Jumping results: Real and obtained by simulation modeling.
\r\n\tAn update on clinical manifestations, their assessment, monitoring, and imagiology, including peripheral arthritis, enthesopathy, and extra-articular findings, and, the differential diagnosis with other diseases which evolves with axial and peripheral calcifications will be provided.
\r\n\r\n\t
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
At present, having taken a systemic-structural approach, general biomechanics studies feature of the locomotor system of a man, biomechanical characteristics of movements, composition, and structure of motions in sports exercises and movements.
This approach suggests to single out spatial and temporal elements in a system of movements [1]. Just here, we see a contradiction, as a material system cannot be subdivided into spatial and temporal elements. From the point of view of contemporary philosophy of scientific cognition, it is incorrect to think of processes, properties, or relations as being systems. They all are no more than manifestations of various properties of a material object, while a system is a model of an original material object, the latter also consisting of material elements.
An approach used by J.G. Hay [2] is the most recognized in sport biomechanics now. Its essence (illustrated by a vertical standing jump) consists in subdivision of the trajectory of the body center of gravity (COG) into a few segments. The following identification of biomechanical characteristics responsible for the COG displacement and velocity of displacement is based on common sense. For example, arm lift-up shifts the COG upwards; legs extension produces the same effect; legs length ensures a certain position of the body COG at the end of take-off. On the ground of common sense, important parameters are selected and subjected to correlation analysis in order to find their relationship and to obtain multiple regression equations. The logic of this approach is similar to that of an empirical research aimed at formulation of an empirical law, which does not reveal the essence of a phenomenon, although enables to make some suggestions.
In biomechanics, we may be unfamiliar with brain organization and the central nervous system (CNS) can be considered as a “black box.” Here lies the boundary between physiology and biomechanics. A biomechanist must be proficient in programming deliberate motor actions aimed at reaching a preset goal, i.e. motor programs. Physiological concepts of movement control do not substantiate the laws of mastering motor actions. However, to achieve success, a coach needs knowledge both in biomechanics and physiology in order to create training programs aimed at the development of motor programs of a competitive movement in an athlete. Thus, to work out a plan of technique development of a track-and-field athlete, it appears necessary to model a competitive movement, as well as training means to be used besides the principle movement.
To model the locomotor system of a man, we must use ideal models from theoretical mechanics [3]. Theoretical mechanics use models including the following elements: two- or three-dimensional space, time, point mass, perfectly rigid body (a rod), hinge, kinematic chain, ideal liquid or gas, etc. [4]. All these models are used in biomechanics, although to create an adequate model of a locomotor system, a model of muscle is needed. Hence, the subject of biomechanics matches that of theoretical mechanics only partially.
At every single instant, human existence can be considered as a combination of biomechanisms. In general, the concept of biomechanism includes biochemical objects (mitochondria, myofibrils, etc.), physiological systems (cardiovascular, endocrine, immune, central nervous, and other systems), and, the locomotor system.
In biomechanics, this concept should be referred to mechanics, in particular, to the theory of machines and mechanisms. Let us define a biomechanism (BM) as an aggregate of certain body parts movements, independent of other parts movements, transforming one type of energy into another that leads to changes in the position and speed of the athlete’s body COG while accomplishing a certain movement task in certain external conditions [5, 6, 7].
To control a multilink system, the CNS combines separate links into subsystems (key kinematic mechanisms), which can act independently, although in doing so to pursue a common goal. A biomechanism as an integral system consists of a set of components, each possessing its own properties, which can manifest themselves in human movements in different ways.
The following components are singled out in a biomechanism:
Muscle as:
a converter of chemical energy into mechanical one;
a resilient element, capable of storing and returning energy;
a ductile element, capable of damping external loads;
an energy (power) transmitter from energy sources.
Bone as:
a lever for force and power transfer;
a pendulum for energy conversion;
a rod for support and reaction against external loads.
Joint as:
a hinge joining bones in a kinematic chain;
a hinge restricting mobility of joined bones.
Besides that, we should take into account controlling units containing control programs (motor programs that are formed, stored, and functioning in the athlete’s CNS) [8, 9].
