**3.2 Mapping experiments among knowledge trees**

In order to prove mapping among the knowledge trees that represent the ontologies which are taken into account in each node of the system, two experiments have been carried out.

(a) OOSS KC nodes topics mapping (b) FLAT KC nodes topics mapping

Fig. 10. Mapping between topics of KnowCat nodes

In the first experiment of this series, again, we start up with KC node on OOSS, although in this case two new nodes have been created from it with knowledge trees equal to the original. The documents of the initial node have been divided up into the recently created

number of topics is higher and the number of documents per topic is lower, groupings

(a) OOSS KC node documents similarity (b) CS KC node documents similarity

In order to prove mapping among the knowledge trees that represent the ontologies which are taken into account in each node of the system, two experiments have been carried out.

(a) OOSS KC nodes topics mapping (b) FLAT KC nodes topics mapping

In the first experiment of this series, again, we start up with KC node on OOSS, although in this case two new nodes have been created from it with knowledge trees equal to the original. The documents of the initial node have been divided up into the recently created

Fig. 9. Automatic grouping of documents by topics of knowledge area

**3.2 Mapping experiments among knowledge trees**

Fig. 10. Mapping between topics of KnowCat nodes

appear like smaller light blocks.

ones, so that every couple of new homologous topics has a similar number of different documents, but relevantly similar. Later the WWV of the topics of the new nodes have been calculated and have been compared with each other. A graph (see Fig. 10 left) has been produced with the values of similarity obtained, where the lines of colour blocks correspond to the topics of one node and the columns to the other. The topics have been organised into both dimensions in order that the homologous topics are in the same position in the corresponding entries of the table. Like in previous graphs, the highest values of similarity are represented by the lighter colours.

As can be seen in the image of this first experiment, the highest grades of similarity -blocks of light colour- are over the diagonal in almost every case. With the proposed approach, this means that it is possible to identify the branches of the knowledge trees that contain documents dealing with the same topics.

For the second experiment two KC nodes on FLAT that have different trees to organise the knowledge have been used. Again the WWV of the topics have been calculated from the documents included within them and the grades of similarity have been calculated from the topics of different nodes comparing their corresponding vectors. The result is shown in a graph (see Fig. 10 right) where the topics of one node are in the axis of abscissa and the other in the organised axis. As on other occasions, the grade levels of similarity are shown in colour blocks, where again, the higher the value of coefficient, the lighter the colours. In this case, the pair of topics that are considered linked to each other through their contents by means of a manual analysis by an expert on the subject have been marked with a cross.

As a result of this second experiment, it can be seen that most of the associations made by an expert fit in over light colour blocks and that every light block is found in topic pairs associated by the expert. Therefore, it is possible to identify the proposed procedure and the topics that deal with related issues in different knowledge trees automatically.

#### **3.3 Automatic association experiment among knowledge nodes**

Starting from the documents included in five KC nodes, the one belonging to CS used in the first group of experiments, the two OOSS prepared for the previous group and the two FLAT used in the same group, a WWV has been established for each of them. In every case, the documents included in the nodes are different. By comparing these WWV a graph (see Fig. 11) has been obtained, in which each line of blocks, vertical and horizontal,

Fig. 11. Grouping of KC nodes per topics

Digestion of Knowledge in a KM System to Reveal Implicit Knowledge 115

Spanish National Plan of R+D, project code TIN2008-02081/TIN, and by the CAM

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Cobos, R. (2003). *Mechanisms for the Crystallisation of Knowledge, a proposal using a collaborative*

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Kiryakov, A., Popov, B., Terziev, I., Manov, and D., Ognyanoff, D. (2004). *Semantic*

Moreno-Llorena, J. (2008). *Collaborative Knowledge Management By Means Semantic Information*. Doctoral dissertation. Universidad Autónoma de Madrid. Moreno-Llorena, J., Alamán, X. (2005). *A Proposal of Design for a Collaborative Knowledge*

Moreno-Llorena, J., Alamán, X., Cobos, R. (2009a). *Modeling of User Interest Based on Its*

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*introduction search approach*. Kluwer, 2001.

**6. References**

corresponds to a node. Like in previous diagrams, the lighter colours represent a greater similarity.

In the graph we can see that the level of similarity among the WWV of the nodes that deal with the same topics are high compared with the ones obtained where comparing the node vectors on different topics. This means -using this technique- that it is possible to identify nodes that deal with similar contents and to distinguish them from others on different subjects.


*2.2. Experimental Setups*

Planar lipid bilayers were formed by the Montal–Muller method [28] across a circular hole 117 µm in diameter (d). The hole was in a thin Teflon sheet sandwiched between two reservoirs in the Teflon chamber. The glass window in the Teflon chamber allowed easy viewing of the hole during the formation of the planar lipid bilayer using stereo microscope with led illumination. Both sides of the hole in the Teflon sheet were first treated with 1 µL of the lipid solution. After the lipid solvent had completely evaporated, the hole was treated on each side with 1.5 µL of the hexadecane and pentane mixture. Both reservoirs were filled with electrolyte just below the hole. A drop of 2 µL of the lipid solution was carefully applied to the electrolyte surface in each reservoir and allowed to spread for at least 15 min. To form a planar lipid bilayer, the electrolyte level in both reservoirs was raised to the same height just above the hole (Figure 1A). After a planar lipid bilayer was ruptured, a new one was formed by lowering the electrolyte levels below the hole, and then raising them again. All experiments were performed at room temperature (24 °C).

The voltage-controlled measurement system [29] consisted of a signal generator, two Ag-AgCl electrodes (E-205, IVM, Healdsburg, CA, USA) immersed in the electrolyte, one on each side of the planar lipid bilayer, and amplifiers connected to an oscilloscope for measuring transmembrane current and voltage. The electrodes were used to apply the transmembrane voltage and measure the transmembrane current. The transmembrane voltage was measured using the LeCroy differential amplifier 1822 and custom made ampere meter was used for transmembrane current measurements. Both signals were stored in Matlab format using LeCroy Waverunner-2 354M oscilloscope and analyzed afterwards.

The current-controlled experiments were performed using a potentiostat-galvanostat measurement system controlled by a computer as described in [20]. Four Ag-AgCl electrodes (E-205, IVM, Healdsburg, CA, USA) immersed in the electrolyte were used, two on each side of the planar lipid bilayer. One pair of electrodes was used to apply current and the other was used as a reference electrodes. The transmembrane voltage measurements were stored on a computer in a raw file and later imported into Matlab for further analysis.

**Figure 1.** The measurement protocol consisted of formation of the planar lipid bilayer (**A**), capacitance measurement (**B** or **C**), and measurement of the breakdown voltage *Ubr* of the planar lipid bilayer (**D** or **E**).


**5. LDL and total cholesterol**

diabetes, and smoking are the main risk factors for atherosclerosis. Fibrinogen, hsCRP, and interleukin-6 (IL-6) which are markers for inflammation have been found elevated in atherosclerosis. Atherosclerotic diseases are also common with systemic inflammatory diseases such

Atherogenesis starts early in life. Subintimal lipoprotein accumulation and leukocyte adhesion occurs as a result of increased endothelial permeability due to endothelial damage. Intimal neovascularization is seen with the migration of vasa vasorum into the intima. There is a structure in the luminal side of the atherosclerotic plaque that is called fibrous cap which contains molecules such as smooth muscle, collagen, and elastin. External elastic lamina is placed adjacent to the atherosclerotic plaque and tunica media and the lipid core is found between these two structures and it is made from the cholesterol crystals, smooth muscle

Atherosclerotic plaques create luminal stenosis in the advanced stage. The first study in this subject was conducted by Glagov et al. in autopsy material of a patient with 136 left main coronary artery (LMCA) lesions. In this study, there is a positive correlation between internal elastic membrane area and plaque area, and luminal stenosis is prevented by expansion of the compensator at the atheromatous load of less than 40% [1]. In the REVERSAL trial, statin therapy was performed in patients with asymptomatic coronary artery disease. Atheroma volume, percentage of atheroma volume, and atheromatous change in the diseased segment were evaluated and progression of coronary atherosclerosis was observed in the pravastatin receiving group compared to baseline but no progression of atherosclerosis was observed in

The lipid core that carries triglycerides and cholesterol esters has a hydrophobic structure and is coated with polar capsules which contain apolipoproteins, phospholipids, and nonesterified cholesterol crystals. When the lipoproteins were classified according to their migration rates in lipoprotein electrophoresis, the band closest to the origin formed the chylomicron band; low-density lipoprotein (LDL) in the beta band, very-low-density lipoproteins (VLDL) in the pre-beta band, and high-density lipoprotein (HDL) in the alpha band, respectively.

