium can occur due to shocks in any of the variables in the model; thus, all the short-run coefficients show the dynamic adjustments of all variables to their long-run equilibrium [70]. If the coefficient is significant, it implies that past equilibrium errors play a role in determining the outcomes of the current period. The ECT measures the speed of adjustment to restore equilibrium in the dynamic model after a disturbance. For the coefficient to be significant, it is required that the error correction term (ECT) must be negative and significant. A highly significant ECT is further proof of a stable long-run relationship [78].
From Table 3, part A, regression I, the ECT estimation results show that the estimated coefficient of the error correction term has the expected sign (negative) and is statistically significant. This reinforces the finding of a long-run relationship in the co-integration equation. The results show that a 1 percent deviation from the equilibrium path is corrected in the next period at a rate of 59.5 percent and is statistically significant at the 1 percent significance level. This confirms the presence of a long-run level equilibrium path between real GDP and the selected regressors (trade openness, human capital, population, government consumption, investment, inflation, foreign aid and a policy dummy (structural adjustment programme). The regression results for the ARDL model reveal a good fit represented by an estimated
Part B, regression I of Table 3 presents the long-run coefficient estimates. The results reveal that the key macroeconomic determinants that are significantly associated with long-run economic growth in Uganda include trade openness, population growth, government consumption, investment, and the policy dummy variable for the structural adjustment programmes (SAPs).
In the long run, the relationship between trade openness and real GDP is positive and statistically significant at the 1 percent significance level. The results reveal that a 1 percent increase in trade openness in the long run leads to a 0.295 percent increase in the level of real GDP. These findings are supported by previous studies that have found a positive and significant relationship between trade openness and economic growth (e.g. [17, 19, 20]).
The study reveals that population growth is positively and significantly associated with the growth of real GDP in Uganda at the 10 percent level of significance. It shows that a 1 percent increase in population leads to a 1.01 percent increase in real GDP. These results are supported by similar studies conducted in developing countries that have found a positive relationship between investment and economic growth in the long run (e.g., [41, 79]).
The study reveals a positive relationship between government consumption and the growth of real GDP at the 1 percent significance level in the long run. A 1 percent increase in government consumption results in a 0.20 percent increase in the level of real GDP. These results are supported by similar studies conducted in developing countries that have found a positive relationship between government consumption and economic growth in the long run (e.g., [17]).
The results confirm the widely established empirical estimation finding that investment and growth in GDP have a positive relationship. A 1 percent increase in the level of investment results in a 0.32 percent increase in the level of real GDP. These results are supported by similar studies conducted in developing countries that have found a positive relationship between investment and economic growth in the long run (e.g., [10, 54, 80, 81, 82]).
The study results did not reveal a significant association between inflation, human capital and foreign aid and the long-run level of GDP growth.
The short-run results presented in Part A of Table 3 reveal that the key macroeconomic determinants that are significantly associated with the growth of real GDP in the short run are initial GDP, inflation, government consumption (percent of GDP), investment, foreign aid, and the policy dummy. The results show that a 1 percent increase in initial real GDP leads to a 0.18 percent increase in real GDP. Meaning that the level of and sign of initial GDP has a positive relationship with current GDP.
The results reveal a negative association between inflation and economic growth. A 1 percent increase in inflation leads to a 0.90 reduction in GDP. These results are supported by a number of empirical growth studies that have also found a negative association between inflation and economic growth in developing countries (e.g., [56, 57, 83, 84, 85, 86]).
The results show that government consumption is positively and significantly associated with the growth of real GDP at the 1 percent significance level. A 1 percent change in government consumption leads to a 0.06 percent increase in the growth of GDP. The positive relationship found between government consumption and economic growth is supported by similar studies in the empirical growth literature that have found a positive relationship between trade openness and economic growth (e.g., [17, 87]).
There is a positive and significant relationship between investment and economic growth at the 1 percent level of significance. A 1 percent increase in investment leads to a 0.19 percent increase in GDP. The results are consistent with existing empirical growth studies that have found a positive relationship between investment and economic growth (e.g., [10, 17, 88]).
The results show that foreign aid is negatively and significantly associated with the growth of real GDP, and the results are statistically significant at the 5 percent significance level. A 1 percent change in foreign aid leads to a 0.03 percent reduction in the growth of GDP. The negative relationship found between foreign aid and economic growth is supported by similar studies in the empirical growth literature (e.g., [10]).
The study results did not reveal a significant association between trade openness, population growth, human capital, and real GDP growth in the short run.