It is noteworthy that a CNS model must meet strict requirements. The model must reflect the process of controlling the object (in our case, the locomotor system) as well as model environment conditions and their relationship. Important is for both processes to be modeled as parallel [10]. The ability to perform deliberate movements means that a person can control target-oriented movements of the body or its parts more or less precisely. As the purpose of a movement is supposed to be solved by a certain BM and perceived by consciousness, it can be controlled and changed deliberately. The problem of movement spatial & temporal parameters differentiation, i.e. a method of performing a motor action, or, in other words, technique of a movement is, probably, solved by means of conscious control of certain BMs [6].
Hypothesis.
Our investigation aims at confirmation of the hypothesis that the concept of bimechanism (BM) forms the basis of the approach to selecting technique development means in track-and-field. We examined one of the most important components of track-and-field jumps, i.e. a take-off.
The following BMs can be identified in the take-off in track-and-field jumps:
BM of the support leg and body extension (LBE);
BM of the arms and swinging leg swinging motion (SM);
BM of the “overturned pendulum” (OP);
In the publication [11] the authors underscore three factors that play a key role in the biomechanism of the support leg and body extension. They are:
consecutive extension of the coxofemoral and knee joints;
differently directed changing angles in the coxofemoral and knee joints in the transition phase from shock absorption to take-off;
optimal legs bending in the knee joints.
Swinging motions contribution. This mechanism increases the vertical component of the COG velocity after take-off [12]. It ensures:
growth of the support reaction force due to the accelerated motion of the swinging links [13];
growth of the swinging links velocity till the start of the knee joint extension [14];
correct position of the swinging links at the end of take-off [13].
The essence of the “overturned pendulum” biomechanism consists in the ability to increase the COG vertical velocity due to the athlete’s body pivoting over the point of bearing [15, 16].
Relatively independent kinematic mechanisms are interdependent at dynamic level, i.e. realization of any of them affects the efficiency of the others. The role and contribution of the key kinematic mechanisms to the result demonstrated by an athlete depend on the type of a jump, initial conditions, and the task set. There exist different ways of realization of any BM as well as different interaction between BMs within the same jumping event.
Physiological basis of biomechanisms (BMs).
To study a concept of “biomechanism” (BM) in voluntary movements, we should understand physiological processes which take place in muscles directly involved in the biomechanisms.
An efficient take-off in jumps depends on well-coordinated simultaneous activation of three biomechanisms:
BM of the “overturned pendulum” that helps additional to load muscles-extensors of the knee joint of the support leg;
BM of “swinging motion of the arms and swinging leg” that provides additional load on the support leg joints and muscles;
BM of the “support leg and body extension” that provides elastic energy storage in ankle muscles-flexors (plantar extensors) of the support leg.
The BM of the “support leg and body extension” involves more muscle activity than the other two BMs (“overturned pendulum” and “swinging motion of the arms and swinging leg”), which play complementary roles by increasing the impact of the first one on the muscles.
In track-and-field jumps, athletes try to develop the maximal power of movement. Greater power of muscle contraction can result from an increase in either its strength, or its velocity, or both components. As a rule, the most significant gain in power is due to the increase of muscular strength.
Multiple studies demonstrate that manifestation of greater power in jumps may be related to physiological peculiarities of muscle-tendon unit (MTU) activity [17]. Elastic energy of tendons is used for solving various motor tasks, notably for increasing output power of the muscle-tendon unit (MTU) [7].
There are two ways to increase the efficiency of power development in the BM of the “support leg and body extension”: pre-stretch of skeletal muscles and mechanical energy transfer via biarticular muscles.
As regards the first way of increasing power of a take-off (pre-stretch of skeletal muscles), it is known that pre-stretch of muscle-tendon unit (MTU) enhances strength of its subsequent contraction [18]. This mechanism can only be involved if extension starts from the most powerful hip joint due to m.gluteus maximus (GM) contraction. The energy is transferred from the hip joint to the shin via m. rectus femoris (RF) m. gastrocnemius (GA) is not involved into work immediately after the extension of the knee joint; it requires some time for GA to start contraсtion. Elastic energy is stored in the tendons of ankle extensors and then released quickly. As a result, the ankle joint being controlled by the weakest muscles can develop greater power due to the energy transferred from the proximal joint [19]. Thus, the maximal power of muscle contraction is achieved due to conjoint activity of muscles and tendons involved in the mechanism of energy transfer based on the pre-stretch effect.