Chylomicron is synthesized in liver from dietary fat molecules. Since chylomicrons and VLDL molecules larger than 70 nanometers cannot reach the subintimal region through the transcytotic transport system, chylomicrons do not have atherogenic potential. But chylomicron remnants are atherogenic and cannot be removed from circulation when they are present in high quantities [3]. Hydrolysis of the chylomicrons with lipoprotein lipase results in the formation of VLDL. The majority of VLDL is converted to LDL. Chylomicrons are attached to ApoB48. Chylomicron rem-

nants, LDL, and VLDL are connected to apoB100 and are called non-HDL cholesterol.

as lupus and rheumatoid arthritis.

96 Cholesterol - Good, Bad and the Heart

**2. Pathogenesis of atherosclerosis**

cells, vascular structures, and foam cells.

the atorvastatin group [2].

**3. Lipoprotein structure**

LDL is the particle that is responsible for transporting cholesterol to tissues. Cholesterol transportation is achieved by binding of the LDL receptor and apoB. There are three separate fractions of LDL: LDL (large/floating), IDL, and small dense LDL. The most atherogenic LDL is small dense LDL.

In the WOSCOP trial and the AFCAPS/TeXCAPS trial which used pravastatin and lovastatin, respectively, the effect of hyperlipidemic therapy on the primary prevention of coronary artery disease was shown [7, 8]. The ASCOT-LLA study was terminated early in hypertensive individuals because atorvastatin significantly reduced nonfatal MI and CAD-induced mortality [9]. Similarly, the CARDS study was terminated early in diabetic individuals because atorvastatin decreased 37% in major cardiovascular events and 48% in stroke [10].

LIPID study compared low-dose and high-dose atorvastatin in patients with stable coronary artery disease and mortality was similar in both groups, but there was a significant decrease in major cardiovascular events in the high-dose atorvastatin group. The HPS study has shown that statin therapy protects high-risk patients with LDL cholesterol levels below 116 mg/dL [11]. CARE study with pravastatin in acute MI and MIRACLE study with atorvastatin in USAP or MI have shown early initiation of statins have a positive affect [12, 13].

In the ASTEROID trial, high-dose statin therapy (rosuvastatin 40 mg/day) was shown to reduce 53% LDL cholesterol, 15% increase in HDL cholesterol, and regression in 78% atheroma [5].

Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors inhibit the PCSK9 protein, which is effective in LDL receptor synthesis and provide 50–70% reduction in LDL-C levels.

In the ACCELERATE study, it was shown that the CETP inhibitor (Evacetrapib) did not reduce major cardiovascular events despite a 39% reduction in cholesterol level [14].


*Investigations of Using an Intelligent ANFIS Modeling Approach for a Li-Ion Battery… DOI: http://dx.doi.org/10.5772/intechopen.105529*

environment integrated with a specialized simulator ADVISOR for batteries design of different chemistry, which is very spread in the automotive industry. National Renewable Energy Laboratory (NREL) developed this simulator in 1983 and improved its performance until the last release in 2003-00-r0116. A 6 Ah nominal capacity and 11 V nominal voltage SAFT Li-ion battery integrated into the hybrid electric vehicle (HEV) BMS structure, namely a Japanese Toyota Prius, one of the most spread commercial hybrid electric cars on the automotive industry market. It is equipped with an MC-AC75 motor of 75 kW and a powertrain control version, TX-5SPD manual transmission with five speeds and frontal wheel drive, as shown in **Figure 1**. In this figure on the right side, the reader can see the type of vehicle in the database of the ADVISOR simulator considered one of its primary inputs; also, at the bottom side is shown the open-circuit voltage (OCV) graph of the proposed SAFT Li-ion battery as the most suitable for the HEV car selected in the case study.

**Figure 2** shows the Simulink diagram of the Toyota Prius HEV car configuration. In **Figure 3**, you can see the graphical user interface that selects the urban dynamometer driving schedule (UDDS) of cycle speed profile for a driving test, the initial temperature, state of charge (SOC), and the ambiental conditions.

In **Figure 4**, the MATLAB simulations results are displayed. Among these results two of the variables of interest can be emphasized, such as the UDDS driving cycle input current profile (ess\_current) and the battery SOC evolution (ess \_soc\_hist).

The UDDS driving cycle of the current profile shown in **Figure 5** is the equivalent of the UDDS driving cycle speed profile shown in the graph in **Figure 3**. It represents

**Figure 1.** *The ADVISOR 3.2 simulator -input interface set up.*

**Figure 2.** *Block diagram of HEV powertrain configuration.*

**Figure 3.**

*Graphical user interface with the ADVISOR simulator parameters.*

the evolution of the input battery current during a repeated sequence of charging and discharging the battery for different periods.

More precisely, the ADVISOR 3.2 Simulator provides an extensive Database of different types of HEV cars, driving cycles speed tests, and input currents profiles for battery charging and discharging cycles.


**2. Chemical characteristics of heavy metals**

Heavy metals contaminate water sources through various industrial activities, agricultural practices, and improper waste disposal [12]. Heavy metals are nonbiodegradable in nature and accumulate in humans and animals when they consume contaminated food and water. Many researchers have investigated the contamination of water sources with heavy metals [13]. **Table 1** provides the characteristics of some common heavy metals found in a water sources.

Among the heavy metals, arsenic, cadmium, and lead were extensively studied for their toxicity in water sources. Studies have estimated various effects of heavy metals in drinking water [14, 15]. Arsenic and cadmium have been considered cancercausing agents in humans [16]. Arsenic also leads to skin damage. It has also been investigated that 1 L of drinking water contaminated with 50 μg of arsenic can cause liver, kidney, bladder, and lung cancer. Lead has been examined to affect the central nervous system, cardiovascular system, gastrointestinal system, reproductive system, neurological system, renal system, endocrine system, and immune system [14]. Exposure to the low concentration levels of lead can also reduce neurodevelopment. The presence of lead in bloodstream alters the functioning of neurons and interferes

*Removal of Heavy Metals from Wastewater with Special Reference to Groundnut Shells… DOI: http://dx.doi.org/10.5772/intechopen.109904*

### **Table 1.**

*Characteristics of common heavy metals [13].*

### **Table 2.**

*Chemical properties of heavy metals [13].*

with the trimming and pruning of synapses during brain development, which may result in permanently altered functions [14].

Cadmium is the most often reported heavy metal in the water sources [14, 15, 17]. Cadmium-contaminated drinking water leads to chronic renal failure, anemia, cardiovascular diseases, osteoporosis, and also hypertension [14]. There are also other heavy metals that can also put a living being in jeopardy. Researchers have estimated that 1 L of drinking water contaminated with nearly 8.29–51 μg chromium can cause liver, lung, and kidney cancer and also affect genitourinary organs among women [12]. Studies have shown that the stagnation of water in hot water tanks and polypropylene water pipes can significantly increase the concentration of various heavy metals. **Table 2** provides the chemical properties of common heavy metals.


**7. Conclusions**

*Glaciers and the Polar Environment*

• Humpback whales from Population D (Area IV) have expanded their geographical distribution to the south in recent years, while the distribution of the Antarctic minke whales of the Eastern Indian Ocean population (Area IV) has remained stable in the open sea. Larger number of Antarctic minke whales are

• The nutritional conditions of Antarctic minke whales has been deteriorating as

7–8 years old until the 1998 cohort, and the PPF of Antarctic minke whale has

• The ASM of Antarctic minke whale has remained low and stable around

Deteriorating nutritional conditions of the Antarctic minke whales suggest less food available for this species in recent years. Less availability/abundance of Antarctic krill could be a response to environmental changes (e.g. global warming) and/or competition for food with the increasing large whale species such as Antarctic blue, fin and humpback whales. No evidence of global warming exists for the Indo-Pacific sector of the Antarctic [31], so competition for food could be a more plausible interpretation for the results of nutritional deterioration in the Antarctic minke whale. This reflects the end of the period of a krill surplus hypothesised during the past century. Nutritional deterioration as a consequence of competition is not entirely consistent with the low ASM and high APR of the Antarctic minke whale over more recent years. Under the competition hypothesis, this could be the result of a temporal phenomenon in that the response of ASM and APR to environmental

distributed in polynias within the pack-ice in recent years.

remained high (over 0.9), and stable.

evidenced by a decreasing trend of several nutritional indices.

changes producing a nutritional deterioration may be subject to time lags.