Sensitivity analysis was carried out to examine the significance of other variables or proxies for the variables used in regression II. This analysis was carried out bearing in mind theory, certain empirical studies and the nature of the Ugandan economy. Key variables/proxies were imports and exports as proxies for trade openness. Exports were found to be positively and significantly associated with GDP at the 1 percent level of significance, while imports were found to be non-significant.
From Table 3, Part A, regression II above, the ECT estimation results show that the estimated coefficient of the error correction term has the expected sign (negative) and is statistically significant. The ECT shows that a 1 percent deviation from the equilibrium path is corrected in the next period at a rate of −0.65 percent and is statistically significant at the 1 percent significance level. This confirms the presence of a long-run level equilibrium path between real GDP and the selected regressors (total school enrolment, primary; real exchange rate; population; government consumption; investment; inflation; foreign aid; imports; and exports). The regression results for the ARDL model reveal a good fit represented by an estimated
Part B, regression II of Table 3 presents the long-run coefficient estimates. The results reveal that the key macroeconomic determinants that are significantly associated with long-run GDP growth in Uganda are government consumption, investment, exports and the policy dummy.
The study reveals a positive relationship between government consumption and real GDP growth at the 1 percent significance level in the long run. A 1 percent increase in government consumption results in a 0.20 percent increase in the level of real GDP. These results are supported by Doppelhofer and Weeks [89] who find a positive relationship between government consumption and economic growth in the long run in developing countries.
The study reveals a positive relationship between investment and real GDP growth at the 1 percent significance level in the long run. A 1 percent increase in the level of investment results in a 0.22 percent increase in the level of real GDP. These results are supported by similar studies conducted in developing countries that have found a positive relationship between investment and economic growth in the long run (e.g., [10, 17, 54, 81, 82]).
There is a positive and significant relationship between GDP and exports in the long run at the 1 percent level of significance. A 1 percent increase in exports leads to a 0.12 percent increase in GDP growth (see [14, 16]).
There is also a negative and significant relationship between GDP and the policy dummy for SAPs in Uganda, as a 1 percent increase in implementation of the SAPs leads to a 0.07 percent reduction in real GDP growth.
The study results did not reveal a significant association between population growth, inflation human capital and foreign aid, imports and GDP growth in the long run.
The short-run results for the sensitivity/option analysis are shown in Part A, regression II of Table 3 above. The key macroeconomic determinants that are significantly associated with the growth of real GDP in the short run are initial GDP, inflation, government consumption (percent, GDP), investment, exports, and the policy dummy in both the current and the previous period.
The results show that a 1 percent increase in initial real GDP leads to a 0.28 percent increase in real GDP.
The results reveal a negative association between inflation and economic growth. A 1 percent increase in inflation leads to a 0.90 percent reduction in GDP. These results are supported by a number of empirical growth studies that have also found a negative association between inflation and economic growth in developing countries (e.g., [55, 56, 57, 83, 84, 85, 86]).
The results show that government consumption is positively and significantly associated with the growth of real GDP at the 1 percent significance level. A 1 percent change in government consumption leads to a 0.07 percent increase in the growth of GDP. The positive relationship found between government consumption and economic growth is supported by similar studies in the empirical growth literature that have found a positive relationship between trade openness and economic growth (e.g., [17]).
There is a positive and significant relationship between investment and economic growth at the 1 percent level of significance. A 1 percent increase in investment leads to a 0.15 percent increase in GDP. The results are consistent with the existing empirical growth studies that have found a positive relationship between investment and economic growth (e.g., [10, 17, 88]).
The results show that exports are positively and significantly associated with the growth of real GDP at the 5 percent significance level. A 1 percent change in exports leads to a 0.03 percent increase in GDP growth. The positive relationship found between exports and GDP growth is supported by similar studies in the empirical growth literature (e.g., [14, 90]).
There was a negative and significant relationship between the implementation of the structural adjustment programmes and GDP growth in the current period. A 1 percent increase in the implementation of the SAPs led to a 0.1 reduction in GDP.
The results indicate that in the short run, policy variables contributed to economic growth more than factor accumulation, while in the long run, a mixture of factor accumulation and policy variables was the major driver of economic growth.
The regressions were tested to ascertain their applicability and robustness. Robustness was confirmed by the Breusch-Godfrey serial correlation LM test, Jarque-Bera normality test, recursive stability tests, and Breusch-Pagan-Godfrey heteroscedasticity test. This means that the model has the desired econometric properties of time series data.