The second way of increasing power of a take-off (energy transfer via biarticular muscles) was described in a few works including those related to jumps [17, 19]. It also involves processes which take place in the MTU. This mechanism is based on the fact that powerful monoarticular gluteus muscles, in particular GM contribute most to hip extension, thus increasing the angle in the hip joint. The energy is transferred from GM to the knee joint via the biarticular RF, and the knee joint is extended by RF and a group of monoarticular m. vastus (VA). Energy transfer via a biarticular muscle takes place when the muscle contracts, although it develops the greatest power when working in almost isometric regimen, i.e. contracting very slowly [20]. At the same time, knee extension causes plantar flexion in the ankle joint due to energy transfer via m. gastrocnemius (GA) that reinforces contraction of the triceps muscle of the calf [21].
The description of two ways to enhance power of a take-off shows that the MTU pre-stretch requires less time than energy transfer via biarticular muscles. In some works, the first way of power enhancement is called a “catapult” [22, 23]. This time difference is very important with regard to interaction of the BM of “support leg and body extension” with the other two BMs. In the first case (MTU pre-stretch) greater contribution of the “overturned pendulum” will decrease the effectiveness of the mechanism, whereas greater contribution of the “swing” will have positive effect on the result. Greater velocity of the swing owing to active contraction of the working muscles will lead to greater storage of elastic energy, greater stiffness of the support leg, and consequently greater power of the movement. Meanwhile shorter time of the movement will decrease the effect of MTU pre-stretch. In the second case (energy transfer via biarticular muscles) extra loading of the support leg due to greater contribution of the “overturned pendulum” will have positive effect. Greater contribution of the “overturned pendulum” is reached by increasing the step length and thereby lowering the COG trajectory that increases time of the take-off.
As a practical matter, this knowledge is important for correct planning and training. Training exercises should be performed by athletes taking into account their individual peculiarities based on different ratios of BMs contribution in the take-off power.
We have analyzed biomechanical parameters of the take-off in a group of elite Russian male jumpers (n = 50) in competitive conditions (during official contests). The aim of this part of the research was to compare the contribution of different BMs involved into take-off in track-and-field jumps. Video recording was made by a digital camera JVC-9800 with the speed of 50 frames per second. Having been captured by standard computer programs, the image of the jumper’s body was modeled by virtue of an anthropomorphic 12-segment [5]. The computer complex consisted of a few modules: calculation of mass-inertial parameters of an athlete; calculation of kinematical and energy characteristics of movements of separate body links and the whole body based on videotape processing (it allowed to determine linear and angular indices of the body links kinematics as well as potential, kinetic and full energy of each link). The unique feature of this module is the capability to determine changes in length and contraction speed of 9 major muscles of the lower extremities. It permits to determine key peculiarities of the athlete’s technique and to simulate conditions, under which top results could be achieved. Mathematical processing was done in the Scientific Research Institute of the Russian State University of Physical Education, Sport, and Tourism. The accuracy of measurements was determined in a metrological study and accounted for 0.01 m (linear parameters) and 0.02 mps (velocity parameters).
Having analyzed the results, we determined the contribution of BMs into take-off in jumps regarding changes in the full energy of separate segments and links of the athlete’s body. Simulation modeling enabled us to make conclusions concerning not only the ratio of BMs contribution into take-off in every jump event and a comparison of different jumps (horizontal and vertical) but to monitor the change of BMs contribution into take-off in case of 5% increase of the athlete’s COG velocity at the last step of the approach run in each type of jumps and its effect on the result.
Results demonstrated by the athletes in the course of the experiment attained the level of master of sport, international class (Table 1). The simulation modeling showed that the growth of the approach speed would provide real chances of getting into the World championship finals.
Parameter | Long jump | High jump | Triple jump | Pole vault |
---|---|---|---|---|
real result (at the registered COG velocity), cm | 805 ± 12 | 228 ± 2.3 | 1715 ± 35 | 565 ± 5.5 |
Simulated result (at the modeled COG velocity), cm | 820 | 232 | 1740 | 575 |
Jumping results: Real and obtained by simulation modeling.