Direct competition occurs when two predators are present in the same area as a prey species, and may interfere with each other's access to the prey. Indirect competition may occur when two predators occur in different parts of the area of prey, but because the prey's production is limited, consumption by the one predator limits the production available for the other, and *vice versa* [32]. To investigate the plausibility of the competition hypothesis, estimates of krill biomass trends in the research area are required. There is some partial information on krill abundance based on dedicated krill surveys in the past, but the information is scattered and needs to be combined with that from new surveys in a comprehensive and consistent way so that time series data can be obtained. The period of these demographic changes in the Antarctic minke whales coincides with the post whaling era (1970's-) which has been characterized as 'unfavorable climate conditions and increasing competition for krill' [3].

Antarctic minke whales could also be using alternative feeding areas (e.g. polynias within the pack-ice) in response to the increase in abundance and geographical expansion of these other large whale species. This could provide an alternative explanation from sighting surveys and population models of a decrease and then re-stabilization of Antarctic minke whales abundance in open areas since the 1970's. The increase of the Adelie penguins (*Pygoscelis adeliae*) in East Antarctic in recent decades [33] seems immediately not to be consistent with the competition hypothesis. The authors of the Adelie penguin study provided two explanations for the increase of this species: (i) harvesting of baleen whales, krill and fish across East Antarctic waters through the 20th century could have reduced competition between Adelie penguins and other predators for food, improving prey availability, and (ii) a proposed reduction in sea-ice extent in the mid-20 century may also have benefited Adelie penguins by enabling better access to the ocean for foraging. Since recovery of krill-eating large baleen whales has been reported since the 1980's, it is suggested that their explanation of environmental factors for the demographic changes of Adelie penguins since the

**98**

This review of the scientific evidence for ecosystem changes in the Indo-Pacific sector of the Antarctic has highlighted the importance of long-time monitoring research programs focused on the collection of biological data for krill predators (both land-based and sea-based predators) as well as data on sea ice cover and environmental variables. The hypothesis proposed for the recent demographic changes found in whales is competition. To investigate this hypothesis further, estimates of krill abundance as well additional data collection of the predators and improved modelling analyses are required (see [34]). Also, oceanographic data obtained for more than 30 years in the Indo-Pacific sector of the Antarctic by JARPA/JARPAII surveys should be analysed to explore further the possible effect of global warming on the pattern of demographic changes found in whales.

In this context, Japan has started a new non-lethal research program to continue studying whales and the environment in the Indo-Pacific sector of the Antarctic. The program called JASS-A (Japanese Abundance and Stock structure Survey in the Antarctic) started in the austral summer of 2019/20, and will continue for at least eight years. It will conduct systematic sighting surveys for abundance estimates, biopsy sampling for genetic analysis of population structure, oceanographic and marine debris surveys, satellite tracking and photo-identification for studies on stock structure, distribution and movement of large whales, and Unmanned Aerial Vehicle (UAV) to observe whales outside of the main survey area. The analyses of the data to be collected will assist to examine the plausibility of the hypothesis proposed in this study further, in particular the observation that Antarctic minke whales have been moving into polynias within the pack-ice in recent years.


*Scientometrics Recent Advances*

### *Contrasting High Scientific Production with Low International Collaboration and Scientific… DOI: http://dx.doi.org/10.5772/intechopen.85825*

On the other hand, it can be observed in the table that the robust quantitative data of the scientific production of the 35 countries conceal the dispersion of the most important qualitative components of this ranking: the scientific impact (Switzerland 22.9, Iran 7.1) and international collaboration (Switzerland, 62.2%, Turkey, 18.0%), with the average for the 35 countries of 15.3 and 40.8%, respectively, for the two indicators. It should be mentioned that when the data of the most recent year (2016) is taken, the international cooperation index of Switzerland increases from 62.2 to 72.1%, Turkey from 18.0 to 21.1%, and the average of the 35 countries from 40.8 to 49.9% (data not shown), thus confirming the recent tendency for the growth of international collaboration among countries. Analyzing the impact ranking (numbers in brackets in the column), a different figure is shown where the first ten countries in number of publications do not appear in a similar position in the impact ranking. Here, the first seven positions are occupied by Switzerland, Denmark, the Netherlands, Scotland, Sweden, Finland, and Belgium; none of them is present in the first quantitative positions, but all of them are showing a high proportion of international collaboration, thus indicating again the correlation—high international collaboration and higher citation impact—as shown in **Figure 3**.

**Table 3** clearly illustrates the influence of international cooperation on the index of scientific impact of the countries. Here we can see that the 21 countries with the highest international collaboration rates (above the average of **Table 2**, or 40.8%), varying between 41.3% (Spain) and 62.2% (Switzerland), have an average impact well above the mean of all countries. In this group, only the United States (25.7%) and Hong Kong (32.7%) have international cooperation level below average. The average impact index of the 21 countries in this group is 17.9 and the international collaboration is 49.2%. On the other hand, among the 14 other countries with the lowest impact rates, only Mexico has international collaboration above the average of the 35 countries. In this second group, the average impact index of the 14 countries is 11.4 and the international collaboration is 28.3%. The indices of the countries in the first group are, respectively, 57 and 74% higher than those in the second group, confirming again this positive correlation: high international collaboration, higher citation impact. Brazil is located in the group of countries that cite more than they are cited [10]. As with most countries with a low level of international cooperation, Brazil's low impact index (9.3), one of the lowest among the 35 most productive countries, is, in turn, followed by a low percentage (29.3%) of international scientific collaboration, also of the lowest in the whole world.

This work also included comparative studies with countries in Latin America, some of them linked to the MERCOSUL agreement and the component countries of the BRICS group. Both consort of countries present common commercial and social interests including the perspective of presenting some level of scientific collaboration. The comparison of Brazil with other Latin American countries is shown in **Table 4** which presents the data of the scientific production of the 12 most productive countries of Latin America in the period 2000–2016. This group includes members of the MERCOSUL: Argentina, Brazil, Chile (associated), Uruguay, and Venezuela (suspended in 2016). The evolution of the percentage of international collaboration in the period studied for the five most productive countries in the region is shown in **Figure 4**. Taken in consideration the number of WoS indexed publications shown in **Table 4**, it is seen that Brazil alone responds for more than 50% of publications of the 12 countries. It is also seen that half of these countries produced in the large period analyzed a small number of publications having all of them a very high percentage (64.4–86.3%) of international collaboration. The average percentage of cited articles in the 12 countries (71.1%) is relatively high compared to the world, and this high value is in agreement with that of the scientific impact (13.3). The variation in international collaboration ranged from 29.3 to 86.3%, with a high

*Scientometrics Recent Advances*

**102**

**Country** Denmark

Israel Austria

Greece Finland Hong Kong

Mexico Portugal

Norway Czech Republic

Singapore South Africa **Total and average**

**World (without double**

**counting)**

**World (with double counting)**

**Table 2.**

*Scientific performance of the 35 most productive countries in 2000–2016.*

**31,110,404** *Source: ESI-InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

35

147,248 **28,671,597** **26,238,799**

**354,501,667**

**66.5**

**13.2**

**466,906,409**

**73.3**

**15.3**

1,800,982

71.4

12.2

33 34

162,098

2,818,900

77.7

17.4

163,456

1,944,604

72.3

11.9

29 30 31 32

175,131

3,224,102

77.7

18.4

175,260

2,317,498

71.7

13.2

175,970

1,886,483

69.9

10.7

180,864

3,077,238

79.0

17.0

24 25 26 27 28

194,118

3,861,474

79.4

19.9

194,490

2,618,069

72.6

13.5

240,026

4,136,021

71.9

17.2

241,825

4,458,141

75.8

18.4

248,517

5,266,614

76.4

**Rank**

**Articles**

**Times cited**

**% Doc. Cited**

**Citation impact**

21.2

**International collaborations**

138,171 104,323 137,232

78,315 99,452 59,202 74,953 86,924 96,192 77,412 83,015 70,708 **9,984,236**

**—**

Percent double counting: 16.1%

**% International collaborations**

55.6

43.1

57.2

40.3

51.2

32.7

42.6

49.6

54.9

47.4

51.2

48.0

**40.8**

**18.0**

### **Table 3.**

*Influence of the international collaboration on the scientific impact of countries: 2000–2016.*

average rate (61.5%). Brazil, despite its higher production, has the lowest impact rate (9.3) and international collaboration (29.3%) level. Contrasting with their rate of publications, the countries with the highest impact rates also present the highest levels of international collaboration, confirming the observation that there is an intrinsic relationship between these two indicators. Thus, in comparison with the most productive Latin American countries, Brazil is behind the other countries of the group reinforcing the significant observation: greater proportion of international collaboration, higher index of scientific impact [14].