Recursive Tests were done using a visual examination of the graphs of the recursive parameter estimates. Additionally, a formal statistical test to test the null hypothesis of model stability was undertaken using the CUSUM test [91]. Figure 2 regression I and regression II illustrate the CUSUM and CUSUMSQ at the 5 percent significance level.
CUSUM and CUSUMSQ results for the estimated growth equation.
As illustrated in Figure 2, the CUSUM test reveals parameter stability, while the results of the CUSUMQ test reveal variance stability given that the residuals for both tests are within the 5 percent critical lines. According to these tests, our ARDL model is stable and has no serial correlation.
Serial correlation was undertaken to test whether the residual is correlated with its own lagged values using the Breusch-Godfrey LM test for serial correlation, and the results are presented in Table 4 below.
Breusch-Godfrey serial correlation LM test | Regression I | Regression II | ||
---|---|---|---|---|
F-statistic | 1.332638 | Prob. F(3,13) | 0.2739 | 0.0223 |
Obs*R-squared | 7.526409 | Prob. Chi-Square(3) | 0.1921 | 0.0004 |
The Breusch-Godfrey test for serial correlation in the residuals of the regression.
The Breusch-Godfrey serial correlation test statistic for the null hypothesis of no serial correlation (Table 4) for regression I has a probability value of 0.2739, which is greater than 5 percent. Thus, we fail to reject the null hypothesis, which indicates that there is no serial correlation in the residuals.
The Breusch-Pagan-Godfrey tests for heteroscedasticity statistic for the null hypothesis of no heteroscedasticity in regressions I and II have probability values of 0.6996 and 0.0612, respectively, which are greater than 5 percent. Thus, we fail to reject the null hypothesis, which indicates that there is no heteroscedasticity in the residuals (Table 5).
Heteroscedasticity test: Breusch-Pagan-Godfrey | Model I (probability) | Model II | ||
---|---|---|---|---|
F-statistic | 0.760385 | Prob. F(15,16) | 0.6996 | 0.0612 |
Obs*R-squared | 13.31781 | Prob.Chi-Square(15) | 0.5778 | 0.1320 |
Scaled Explained SS | 2.325347 | Prob.Chi-Square(15) | 0.9999 | 0.9945 |
Breusch-Pagan-Godfrey test for heteroscedasticity results.
The ARDL model assumes that the residuals are normally distributed. The Jarque-Bera statistic is assumed to have a
As indicated in Figure 3, in regression I, the probability value for the Jarque-Bera statistic is 0.49 with a probability value of 0.782, which is more than 5 percent; hence, the residuals are normally distributed. In regression II, the probability value for the Jarque-Bera statistic is 0.647 with a probability value of 0.724, which is more than 5 percent; hence, the residuals are normally distributed. This means that statistical tests for inference on regression coefficients are reliable, since these tests require that the dependent variable (and hence the residuals) follows a normal distribution.
Histogram normality test model I.
Specification errors can be errors in the specification of the functional form that the equation should take in describing the relationship between the variable. If the F test statistic is greater than the F critical value, we reject the null hypothesis that the true specification is greater than the F critical value, hence reject the null hypothesis that the true specification is linear (which implies that the true specification is non-linear). If we are unable to reject the null, then the results suggest that the true specification is linear and the equation passes the Ramsey Reset test (Table 6).
Ramsey RESET Test | |||
---|---|---|---|
Equation: UNTITLED | |||
Specification: LRGDP LRGDP(−1) LTRO LTRO(−1) LPOPN LINF LINF(−1) LHC LHC(−1) LHC(−2) LGGC LGGC(−1) LGFCF LAID C | |||
Omitted variables: squares of fitted values | |||
Value | df | Probability | |
t-statistic | 0.326505 | 17 | 0.7481 |
F-statistic | 0.106606 | (1, 17) | 0.7480 |
Ramsey rest test for the functional form test results.
The probability values from the Ramsey rest test for the T and F statistics are greater than 0.05 level of significance, meaning that the estimated model is free from specification errors.
Attaining high and sustainable economic growth is a major policy objective for any country especially among developing countries. In this paper, we examined the macroeconomic determinants of economic growth in Uganda using the factor accumulation framework for the period 1982–2015.
The autoregressive distributed lag (ARDL) approach to co-integration was used to estimate both the short- and long-run elasticities of the selected macroeconomic determinants. The ARDL bounds testing approach to co-integration in the benchmark regression indicated that the key determinants that are positively associated with growth in GDP in the short run are the initial level of real GDP growth, government consumption and investment, while foreign aid, inflation and a dummy for SAPs were negatively and significantly associated with real GDP growth. The results failed to show that trade openness, population growth and human capital accumulation were significantly associated with real GDP growth in the short run [95, 96, 97, 98].