Table 2 displays proportion (in %) of the BMs contribution into take-off based on changes in the full energy of the athlete’s body links measured in the experiment.
Biomechanism | Long jump (%) | High jump (%) | Triple jump (%) | Pole vault (%) |
---|---|---|---|---|
Leg and body extension | 15.8 | 14.3 | 16.1 | 18.9 |
Swinging links | 69.8 | 70.2 | 70.5 | 67.9 |
Overturned pendulum | 14.4 | 15.5 | 13.4 | 13.2 |
Contribution of different BMs into take-off in track-and-field jumps (%).
The greatest contribution of the BM of swinging links into the take-off is at once apparent. The contribution of the BM of the take-off leg extension is more pronounced in pole vault and less pronounced in high jump, whereas the contribution of the BM of “overturned pendulum” is greater in high and long jumps.
High and long jumps are the most similar in what concerns the structure of BMs operation at take-off, although the execution of the jumps is quite different (horizontal and vertical directions).
We decided to find out what would happen if the athlete’s speed at the last step of the approach run (just prior to take-off) increased by 5%. We took the value 5% because the examination of strength-velocity qualities of top-class athletes permitted to suppose such an increase of speed to be attainable by advanced jumpers, who seem to be capable of bearing higher strength loads.
An increase in speed will naturally lead to an increase in the body links energy. The question arises if the energy growth will be proportional to the speed of the COG in every BM.
Important changes were observed in the BM of swinging links (Figure 1). In high and long jumps the contribution of this BM not only increased, but started earlier. As for triple jump, both temporal and amplitude parameters of the BM of swinging links grew. In pole vault, the increase was proportional to the increase of the COG velocity. The structure of the BM of the take-off leg extension for all the jumps under study remained the same. Considerable changes took place in the BM of “overturned pendulum” in high and long jumps. Its contribution became more important in triple jump but nearly did not change in pole vault.
Change of BMs contribution to take-off.
Changes in the ratio (in %) of BMs contribution into take-off are shown in Table 3.
Biomechanism | Long jump (%) | High jump (%) | Triple jump (%) | Pole vault (%) |
---|---|---|---|---|
Leg and body extension | 13.5 | 13.7 | 12 | 18.7 |
Swinging links | 71.5 | 72.6 | 75.5 | 68.9 |
Overturned pendulum | 15.0 | 14.3 | 12.5 | 12.4 |
Changes in BMs contribution into take-off obtained by modeling a 5% increase in velocity at the last step of the approach.
We should also note an increase in the total energy of the BMs, the greatest being observed in high and triple jumps.
On present evidence, it may be suggested that high and long jumps are the most similar in the structure of take-off, lesser similarity being found with triple jump and pole vault. Therefore, horizontal and vertical jumps reveal a certain resemblance in the structure of take-off (Table 4).
Biomechanism | Long jump (%) | High jump (%) | Triple jump (%) | Pole vault (%) |
---|---|---|---|---|
Leg and body extension | 53 | 52.2 | 52 | 51.9 |
Swinging links | 56.6 | 61.81 | 56.59 | 63.9 |
Overturned pendulum | 57.9 | 59.25 | 57.67 | 54.1 |
Changes of the total energy in every BM (in %) obtained by modeling a 5% increase of speed at the last step of the approach.
The greatest increase in the body COG speed can be reached by intensifying movements of swinging links (as in amplitude, as in temporal parameters). At the same time, training means and methods aimed at the development of the BM of the take-off leg and body extension should not be excluded from training programs, because the links of this BM would have to bear the increased loads resulting from more intensive work of the swinging links. This will provide efficient work of the BM of “overturned pendulum”.
Data obtained in this part of the research permit to conclude that the structure of take-off in track-and-field jumps is formed according to a certain motor objective that depends on the character of the jump.
The speed of the athlete’s body COG at take-off in all jumping events is controlled to a great extent by swinging movements of the body links, in other words, it depends on amplitude and temporal parameters of the swing.
In running jumps, the efficiency of key kinematic mechanisms and, consequently, the efficiency of the athlete’s interaction with the support depends on movements pattern executed by an athlete and aimed at realization of the following pedagogical requirements:
For all jumps:
to run as fast as possible in the approach;
to start active swinging movements before touch-down.