However, according to recent studies [14, 21], it is doubtful whether the apparently positive data of high scientific impact by itself with low autonomous significant science production and very high dependence of international collaboration would be able to give good perspectives for the country's social and economic development.

**105**

**Figure 4.**

**Table 4.**

cooperation shown by Russia.

*Contrasting High Scientific Production with Low International Collaboration and Scientific…*

Brazil 1 539,049 4,997,160 69.5 9.3 158,083 29.3 Mexico 2 175,970 1,886,483 69.9 10.7 74,953 42.6 Argentina 3 133,349 1,611,771 73.4 12.1 56,759 42.6 Chile 4 87,419 1,107,194 71.9 12.7 49,465 56.6 Colombia 5 42,021 417,829 64.3 9.9 25,034 59.6 Venezuela 6 21,667 239,514 68.2 11.1 11,064 51.1 Cuba 7 14,331 145,440 71.1 10.2 9231 64.4 Peru 8 12,892 181,816 66.6 14.1 10,246 79.5 Uruguay 9 11,920 158,319 73.5 13.3 7997 67.1 Costa Rica 10 7562 129,555 75.4 17.1 5679 75.1 Ecuador 11 6972 85,375 68.6 12.3 5841 83.8 Panama 12 4941 132,191 80.6 26.8 4262 86.3 **Total and average 1,058,093 11,092,647 71.1 13.3 418,614 61.5** *Source: ESI-InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

**% Doc. cited**

**Citation impact**

**International collaborations**

**% International collaborations**

**cited**

In the case of the BRICS countries, South Africa is the country with the highest international collaboration rate (48.0%) and the country with the highest impact (12.2) (**Table 2**). **Figure 5** illustrates the recent evolution (2000–2016) of the international collaboration of the BRICS countries. With the exception of South Africa that exploits international collaboration at a level similar to the more developed countries, the other members of the group have much lower rates. Brazil, which has had an oscillating collaboration rate since the beginning of the period, has resumed a stronger growth from 2010 onwards, surpassing in 2015 the index of international

*International collaborations in scientific publications in Latin American countries in 2000–2016. Source: ESI-*

*InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

*DOI: http://dx.doi.org/10.5772/intechopen.85825*

**Country Rank Articles Times**

*Productivity ranking of Latin American countries in 2000–2016.*

*Contrasting High Scientific Production with Low International Collaboration and Scientific… DOI: http://dx.doi.org/10.5772/intechopen.85825*

### **Table 4.**

*Scientometrics Recent Advances*

**N. Country Citation**

**impact**

1 Switzerland 22.9 1 62.2 United

2 Denmark 21.2 2 55.6 Hong

**104**

20 South Africa

21 Czech Republic

**Table 3.**

average rate (61.5%). Brazil, despite its higher production, has the lowest impact rate (9.3) and international collaboration (29.3%) level. Contrasting with their rate of publications, the countries with the highest impact rates also present the highest levels of international collaboration, confirming the observation that there is an intrinsic relationship between these two indicators. Thus, in comparison with the most productive Latin American countries, Brazil is behind the other countries of the group reinforcing the significant observation: greater proportion of international

*Influence of the international collaboration on the scientific impact of countries: 2000–2016.*

**Average 17.9 — 49.2 Average 11.4 — 28.3** *Source: ESI-InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

**More than 41% of international collaboration Less than 41% of international collaboration**

 Netherlands 21.2 3 51.0 Japan 14.7 21 23.7 Scotland 20.8 4 43.6 Greece 13.5 22 40.3 Sweden 20.7 5 54.5 Taiwan 12.0 25 23.1 Finland 19.9 6 51.2 Korea 11.1 27 26.3 Belgium 19.2 7 58.8 Mexico 10.7 28 42.6 England 18.7 9 42.0 China 10.1 29 23.6 Israel 18.4 10 43.1 Poland 9.9 30 34.8 Norway 18.4 11 54.9 India 9.5 31 20.9 Germany 18.2 12 45.5 Brazil 9.3 32 29.3 France 18.2 13 48.4 Russia 7.9 33 33.1 Canada 18.0 14 43.6 Turkey 7.8 34 18.0 Singapore 17.4 15 51.2 Iran 7.1 35 21.4 Austria 17.2 16 57.2 — — — — Australia 16.3 18 43.1 — — — — Italy 16.1 19 41.0 — — — — Spain 15.0 20 41.3 — — — — Portugal 13.2 23 49.6 — — — —

**Country Citation**

States

Kong

12.2 24 48.0 — — — —

11.9 26 47.4 — — — —

**impact**

**Rank % International collaborations**

19.0 8 25.7

17.0 17 32.7

**Rank % International collaborations**

However, according to recent studies [14, 21], it is doubtful whether the apparently positive data of high scientific impact by itself with low autonomous significant science production and very high dependence of international collaboration would be able to give good perspectives for the country's social and economic development.

collaboration, higher index of scientific impact [14].

*Productivity ranking of Latin American countries in 2000–2016.*

### **Figure 4.**

*International collaborations in scientific publications in Latin American countries in 2000–2016. Source: ESI-InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

In the case of the BRICS countries, South Africa is the country with the highest international collaboration rate (48.0%) and the country with the highest impact (12.2) (**Table 2**). **Figure 5** illustrates the recent evolution (2000–2016) of the international collaboration of the BRICS countries. With the exception of South Africa that exploits international collaboration at a level similar to the more developed countries, the other members of the group have much lower rates. Brazil, which has had an oscillating collaboration rate since the beginning of the period, has resumed a stronger growth from 2010 onwards, surpassing in 2015 the index of international cooperation shown by Russia.

### **Figure 5.**

*International collaborations in scientific publications in WoS of the BRICS countries in 2000–2016. Source: ESI-InCites dataset updated 2017-04-15. Includes Web of Science content indexed through 2017-02-03.*

### **Table 5.**

*Scientific collaborations of Latin American and BRICS countries with Brazil in 2000–2016.*

Based on the set of results shown for the Latin American and BRICS countries, we analyzed the scientific cooperation of these countries with Brazil. **Table 5** shows the total production data of the Latin American countries and the components of the BRICS group, the number of joint publications with Brazil, and the respective

**107**

*Contrasting High Scientific Production with Low International Collaboration and Scientific…*

percentage of collaboration of these countries with Brazil. The indices of collaboration in joint publications of Brazilian scientists with the Latin American or from BRICS countries are extremely low. Moreover, taking as an example the cooperation of the Latin American countries with Brazil shown in **Table 5**, it can be seen that the percentage of articles coming from this cooperation weighs much less on the total of Brazilian publications than on each of the partner countries, indicating that the rates of scientific cooperation among the countries of the region are very low when compared to the levels of international cooperation shown by these countries, as seen in **Table 5**. For Mexico's production, for example, this figure represents only 3.1% of its total scientific output and 14.5% for Costa Rica. For the estimation of this weight on Brazilian scientific production, the levels are even lower, varying from 0.1% (Panama and Costa Rica) to 1.7% with Argentina. A similar situation occurs when one compares the collaboration between scientists from BRICS countries and Brazil. That is, collaboration in the research projects of these countries, components of these two important trade blocs with Brazil, is practically nonexistent, suggesting that scientific and technological cooperation does not assume any significance in the context of these official partnerships. Nonetheless, such treaties emphasize that cooperation must include not only economic aspects but also scientific partnerships. Conversely, it has been observed for Latin American countries (data not shown) that intra-regional collaborations are much weaker than collaborations with developed countries. A similar situation was identified by Finardi [11] and Finardi