The study revealed that in the long run, trade openness, population growth and government consumption and investment were positively and significantly associated with GDP growth, while the policy dummy on SAPs was negatively and significantly associated with GDP. In the long run, the study failed to show that inflation, human capital and foreign aid were significantly associated with GDP growth. It can be concluded that in the short run, policy variables contributed to economic growth more than factor accumulation (physical and human capital), while in the long run, a mixture of both factor accumulation and policy variables was the major driver of economic growth.
The study results have significant policy implications for Uganda. They show that investment and population have are significantly associated with economic growth both in the short and long run. Thus, it is recommended that the economic strategies to be adopted should include those that create incentives to attract investment—with an emphasis on the adoption of labour–intensive technologies, on quality–based human capital development. In the short run trade openness, government consumption, foreign aid and inflation are positively and significantly associated with economic growth meaning that the country should pursue policies that enhance trade, government effectiveness, aid effectiveness and economic management.
The study found that the key determinants that were positively associated with growth in GDP in the short run were the initial level of GDP growth, government consumption, investment and a dummy for SAPs, while foreign aid and inflation were negatively associated with GDP growth. The results failed to show that trade openness, population growth and human capital accumulation were significantly associated with GDP growth in the short run. In the long run, the study revealed that trade openness, population growth, government consumption and investment were positively associated with GDP, while the policy dummy on SAPs was negatively associated with GDP growth. In the long run, the study failed to show that inflation, human capital and foreign aid were significantly associated with growth in GDP.
These results have significant policy implications for Uganda, both in the short and long run. In the short run it is recommended that economic strategies that would spur accumulation of physical capital/Investment, increase government consumption, improve price stability be pursued while in the long run, strategies that improve trade openness, population growth, government consumption and investment should be pursued.
We would like to acknowledge Prof John Dumba Ssentamu and Associate Professor Eria Hisali, for their insurmountable technical contribution to this paper through their reviews and comments.
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The incorporation of legumes in diets, especially in developing countries, could play a major role in eradicating protein-energy malnutrition especially in developing Afro-Asian countries. Legumes could be a base for the development of many functional foods to promote human health.",book:{id:"5963",slug:"functional-food-improve-health-through-adequate-food",title:"Functional Food",fullTitle:"Functional Food - Improve Health through Adequate Food"},signatures:"Yvonne Maphosa and Victoria A. 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The main global health organizations have incorporated patient safety in their review of work practices. The data provided by the medical laboratories have a direct impact on patient safety and a fault in any of processes such as strategic, operational and support, could affect it. To provide appreciate and reliable data to the physicians, it is important to emphasize the need to design risk management plan in the laboratory. Failure Mode and Effect Analysis (FMEA) is an efficient technique for error detection and reduction. Technical Committee of the International Organization for Standardization (ISO) licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. FMEA model helps to identify quality failures, their effects and risks with their reduction/elimination, which depends on severity, probability and detection. 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Leadership in health services is important for following innovations and adapting to current situations. Nurses working together with other health personnel in hospitals providing health services constitute an important group in leadership. Nursing, which is a key force for patient safety and safe care, is a human-centered profession, and therefore leadership is a key skill for nurses at all levels. The leadership styles of nurse managers are believed to be an important determinant of job satisfaction and persistence of nurses. The need for nurses with leadership skills and the need for nurses to develop their leadership skills are increasing day by day. There are several leadership styles defined in nursing literature. These leadership styles are examined under the titles of relational leadership style, transformational leadership, resonant leadership, emotional intelligence leadership, and participatory leadership. The task-focused leadership style is explored under the headings of transactional and autocratic leadership, laissez-faire leadership, and instrumental leadership.",book:{id:"9047",slug:"nursing-new-perspectives",title:"Nursing",fullTitle:"Nursing - New Perspectives"},signatures:"Serpil Çelik Durmuş and Kamile Kırca",authors:null},{id:"58916",title:"Factors Affecting the Attitudes of Women toward Family Planning",slug:"factors-affecting-the-attitudes-of-women-toward-family-planning",totalDownloads:8485,totalCrossrefCites:9,totalDimensionsCites:18,abstract:"Everyone has the right to decide on the number and timing of children without discrimination, violence and oppression, to have the necessary information and facilities for it, to access sexual and reproductive health services at the highest standard. Deficient or incorrect family planning methods, wrong attitudes and behaviors toward the methods and consequent unplanned pregnancies, increased maternal and infant mortality rates are the main health problems in most countries. Individuals’ learning modern family planning methods and having positive attitude for these methods may increase the usage of these methods and contributes the formation of healthy communities. 