For taking off in long and high jumps:
to lower the body COG at the last 2–3 approach strides;
to plant the take-off foot by a “paddling” movement, but at less angle with the support (i.e. in front of the body) and to lean the trunk backward from the vertical.
The degree of realization of some of the requirements differently affects the realization of the others. For example, the COG lowering at the last approach strides in high and long jumps (contrary to triple jump and pole vault) creates favorable conditions for correct placement of the take-off foot and less angle of the body lean with the support, in spite of setting higher requirements for strength-velocity qualities of the take-off leg muscles. This should increase the contribution of the “overturned pendulum” and swinging links BMs in the phase of the body and take-off leg pivoting upon the point of support so that the trunk becomes positioned vertically above it. The subsequent forward-downward rotation of the take-off leg causes lowering of the knee and coxofemoral joints that is compensated by the mechanism of the take-off leg and body extension. When the take-off leg is planted “under” the trunk at take-off (a popular method of learning technique in long jumps), the take-off leg is lowered (forward-downward rotation), that can be considered as a technique fault, because in this case the efficiency of the BM of the take-off leg and body extension and the contribution of the “overturned pendulum” decrease.
The realization of some of the requirements mentioned above does not always favor the realization of the others. For instance, a too fast approach increases high-impact and inertia loads on the take-off leg, in particular, when it is placed at narrow angle with the support.
Taking into account the take-off structure in a given type of jumps and the pedagogical requirements listed above, any training program in track-and-field should include special means, each affecting technical skills depending on the core and form of a certain movement.
The revealed phenomena allowed to set objectives for the second part of the research aimed at biomechanical investigation of training means most frequently used for technique development in track-and-field jumping events.
To examine biomechanical features of take-off in special exercises primarily used in technique development sessions by track-and-field jumpers (in different jumping events), we have carried out a laboratory experiment on a special complex “Qualisys” (Sweden) using high-speed recording camera (240 frames per second). 5 elite male track-and-field jumpers, regularly performing in international competitions took part in the experiments (2 long jumpers, 2 high jumpers, 1 pole-vaulter). The age of the subjects was 22 ± 1.4 yrs., duration of practicing track-and-field jumps 7 ± 2.3 yrs., height 1.84 ± 0.04 m, weight 74 ± 3.1 kg. Exercises performed in the experiment included: long jump, long jump over a hurdle (0.96 m) placed in 1 m distance from the take-off spot, long jump taking off a raised or lowered board (0.05 m), jump up with touching an object suspended at 2.5 m height and in 1 m distance from the take-off spot, a pattern of 3 hops after an approach run. All exercises were performed after 6 running strides at the maximal approach speed.
In jumps, the greatest mechanical impact directed at stretching biarticular muscles of the lower extremities, in particular, rectus femoris, is achieved at take-off due to simultaneous forced bending of the take-off leg in the knee joint (at shock absorption) and its active straightening in the coxofemoral joint. In this case, the tractive force produced by this muscle is aimed at the knee joint extension. This element of the BM of the take-off leg and body extension at the phase of interaction with the support outwardly looks as the leg flexion with the simultaneous driving of the pelvis and knees forward while leaning backward.
Biceps femoris has two functions in running jumps. According to our data and that reported in other studies, the take-off leg hits the take-off board at the angle of 59–74° with the horizontal, the angles in the coxofemoral and knee joints varying within the range of 165–170° and 160–175° correspondingly, depending on a jump event (Table 5). The trunk having “run against” the support leg planted on a take-off board, starts pivoting forward. Less angles of the support leg touch-down and body lean (measured clockwise with respect to the horizontal) were observed in the following exercises: jump up with touching a suspended object by hand, long jump from a raised platform (0.05 m).
Exercise | Angle in the ankle joint (AJ) at touch-down | Angle in the knee joint (KJ) at touch-down | Angle in the coxofemoral joint (CFJ) at touchdown |
---|---|---|---|
Long jump, ° | 67 | 169 | 171 |
Long jump over a hurdle | 65 | 172 | 175 |
Long jump from a raised take-off platform (0.05 m), ° | 60 | 165 | 170 |
Long jump from a lowered take-off platform (0.05 m), ° | 74 | 174 | 168 |
Jump up with touching an object by hand, ° | 59 | 175 | 172 |
pattern of 3 hops, ° | 70 | 160 | 165 |
Kinematic characteristics of take-off in exercises under study (°).