In this article, special emphasis was given on the influence of international cooperation on the qualitative performance of scientific production. In the analysis presented here, which identifies in the low international scientific collaboration the unfavorable position of Brazil as concerned to the citations and impact of its publications, whether in the world context or in its position among the countries of the two economic blocs in which it participates, the MERCOSUL and BRICS. It is important, however, to point out that other factors, not discussed in this article, can influence the impact of scientific publications such as the size of the scientific community in each area of knowledge, the language, the maturity level of the areas in each country (or even the global world maturity of the same areas), and the degree of priority given by government agents to the technical and scientific development of certain areas with a view to explore comparative advantages as well as focusing

As noted in an earlier study, the unbalanced and asymmetric international collaboration introduces profound distortions in the qualitative data of scientific production (citations, impact, world impact) of numerous countries and in the world, thus interfering in the expectations of scientific, technological, social, and economic development of the countries dependent on this type of international cooperation [14]. In this sense, in a recent article, Silva [22] deals with the relationship between productivity aspects and the quality of scientific production in the countries. The author makes severe criticism regarding comparisons of the scientific performance of Latin American countries. The author points out that it would not be appropriate to congratulate to some countries based on a simple analysis of these issues, since some countries have differentiated productivity in terms of their research and development priorities, with a high degree of self-financing, whereas the scientific production in other countries is highly dependent on the participation of international research groups and external financing. In our opinion, this

*DOI: http://dx.doi.org/10.5772/intechopen.85825*

and Buratti [12] for the BRICS case.

**4. Brazilian situation in the analyzed context**

the economic and social development of the countries.

*Contrasting High Scientific Production with Low International Collaboration and Scientific… DOI: http://dx.doi.org/10.5772/intechopen.85825*

percentage of collaboration of these countries with Brazil. The indices of collaboration in joint publications of Brazilian scientists with the Latin American or from BRICS countries are extremely low. Moreover, taking as an example the cooperation of the Latin American countries with Brazil shown in **Table 5**, it can be seen that the percentage of articles coming from this cooperation weighs much less on the total of Brazilian publications than on each of the partner countries, indicating that the rates of scientific cooperation among the countries of the region are very low when compared to the levels of international cooperation shown by these countries, as seen in **Table 5**. For Mexico's production, for example, this figure represents only 3.1% of its total scientific output and 14.5% for Costa Rica. For the estimation of this weight on Brazilian scientific production, the levels are even lower, varying from 0.1% (Panama and Costa Rica) to 1.7% with Argentina. A similar situation occurs when one compares the collaboration between scientists from BRICS countries and Brazil. That is, collaboration in the research projects of these countries, components of these two important trade blocs with Brazil, is practically nonexistent, suggesting that scientific and technological cooperation does not assume any significance in the context of these official partnerships. Nonetheless, such treaties emphasize that cooperation must include not only economic aspects but also scientific partnerships. Conversely, it has been observed for Latin American countries (data not shown) that intra-regional collaborations are much weaker than collaborations with developed countries. A similar situation was identified by Finardi [11] and Finardi and Buratti [12] for the BRICS case.


*3.3. Joint Dwell Time and Bandwidth Optimization Problem Solution*

The optimization problem proposed in Equation (45) is non-convex, containing two parameters *T*d *m*,*q*,*k* and β*m*,*q*,*k*. We can use the exhaustive method to solve it, which is simple but too inefficient. The genetic algorithm uses selection, cross and mutation operators for searching, which has a great global search ability. However, the local search ability of this genetic algorithm is weak. In contrast, most of the classical nonlinear algorithms adopt the means of the gradient method, which has a strong local search ability, while also possessing a weak global search ability. As a result, we will solve the problem in (45) by NPGA [29], which combines the global search ability of the genetic algorithm and the local search ability of the classical nonlinear programming algorithms. The flowchart of NPGA is shown in Figure 1:

**Figure 1.** The nonlinear programming-based genetic algorithm (NPGA) flowchart.

By working out the problem (45) for *Q* · C*M N* times, we can get all the optimal solutions of the dwell time with respect to different target and radar combinations in the constraint of \$*N i*=1 *u q i*,*k* = *M*. Then we can use the exhaustive method to obtain the optimal results of the dwell time and radar allocation index in the constraints of \$ *Q q*=1 *u q i*,*k* ≤ 1. However, the exhaustive method is complex and inefficient. As a result, we propose a radar node selection algorithm with lower computation complexity.

Assuming *M* = 2, which means that each target is fixed to be tracked by two radars at each moment. We define *Rl* = {*a*, *b*}(*l* = 1, 2, ... , *L*) as the combinations of the two radars in the radar network, where *L* = C2 *N* = *N*! (*N*−2)!2! . When the target *q* is illuminated by the *Rl* index radars, suppose *Sl*,*k*,*q*,min = % *T*d *a*,*q*,*k*,min&(*l*) + % *T*d *b*,*q*,*k*,min&(*l*) denotes the minimum dwell time which is solved in (45) through NPGA, where % *T*d *a*,*q*,*k*,min&(*l*) and % *T*d *b*,*q*,*k*,min&(*l*) denotes the dwell times of radar *a* and radar *b*, respectively. The minimum dwell time matrix **S***k*,min which is composed of *Sl*,*k*,*q*,min, is shown in Table 1.

**Table 1.** Minimum dwell time matrix for the fixed radar combination (*M* = 2).

Similar to the term *u q i*,*k* , we define a set of binary variables *Uq l*,*k* ∈ {0, 1} to represent the radar combination selection index.

$$L\_{l,k}^q = \begin{cases} 1, \text{ if the } q \text{th radar is translated by the } l \text{th radar combination at time index } k\\ 0, \text{ otherwise} \end{cases} \tag{46}$$

Then the optimization model of the radar combination allocation index can be described as:

$$\begin{aligned} \min & \sum\_{q=1}^{Q} \sum\_{l=1}^{L} \mathsf{LI}\_{l,k}^{q} S\_{l,pk,\min} \\ & \text{s.t.} \begin{cases} \sum\_{l=1}^{L} \mathsf{LI}\_{l,k}^{q} = 1 \\ \sum\_{l=1}^{L} \mathsf{LI}\_{l,k}^{q} R\_{l} \cap \left( \bigcup\_{l=1}^{L} \mathsf{LI}\_{l,k}^{m} R\_{l} \right) = \mathcal{Q}\_{\prime} \,\forall r \neq m, r, m = 1, 2, \dots, Q \end{cases} \end{aligned} \tag{47}$$

where the first constraints imply that each target is tracked by a fixed radar combination at time index *k*, while the second one suggests that a single radar tracks at most one target at *k*. The solution method of (47) can be shown in Algorithm 1.

**Algorithm 1.** Radar allocation method

**Step (1):** Working out the problem in (47) *Q* · *N*! (*N*−2)!2! times, then we can get the minimum dwell time matrix *q*

**S***k*,min in the constraint of \$*N i*=1 *u i*,*k* = 2.

**Step (2):** Sort the columns of matrix **S***k*,min in ascending order and assign the target corresponding to the smallest element in the first row to the corresponding radar combination.

**Step (3):** Remove the column vectors corresponding to the target assigned in Step (2). Remove all the row vectors of the radar which is contained in the radar combination assigned in Step (2).

**Step (4):** Repeat Step (2) and Step (3) until all the targets are assigned in order to obtain the optimal allocation matrix **U***k*,opt.

By using the above algorithm, we can obtain the optimal radar allocation results **U***k*,opt, where **U***k*,opt = 4 **U**1 *k*,opt, **U**2 *k*,opt, ... , **U***Q k*,opt5 , **U***q k*,opt = 4 *Uq* 1,*k*,opt, *Uq* 2,*k*,opt, ... , *Uq L*,*k*,opt5T . When *Uq l*,*k*,opt = 1, *u q a*,*k*,opt = *u q b*,*k*,opt = 1, *T*d *a*,*q*,*k*,opt = % *T*d *a*,*q*,*k*,min&(*l*) , *T*d *b*,*q*,*k*,opt = % *T*d *b*,*q*,*k*,min&(*l*) . When *Uq l*,*k*,opt = 0, *u q a*,*k*,opt = *u q b*,*k*,opt = 0, *T*d *a*,*q*,*k*,opt = *T*d *b*,*q*,*k*,opt = 0. Then we can get **u***k*,opt and **T**d *k*,opt at time index *k*, which are the radar allocation index and dwell time optimization results, respectively.