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Over periods of time, some of these norms become standards that all members of the community are expected to adhere to. Deviance from these standards is seen as absurd, wrong, or frankly abnormal. However, many of these cultural mores have no scientific basis and, some of them actually promote behaviors with negative health consequences. 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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. 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. 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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. 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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 Principal Investigator and Scientist 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 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 machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, 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. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. from Integral University, Lucknow, India, with his work titled ‘Development and evaluation of silymarin nanoformulation for hepatic carcinoma’. Currently, he is an Assistant Professor of Pharmaceutics, at the Faculty of Pharmacy, Integral University. He has been teaching PharmD, BPharm, and MPharm students and conducting research in the novel drug delivery domain. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than twenty-four original journal articles, two edited books, four book chapters, and several scientific articles to his credit. He is a member of the American Association for Cancer Research, the International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"333824",title:"Dr.",name:"Ahmad Farouk",middleName:null,surname:"Musa",slug:"ahmad-farouk-musa",fullName:"Ahmad Farouk Musa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333824/images/22684_n.jpg",biography:"Dato’ Dr Ahmad Farouk Musa\nMD, MMED (Surgery) (Mal), Fellowship in Cardiothoracic Surgery (Monash Health, Aust), Graduate Certificate in Higher Education (Aust), Academy of Medicine (Mal)\n\n\n\nDato’ Dr Ahmad Farouk Musa obtained his Doctor of Medicine from USM in 1992. He then obtained his Master of Medicine in Surgery from the same university in the year 2000 before subspecialising in Cardiothoracic Surgery at Institut Jantung Negara (IJN), Kuala Lumpur from 2002 until 2005. He then completed his Fellowship in Cardiothoracic Surgery at Monash Health, Melbourne, Australia in 2008. He has served in the Malaysian army as a Medical Officer with the rank of Captain upon completing his Internship before joining USM as a trainee lecturer. He is now serving as an academic and researcher at Monash University Malaysia. He is a life-member of the Malaysian Association of Thoracic & Cardiovascular Surgery (MATCVS) and a committee member of the MATCVS Database. He is also a life-member of the College of Surgeons, Academy of Medicine of Malaysia; a life-member of Malaysian Medical Association (MMA), and a life-member of Islamic Medical Association of Malaysia (IMAM). Recently he was appointed as an Interim Chairperson of Examination & Assessment Subcommittee of the UiTM-IJN Cardiothoracic Surgery Postgraduate Program. As an academic, he has published numerous research papers and book chapters. He has also been appointed to review many scientific manuscripts by established journals such as the British Medical Journal (BMJ). He has presented his research works at numerous local and international conferences such as the European Association for Cardiothoracic Surgery (EACTS) and the European Society of Cardiovascular Surgery (ESCVS), to name a few. He has also won many awards for his research presentations at meetings and conferences like the prestigious International Invention, Innovation & Technology Exhibition (ITEX); Design, Research and Innovation Exhibition, the National Conference on Medical Sciences and the Annual Scientific Meetings of the Malaysian Association for Thoracic and Cardiovascular Surgery. He was awarded the Darjah Setia Pangkuan Negeri (DSPN) by the Governor of Penang in July, 2015.",institutionString:null,institution:{name:"Monash University Malaysia",country:{name:"Malaysia"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. Currently, he is a full professor at Central South University and Shandong First Medical University, and an advisor to MS/PhD students and postdoctoral fellows. He is also a fellow of the Royal Society of Medicine and European Association for Predictive Preventive Personalized Medicine (EPMA), a national representative of EPMA, and a member of the American Society of Clinical Oncology (ASCO) and the American Association for the Advancement of Sciences (AAAS). He is also the editor in chief of International Journal of Chronic Diseases & Therapy, an associate editor of EPMA Journal, Frontiers in Endocrinology, and BMC Medical Genomics, and a guest editor of Mass Spectrometry Reviews, Frontiers in Endocrinology, EPMA Journal, and Oxidative Medicine and Cellular Longevity. He has published more than 148 articles, 28 book chapters, 6 books, and 2 US patents in the field of clinical proteomics and biomarkers.",institutionString:"Shandong First Medical University",institution:{name:"Affiliated Hospital of Shandong Academy of Medical Sciences",country:{name:"China"}}}]}},subseries:{item:{id:"4",type:"subseries",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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