The analysis of the dynamics of αCFJ/αKJ (Figure 2) and values of angles in the involved joints in different types of jumps showed that the speed of contraction in biarticular muscles is lower than in those being monarticular, and consequently, the tractive force produced by biarticular muscles is greater.
Changes in angles in the coxofemoral and knee joints (αCFJ/αKJ) of the take-off leg at take-off. 1- long jump, 2 - long jump over a hurdle; 3- long jump from a raised take-off platform (0.05 m), 4 - long jump from a lowered take-off platform (0.05 m), 5 -jump up with touching an object by hand °.
It led us to suggest that biarticular muscles play more significant role in providing efficient interaction with the support in jumps. In this context, training means and exercises should be selected so that they could develop strength-velocity qualities of those muscles in plyometric regimen of contraction, primarily with oppositely directed change in angles (as in the pairs CFJ-KJ and KJ-AJ).
Maximal results in jumps are reached when the angle of the knee joint flexion at shock absorption is optimal. These optimal values are different for different jump events. Similar in all the jumps is that the amplitude of the forced leg bending varies within the range of 25–35° and is independent of the type of a jump.
The examination of the three key features of the BM of legs and body extension demonstrated that at dynamic level the structure of the locomotor system determined the specificity of interaction with support in jumping exercises. Under otherwise equal conditions, the following factors are thought to be the most important: 1) maximal values and ratio of force momentums in the joints being involved, and 2) plyometric regimen of contraction of monarticular and, in particular, biarticular muscles.
Additional stretching of the lower extremities muscles at the end of shock absorption is provided by external mechanical load originating from the vertical component of inertia forces (Fin) applied to the centers of mass of swinging links and transferred to the centrifugal force (Fcf), directed along the kinematic chain. The value of Fin depends as on the swinging movement of swinging links, as on the accelerated lift of the linkage points of those links: for arms – the shoulder girdle lift and trunk straightening; for the swinging leg – lift of the pelvis (due to the take-off leg straightening and/or trunk pivoting over the point of bearing in accordance with the BM of the “overturned pendulum”). Contribution of the accelerated lift of the linkage points of the swinging links can be estimated from the difference between the values of Fin and Fcf.
The links are accelerated by:
positive force momentums in the shoulder joints and the coxofemoral joint of the swinging leg;
a decrease of the swinging links radius of inertia (arms flexion in the elbow joints and the swinging leg flexion in the knee joint). According to the law of kinetic momentum conservation, it leads to an increase in the angular velocity of rotating links.
Deceleration of the swinging links goes on in the reverse order – the radius of inertia grows and the sign of the force momentum changes from positive to negative one due to the action of antagonist muscles. This enables an abrupt reduction of Fin in the centers of mass of the swinging links up to zero that, consequently, reduces the load on the lower extremities muscles at the end of the transfer from the plyometric regimen of their contraction to the myometric one. It is the effect of a sudden release of a stretched active muscle [24] Therefore, at that instant the swinging links should have gained the maximal momentum in the direction of the take-off, and the lower extremities should work on the acceleration of the trunk solely.
Results displayed in Table 6 show that inertia forces in swinging motions caused significant changes in the COG vertical velocity at take-off, which were due to:
Exercise | Fin, swinging leg | Fin, upper extremities |
---|---|---|
Long jump | 1253 ± 54 | 655 ± 24 |
Long jump over a hurdle | 1080 ± 36 | 583 ± 35 |
Long jump from a raised take-off platform (0.05 m) | 1345 ± 44 | 674 ± 64 |
Long jump from a lowered take-off platform (0.05 m) | 1437 ± 87 | 812 ± 75 |
Jump up touching a suspended object by hand | 1315 ± 89 | 702 ± 72 |
pattern of 3 hops | 1214 ± 65 | 733 ± 32 |
Maximal vertical component of inertia forces of the swinging links centers of mass at take-off (N).
creation of additional load on muscles-extensors of the lower extremities at the end of shock absorption phase (inertia forces being transferred to the support by kinematic chains);
growth of the swinging links velocity until the start of the knee joint extension;
swinging links position at the end of take-off.