The computational complexity of 0 is *O* % *Q*2 2 × *N*! (*N*−2)!2! log2 % *N*! (*N*−2)!2!&&, while the computational complexity of the exhaustive method is *O* % *N*! (*N*−2)!2!&*Q* . Compared with the enumeration method, 0 can greatly reduce the computational complexity and improve the real-time performance.


**Acknowledgements**

This work was supported by the Artemis Trust (No grant number). The funder had no further role in the design, collection, analysis, interpretation and compilation of this paper and no financial interests or benefits have arisen from the direct applications of this research.


**6. Conclusion**

The goal of this chapter is to implement HIL-based grasp and task control algorithms supported by using the vision-based sensors, tactile, and force sensors. The objective was to test the performance of the youBot arm for physical interaction with an environment and control its manipulation using the algorithms implemented in C2000 real time controller based on HIL simulation technique. HILbased control algorithms actuate the joints of virtual youBot model implemented with appropriate scenario in V-REP robot simulator driven by the model of the robot implemented using MATLAB. After the analysis of performance of HILbased grasp and task control algorithms youBot arm and a study of the pre-existing control and hardware, we proposed a new decentralized control architecture. The control algorithms are tested first using C2000 controller on a V-REP robot simulator, then by observing satisfactory performance of the algorithms they can be ported to real youBot arm. The HIL-based results of the vision and force controller give confidence level of 92% to deploy them on real youBot mobile manipulator. It can be further improved with more adaptive and optimization techniques.

## **Acknowledgements**

The authors gratefully acknowledge the support of the Management of Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India, the Principal and Head of Electronics and Communication Engineering Department for their constant support and encouragement.


*Brief Report* **Descriptive Kinematic Analysis of the Potentially Tragic Accident at the 2020 Austrian MotoGP Grand Prix Using Low-Cost Instruments: A Brief Report**

### **Marco Gervasi \* , Erica Gobbi , Valentina Natalucci, Stefano Amatori and Fabrizio Perroni**

Department of Biomolecular Sciences, University of Urbino, 61029 Urbino, Italy<EMAIL_ADDRESS>(E.G<EMAIL_ADDRESS>(V.N<EMAIL_ADDRESS>(S.A<EMAIL_ADDRESS>(F.P.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-07-2230-3413

Received: 19 September 2020; Accepted: 28 October 2020; Published: 30 October 2020

**Abstract:** Background: During the first Austrian MotoGP Grand Prix of 2020, following a serious accident involving the riders J. Zarco and F. Morbidelli, Morbidelli's riderless bike cartwheeled across turn 3, narrowly missing V. Rossi and M. Viñales by just a few centimeters. As is the case with ordinary traffic accidents, analyzing the dynamics of motorcycle racing accidents can help improve safety; however, to date, the literature lacks studies that analyze the causes and severity of such accidents. Hence, the purpose of this study was to analyze the main causes that led to the accident at the 2020 Austrian MotoGp Grand Prix, to quantify the speeds and distances of the bikes and riders involved, and to hypothesize several alternative scenarios using a low-cost method. Method: Kinovea and Google Earth Pro software were used to identify markers along the racetrack and to measure the distances and calculate the time it took the motorcycles to cover those distances. The analyses were carried out on three 30-fps (frames per second) videos. Results: Zarco's average speed as he was overtaking Morbidelli on the straightaway before turn 2 was 302 ± 1.8 km/h, higher than that of Rins and Rossi (299.7 ± 1.7 and 296 ± 1.7 km/h, respectively). The speed of Zarco and Rossi's bikes 44.5 m before the crash was the same (267 ± 7.9 km/h). Immediately after overtaking Morbidelli, Zarco moved 2.92 m towards the center of the racetrack from point A to B, crossing Morbidelli's trajectory and triggering the accident. Morbidelli's riderless bike flew across turn 3 at a speed of about 76 km/h, missing V. Rossi by just 20 cm. The consequences could have been catastrophic if Rossi had not braked just 0.42 s before encountering Morbidelli's bike in turn 3. Conclusion: Through a low-cost quali-quantitative analysis, the present study helps us to gain a deeper understanding of the dynamics of the accident and its main causes. Furthermore, in light of our findings regarding the dynamics and severity of the accident and the particular layout of the Red Bull Ring circuit, racers should be aware that overtaking at the end of turn 2, following the same trajectory as the riders involved in the crash, could be very risky.

**Keywords:** MotoGP; video analysis; collision; accident; safety

#### **1. Introduction**

Motorcycle racing in the elite MotoGP class attracts millions of spectators worldwide, with riders competing in 18 races across the globe each year. Crashes are a rare but regular feature of elite motorcycle racing [1–3], with a prevalence of 9.7 per hundred rider hours, while injuries are uncommon, occurring in only 9% of crashes [4]. In a recent study, Bedolla et al. identified four basic crash types in elite motorcycle racing: lowside, highside, topside, and collision crashes [4]. The causes and dynamics of such accidents are multiple and, without reliable data, difficult to understand. However, as is often done in the case of ordinary traffic accidents, a reliable analysis of motorcycle crash dynamics can be

made using video footage, providing us with insights into the causes of the accident and, in some cases, leading to improvements in safety conditions [5–8].

In particular, in the case of ordinary traffic accidents, the video footage that is analyzed comes from safety cameras installed in vehicles, from smartphones [5,8], or from surveillance cameras found in the area of the accident [7]. This footage is then analyzed using software that allows us to precisely measure distances, speed, and angles and hence, reconstruct specific trajectories. Nevertheless, in these cases, there are limitations stemming from the difficulty of obtaining video footage from different angles, as well as limitations related to the frequency of the frames per second of the images, which can vary according to the type of camera being used. On the contrary, elite MotoGp motorcycle races are filmed with cameras providing top quality images from multiple angles, making video analyses based on such footage more precise. Unfortunately, in the literature, there are few studies investigating the dynamics of accidents in motorcycle racing [6]. This lack of research is probably due to the inherent difficulty of obtaining data even though such sporting events are monitored by high definition cameras and multiple high precision sensors. In the absence of this sort of high-quality data, the analysis of television footage using low-cost instruments can provide insights into the dynamics and seriousness of accidents, as well as other key factors contributing to accidents, to improve race safety conditions. In this brief report, we analyzed the potentially catastrophic accident that occurred at the MotoGP Grand Prix in Austria on 16 August 2020. Veteran rider Valentino Rossi and his teammate Maverik Viñales miraculously emerged from the crash unscathed, narrowly avoiding being struck by not just one, but two airborne motorbikes. The circuit at the Austrian Grand Prix, namely the "Red Bull Ring" (see Figure 1), is among the fastest in the world [9], featuring numerous stretches where riders reach speeds of over 300 km/h, followed by sections that require them to slow down to 50–60 km/h. The total length of the track is 4318 m and it features 10 turns with a vertical drop of 65 m. In the specific case of the 2020 Austrian Grand Prix, the contact that occurred between the two bikes could easily have resulted in an unprecedented tragedy.

**Figure 1.** An overview of the Red Bull Ring circuit, Austria: points from 1 to 10 represent the turns in the track.

#### *Accident Description*

It all unfolded on the eighth lap of the race, as the riders raced down the straightaway between turns 2 and 3 (see Video S1). Until the moment of the crash, the riders were in the following order, from first to tenth: P. Espargaró, A. Dovizioso, J. Miller, J Mir, M. Oliveira, M. Viñales, V. Rossi, F. Morbidelli, J. Zarco, and A. Rins. The accident occurred after turn 2. Zarco had just overtaken Morbidelli and was preparing to enter turn 3, but moments after overtaking the other rider, Zarco's rear wheel came into contact with the front wheel of Morbidelli's bike, setting off a terrible chain of events. Following the contact, the two riders were thrown from their bikes, which continued careening down the track at high speed along different trajectories. Morbidelli's bike proceeded along the outer edge of the track in proximity to the curb, crossing over turn 3 and maintaining the same trajectory that it had been on in the straightaway leading into the curve. Zarco's bike followed a different trajectory, crashing into barriers, which, nonetheless, did not prevent it from careening across turn 3 an instant before Morbidelli's bike. Viñales and Rossi were rounding turn 3 just as the two bikes cartwheeled across their paths. Miraculously, Morbidelli and Zarco's bikes missed the riders by only a few centimeters. Zarco's bike, after striking the barriers, flew across the curve just a few centimeters over Vinales' helmet, while Morbidelli's bike sliced between Viñales (in front) and Rossi, crossing the latter rider's path. This accident has been the focus of a great deal of media attention, and in its wake, there have been numerous unconfirmed reports regarding the factors that may have led to the crash. Hence, the aim of the present study was to perform an analysis of the available race footage to answer the following questions: What were the main factors and dynamics that led to the accident? How fast was Morbidelli's bike travelling as it careened across Rossi's path? How close did Morbidelli's bike come to striking Rossi and why did it miss him? Can we quantify the hypothetical impact of Morbidelli's bike with Valentino Rossi?

#### **2. Materials and Methods**

The analysis of the available race footage was developed according the procedure showed in Figure 2.

**Figure 2.** Chart showing the analytical procedures applied in the study.

The analysis was performed using Kinovea software (Kinovea ver. 0.8.15, open source project) [10] applied to three 30 fps (frames per second) videos (30 fps means that the camera captured 30 frames in a single second of video). Due to the video frame rate that limits precision to 0.03 s, we incorporated this error into the calculated speeds, showing the upper and lower average error estimates. Google Earth Pro software (Google LLC, Mountain View CA, USA) was used to measure the distance between the key points on the racetrack (see Figures 3–5). This software is scientifically validated for measurements of this kind [11]. Finally, Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) was used to perform data analysis and to create the figures.

**Figure 3.** Satellite image showing the section of the straightaway from point A0 (corresponding to the point where the asphalt ends and the grass begins on the outer edge of the track) to point A (line between points G and H, where a shadow was cast directly under the "myWorld" billboard). The distance from A0 to A measures 288 m.

**Figure 4.** Satellite image showing the section of the straightaway before turn 3. The white arrows indicate a billboard and the first white stripe on the left side of the track (race direction). The red dashed lines were traced from point G to point H and from point E to point F. Points A and B indicate the crossing of the front wheel of Zarco's bike, as well as the front wheels of the other riders under consideration. The distance from point A to point B measures 44.5 m.

**Figure 5.** Satellite image showing the section of the straightaway leading into turn 3. Point C corresponds to a marker on the internal curb of turn 3, the point where Morbidelli's bike began cartwheeling across the track. Point D is shown by a marker on the outer edge of turn 3 with a trajectory that follows the line of the straightaway, the same trajectory that Morbidelli's bike followed as it cartwheeled across the curve. The line from C to D measures 14 m.

Δ − The first video was provided by a fixed camera near the track, while the other two videos were from the onboard cameras of Rins (second video) and Rossi (third video). In all three videos, but from different angles, we identified point A as the exact instant when the front wheel of Zarco's bike crossed the line between points G and H (Figure 4), where a shadow was cast directly under the 'myWorld' billboard, while point B indicates the exact instant when the front wheel of Zarco's bike crossed the line formed between the first white stripe, clearly visible on the left side of the track (point E) and the point on the opposite side of the racetrack (point F, Figure 4), just before Zarco and Morbidelli's bikes first made contact. Point C indicates the moment just before Morbidelli's bike began crossing turn 3, while point D indicates the moment when it had completed its trajectory across turn 3 (Figure 5). In the first video, provided by the fixed camera positioned near turn 3, we can see the straightaway where the accident took place and turn 3 as Rossi and Viñales encountered the careening bikes. In order to measure the movement of each rider towards the middle of the track immediately after turn 2, we used the first video to calculate the distance that the first nine riders maintained between their Front Wheel and the inner edge of the track at point G (FWG) and point E (FWE) (Figure 6); hence, we calculated the delta between the two distances (∆ = FWE − FWG). In addition, we traced the trajectories of the first nine riders and reported the movement of Morbidelli and Zarco immediately before, during, and after Zarco's overtaking of Morbidelli. In order to calculate the average speed of the bikes at various stages of the accident, we measured how long it took Zarco's bike to travel from point A to point B and Morbidelli's bike to travel from point B to point C and from point C to point D. Furthermore, examining the first video, we identified and analyzed the frame that captures the exact moment when Rossi encountered Morbidelli's bike in turn 3. Considering the measurement of the diameter of Morbidelli's rear wheel, we were able to measure how close the bike came to striking Rossi.

**Figure 6.** Distance between point G and point H: 12 m; distance between point E and point F: 12 m. The colored lines represent the trajectories of the first nine riders traced from point A, where the front wheels of the bikes cross the line between G and H, and point B, where they cross the line between E and F. We also reported the distance between Zarco's Front Wheel and point G (FWG) and between his Front Wheel and point E (FWE): FWG: 0.98 m; FWE: 3.90 m.

We also analyzed the second video from the onboard camera of Rins, who, at that moment in the race, was right behind Zarco and Morbidelli. The analysis of this footage allowed us to measure the average speed of Zarco and Rins before they headed into turn 2 and to identify the moment when the riders passed point A0 (corresponding to the point where the asphalt ends and the grass begins on the outer edge of the track; see Figure 3). Using Google Earth Pro, we then measured the distance between points A0 and A, and, using Kinovea, we measured the time it took Zarco and Rins to travel between those points. Moreover, in order to confirm the previous measurements made using the first video, the same calculations for Zarco and Morbidelli's bikes were performed from points A to B and from points C to D. Using the third video provided by the onboard camera of Rossi, which shows the speed of the rider in real time, we tested the reliability of the indirect measurements. Specifically, we compared Rossi's average speed from points A0 to A and from points A to B, calculated using both the average speed from all the collected frames and the time it took Rossi to travel the distances measured. To understand why Morbidelli's bike did not strike Rossi, we analyzed Rossi's actual speed and the lean angles from point A to the exact moment when Rossi encountered Morbidelli's bike. Finally, we hypothesized two alternative scenarios and calculated the potential impact force of a collision between Rossi and Morbidelli's bike.

#### **3. Results**

The distances between the key points, which were identified on the track and measured using Google Earth Pro, are reported in Table 1 (see Figures 3–5), whereas the FWG and FWE distances of the first nine riders and their delta are reported in Table 2.

From a qualitative standpoint, the analysis of the trajectory showed that Zarco, as he moved past Morbidelli, crossed the latter rider's trajectory, moving 2.92 m towards the center of the track (see Table 2 and Figures 6 and 7).

**Table 1.** Distances between key points on the racetrack.

A0 = point where the asphalt ends and the grass begins on the outer edge of the track; A = point where the front wheels of the riders cross the line between points G and H; B = point where the front wheels of the riders cross the line between points E and F; C = point where Morbidelli's bike began to cross the track of turn 3; D = point where Morbidelli's bike ended to cross turn 3; G and H = the two ends of the line cast directly under the "myWorld" billboard; E and F = the two ends of the segment crossing the racetrack starting from the first white stripe on the left side of the track. **Δ −**

**Table 2.** Distances between the front wheels of the first nine riders and points G (FWG) and E (FWE) and their Delta. Zarco's values are in bold.

FWG = distance that the first nine riders maintained between their front wheel and the inner edge of the track at point G; FWE = distance that the first nine riders maintained between their front wheel and the inner edge of the track at point E.

**Figure 7.** Representation of the dynamics of the accident showing that Zarco crossed Morbidelli's trajectory immediately after overtaking him as he moved towards the center of the track.

Zarco's movement towards the middle of the track was found to be greater than that of the other riders, whose movement was analyzed. Regarding the measurements of the time the motorbikes took to travel between the various points in the three different videos, we obtained the following results: based on footage from the first video, Zarco's bike took 0.60 s to travel from point A to point B (44.5 m), at an average speed of 267 ± 7.9 km/h; Morbidelli's bike took 3.93 s to go from point B to point C (205 m), at an average speed of 188 ± 1 km/h, and 0.66 s to travel from point C to point D (14 m), at an average speed of 76 ± 2.3 km/h. Analyzing the second video from Rins' onboard camera, we determined that Zarco covered the 288 m from point A0 to point A in 3.43 s, at an average speed of 302 ± 1.8 km/h, whereas Rins covered the same distance in 3.46 s, travelling at an average speed of 299 ± 1.7 km/h. Moreover, from the same footage, we were able to determine that it took Morbidelli's bike 0.60 s to cover the stretch from point A to point B, 3.93 s from point B to point C, and 0.66 s from point C to point D (confirming the previous measurements). Using the footage from Rossi's onboard camera, we determined that it took Rossi's bike 3.50 s to cover the stretch from point A0 to point A, at a calculated average speed of 296 ± 1.7 km/h, which corresponds to the average speed determined from the actual speeds recorded in the 105 frames analyzed from point A0 to point A (295.6 km/h). Furthermore, we found that Rossi's actual average speed from point A to point B was 266 km/h, and it took him 0.60 s to cover the stretch from point A to point B, with an average indirectly calculated speed of 267 ± 7.9 km/h. These data confirm the reliability of our measurements and hence the quality of the analyses that were carried out based on those measurements. Analyzing Rossi's real speed (exactly 172 frames) from point A to the moment in which he encountered Morbidelli's bike (turn 3), we identified a point in which Rossi appeared to brake harder, as he entered turn 3, reducing his speed by 10 km/hr in 0.03 s, just 0.40 s before encountering Morbidelli's bike (see Figure 8).