According to our data, the highest (0.06 m) lift of the pelvis (or the marker attached at the point of the CFJ axis of rotation of the take-off leg) at shock absorption takes place in long running jumps with the take-off from a raised platform, despite the knee joint flexion.
It was found out that in a hop performed after an approach run the center of mass of the take-off leg thigh was raised by 0.03 m, while the motion of the swinging links was directed forward-downwards. As consequence of these compensatory movements in long jumps the body COG moves in parallel to the support, and in triple jump (step and jump phases) the body COG is lowered toward the support (Table 7).
Exercise | Shift of the marker, mm |
---|---|
Long jump | 1.2 ± 0.09 |
Long jump over a hurdle | 4.6 ± 0.55 |
Long jump from a raised take-off platform (0.05 m) | 6.1 ± 1.4 |
Long jump from a lowered take-off platform (0.05 m) | 3.0 ± 0.8 |
Jump up touching a suspended object by hand | 5.1 ± 1.3 |
pattern of 3 hops | −2.1 ± 0.07 |
Shift of the marker placed at the CFJ axis of rotation of the take-off leg at shock absorption.
The evidence concerning the BM of the “overturned pendulum” proved that its efficiency depends to a certain extent on the position of the athlete’s body at touch-down. The less is the touch-down leg angle and the more is the body backward lean, the longer will the distance used for accelerating the pelvis, trunk, and the whole body be.
Findings of the second part of the research demonstrate that there exist specific biomechanical characteristics of training means used by track-and-field jumpers.
We have found out that in training exercises the take-off is performed using relatively independent BMs, similar to those recorded in competitive jumps. Being dependent of the motor task (conditions of performing the exercise), key biomechanisms appear to be interdependent on the dynamic level, i.e. the contribution of one of them affects that of the others. The role and contribution of the BMs depend on the type of an exercise or conditions of its execution, initial conditions, and a motor task set to an athlete. There exist different ways of realization of any BM as well as different interaction between BMs within the same jumping event.
Specific features of take-off in the examined exercises permitted to classify all the training means into four groups (Table 8):
training means, mostly involving the BM of legs (take-off leg) and body extension (group I)
training means, mostly involving the BM of swinging links (group II);
training means, mostly involving the BM of an “overturned pendulum” (group III);
training means, involving the combination of the BM of swinging links with the BM of the “overturned pendulum” (group IV).
Exercise | BM of leg and body extension | BM of swinging links | BM of the “overturned pendulum” | Group |
---|---|---|---|---|
Long jump after a short approach run | 17 | 68 | 15 | I |
Long jump over a hurdle | 15 | 65 | 20 | III |
Long jump from a raised take-off platform (0.05 m) | 13 | 65 | 22 | II |
Long jump from a lowered take-off platform (0.05 m) | 10 | 74 | 16 | III |
Jump up with touching a suspended object by hand | 13 | 70 | 17 | IV |
pattern of 3 hops | 18 | 69 | 11 | I |
Contribution of different BMs in track-and-field technique development exercises (%).
Thus, different special exercises are intended to exert specific effects on the structure of take-off, those effects being dependent on the specifics of the content and form of an exercise.
This comparison of technical drills differs from the conventional one, in which every kinematic or dynamic parameter of an exercise is compared with the similar parameter of an actual competitive jump.
Several specific exercises that are currently used in training athletes in different jumping events are listed below as examples (Table 9). All the exercises are classified into groups I – IV and may be recommended for practical use by athletes of a corresponding specialization. The list of exercises is not full because of the scope limitations for materials to be presented, but it provides general notion about aspects of training means selection for solving concrete training tasks taking into account jumpers’ specialization.
Group | Jump event | Exercises |
---|---|---|
I | Triple jump Long jump Pole vault | After 4–6 running strides jump onto a pile of mats landing on a swinging leg. Important is to bring pelvis forward at the take-off. |
III | High jump Long jump | After 4–6, running strides jump in a “stride” over 2 hurdles; the distance between the take-off spot and the first hurdle is from 180 to 220 cm, the distance between the hurdles 80–90 cm. |
III | Triple jump Long jump Pole vault | After 6–7 strides of a direct approach, run make a long jump over a bar set at the height 70–80, 90–100, 110–120, or 130–140 cm, taking off in 80–90 cm from the nearest upright. |
IV | High jump Long jump | After 4–8 running strides make a long jump attempting to touch a suspended object by the chest or head. Take off in a distance of 2–2.5 m from the projection of the hanging object. |
II | Triple jump Long jump | Alternate leg bounds in a pattern: floor (the take-off leg) – floor board (the swinging leg) – low vaulting horse (the take-off leg) with touching a suspended object. |
I | Triple jump Long jump High jump | A barbell 20–30 kg on the shoulders. After 2–3 walk steps plant the take-off leg on the board (5–10 cm lower than the surface of the approach) and pushing off by the swinging leg quickly straighten the take-off leg with the following swing by the swinging leg. |
II | High jump Long jump | Standing on a gymnastic bench, feet shoulder width apart, a barbell (10–30 kg) on the shoulders. Step forward, straighten the support leg bringing the swinging leg forward simultaneously, and putting it on a bar of wall bars. The distance between the bench and the wall bars – 200 – 280 cm. |
II | Triple jump Long jump Pole vault | Stand with one foot in front, the other one behind the trunk. Grip the hanging rope at the head level. Push off the take-off leg bringing pelvis to the rope. While moving the rope in a circle, perform giant strides. Important is to bring pelvis forward in proper time. |
IV | Triple jump Long jump Pole vault | Stand on a platform 70–80 cm high, the swinging (take-off) leg in front, the take-off (swinging) leg behind. Jump down landing on the take-off (swinging) leg and bounce up onto a platform 20–30 cm high trying to shorten the touch-down to take-off phase as much as possible. The take-off is similar to that of a long (high) jump. |
IV | High jump | After 6–8 approach strides in a curve leaning into the arc, make a long jump attempting to touch a suspended object by the chest or head. Take off in a distance of 2–2.5 m from the projection of the hanging object. |
IV | High jump | The same exercise as the previous one, but after a swing turn the knee of the swinging leg inside (toward the take-off leg). When taking off turn the trunk so that the shoulder opposite to the take-off leg is brought forward. |
III | Triple jump Long jump High jump | Jump down from a platform 50–80 cm high, land on one foot, and jump over a hurdle trying to make the touch-down to take-off phase as short as possible. The height of the hurdle should be gradually increased. The distance from the platform to the hurdle 3–5 m. |
III | Triple jump Long jump High jump | The same as the previous one, but landing on both feet. |
IV | Triple jump Long jump | After 2–4 running strides make a triple jump starting from a raised platform. In the third phase (the jump) clear a hurdle set at 50–80 cm height. |
I | Pole vault | Starting position (s. p.): Grip onto a pole (in a vertical position) by the right hand, keeping the arm straight, do one step backward and grip the pole by the left hand 30–40 cm lower than the right one. Without moving in any direction tries to touch the pole by chest lifting the knee of the swinging leg. |
I | Pole vault | S. p. – similar to the previous one. While stepping forward, do a take-off and hang on to the pole. |
II | Pole vault | After 4–6 running strides take off and hang on the pole. At the end of the legs swing make a half turn counterclockwise to face the approach runway. |
I | Pole vault | After an approach run jump and grip on to a hanging rope, swing legs upward–forward and turns counterclockwise. Clearing a bar can be added. |
II | Pole vault | When performing a pole vault swing legs upward trying to reach the upper end of the pole by them. |
Specific exercises that are currently used in training athletes in different jumping events.
BM | Biomechanism |
CFJ | coxofemoral joint |
KJ | knee joint |
AJ | ankle joint |
COG | center of gravity |
CNS | central nervous system |
SP | starting position |
MTU | muscle-tendon unit |
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). 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He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"19",type:"subseries",title:"Animal Science",keywords:"Animal Science, Animal Biology, Wildlife Species, Domesticated Animals",scope:"The Animal Science topic welcomes research on captive and wildlife species, including domesticated animals. 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