**Figure 8.** Rossi's actual speed and instantaneous G-Force peak from point A to the point where he narrowly missed colliding with Morbidelli's bike. The circled area shows the moment in which Rossi braked, reducing his speed by 10 km/h in 0.03 s.

However, a deceleration of this magnitude is highly unlikely because, even if it were just for an instant, it would reach about 9 G. The braking system manufacturer, Brembo, claims that the "average" deceleration in the approach to turn 3 at the 2019 Austrian MotoGP was 1.23 G, and this value is in line with that which was analyzed here (from 285 to 53 km/h in 5.58 s, corresponding to an average of 1.2 ± 1.2 G). However, deceleration peaks are possible because rapid deceleration may occur with both sudden downshifting and with variations in the pressure applied by the rider to the brake lever. Indeed, analyzing the frame-by-frame G forces from Rossi's deceleration curve (see Figure 8), we obtained instantaneous peaks that were even higher than 3.4 G. Hence, we proceeded to correct a possible data transmission error and/or lag by applying a moving average of five frames to reduce the G-force peak. Rossi's deceleration thus appears more plausible. Indeed, as he rode through turn 3, he probably applied additional pressure to his brake lever, reducing his speed by 16.7 km/h in the 0.42 s before he encountered Morbidelli's bike. We then developed two different hypothetical braking scenarios 15 frames before Morbidelli's bike crossed Rossi's path: in scenario 1, we hypothesized what would have occurred if Rossi had reduced his speed by 12.7 km/h, while in scenario 2, we postulated what would have occurred if he had reduced his speed by 8.7 km/h (see Figure 9).

**Figure 9.** Rossi's actual speed (moving an average of 5 points), 33 frames before the point where he narrowly missed colliding with Morbidelli's careening bike. As described in the legend, the solid dots represent Rossi's real speed and the solid triangles represent the speed in hypothetical scenario 1, while the empty dots represent the speed in scenario 2.

Based on the calculation of the distance that would have been covered in the first hypothetical scenario, Rossi would have been 40 cm ahead of where he actually was on the track, while in the case of the second scenario, he would have been 76 cm ahead (see Figure 10). In addition, from the analysis of the frame in which Rossi encountered Morbidelli's bike, we determined that the bike missed Rossi by just 20 cm. We also represented the points in which Morbidelli's bike would have struck Rossi in the two hypothetical scenarios (see Figure 10).

Finally, based on footage from the first two videos, we can estimate with a good degree of accuracy that the speed of Morbidelli's bike as it tumbled across turn 3 was about 76 km/h or 21.1 m/s. If it had struck Rossi at that speed, the consequences would certainly have been catastrophic. To have a more precise idea of those possible consequences, we would need to know the deceleration time of the bike following its impact with the rider. Fortunately, there was no impact, and thus, we were not able to calculate the deceleration time. We therefore calculated the force of the impact by hypothesizing that if the motorbike (150-kg) impacted an air-fence (soft-wall safety barrier) and we were to see a 2-m deformation of the barrier, we could calculate the deceleration time in the following manner Equation (1):

$$\text{Deceleration Time (impact duration)} = (2 \times 2 \text{ m})(21.1 \text{ m/s}) = 0.19 \text{ s} \tag{1}$$

Hence, we would obtain the impact force (IF) Equation (2):

$$\text{IF} = (150 \text{ kg} \times 21.1 \text{ m/s})(0.19 \text{ s}) = 16.657 \text{ kN} = 1698 \text{ kg} \tag{2}$$

The formula can easily be extended to calculate the approximate maximum impact force (peak impact force) by multiplying the resulting average impact force by two: 33.314 kN = 3396 kg.

**Figure 10.** The frame capturing the exact instant when Morbidelli's bike crossed Rossi's path. Kinovea was used to measure the distances based on the measurement of the rear wheel of Morbidelli's bike. Specifically, the white arrows indicate the 60-cm diameter of Morbidelli's rear wheel (black circle); the green arrows represent the exact distance between Rossi's front wheel and Morbidelli's rear wheel; the yellow and red arrows indicate the distance that Rossi's bike would have covered if he had slowed down according to scenario 1 (about 40 cm) and scenario 2 (about 76 cm), respectively.

#### **4. Discussion**

In the present report, using low-cost instruments, we performed a quali-quantitative analysis of the dynamics of the accident involving Zarco, Morbidelli, Rossi, and Viñales during the 2020 Austrian MotoGP. To our knowledge, this is the first time that a video analysis has been applied to an accident in a motorcycle race, whereas analyses of this kind are common for ordinary traffic accidents [12,13]. From our analyses, we were able to calculate that, in the straightaway leading into turn 2 (from A0 to A), Zarco was travelling at a higher average speed (302 ± 1.8 km/h) than either Rins or Rossi (299 ± 1.7 and 296 ± 1.7 km/h, respectively), probably because he was drafting off Morbidelli as he overtook him. In addition, from the average speeds of Zarco and Rossi in the stretch from point A to point B (266 km/h), we can conclude that the accident occurred as the riders were slowing down. Our analysis of the trajectories showed that, after point B, all the riders tended to move towards the middle of the track because of the layout of the circuit. However, the movement of Zarco's bike (2.92 m) was greater than that of all the other riders' bikes that were taken into consideration. Moreover, if we trace the trajectory of Zarco and Morbidelli (Figures 6 and 7), we clearly see that, as he overtook Morbidelli, Zarco crossed his trajectory as he proceeded towards the middle of the track, just as Morbidelli did after having been overtaken, with his front wheel extremely close to Zarco's rear wheel. It seems clear that Zarco made a risky maneuver choosing to pass Morbidelli at that point in the track and then moving towards the center of the track, knowing that the other rider was exceedingly close. Based on these analyses, it would seem that the layout of the track contributed to the scenario that played out, leading to the accident, and therefore, possible changes in the circuit might be considered. However, few data are available on the relative safety of motor racing circuit layouts. In fact, to the best of our knowledge, there is only one study [14] that analyzed whether changing the configuration of a

motor racing circuit makes it safer. This particular study showed that the introduction of two bends into the fastest part of the track (similar to the one analyzed here) led to a decrease in the seriousness of auto racing accidents; however, these changes in the track seem to have resulted in a tendency for motorcycle racers to suffer slightly more serious injuries [14]. Hence, the decisions riders make to undertake or not to undertake certain maneuvers remain a key determining factor. Regarding the frame by frame analysis of Rossi's speed as he entered and then rounded turn 3, it is very interesting to note his sudden deceleration, which, from a qualitative analysis of the images, appears to have been the result of his drawing too close to Vinales. If this was indeed the case, the presence of Vinales ahead of Rossi may have saved Rossi's life or, in any case, led to his narrow avoidance of the collision. Indeed, if Rossi had braked slightly less, as hypothesized in scenarios 1 and 2, he would have been struck directly by Morbidelli's bike. Finally, based on our analysis of the first and second video and the distance measured between points C and D, we calculated that Morbidelli's bike travelled across curve 3 at a speed of 76 ± 2.3 km/h. Considering that a MotoGP bike weighs 150 kg, we hypothesized what the impact of the bike would be at that speed against air-fence barriers, obtaining a peak impact force of 33.3 kN. The tolerance of the human body to kinetic forces released in ordinary road accidents is limited. Injury is broadly related to the amount of kinetic energy applied to the human frame [15]. For example, a study estimated that the tolerance of the whole human neck to injury in tensile loading is as low as 3.1 kN for subjects with no pre-impact awareness and as high as 3.7 kN for subjects with a tensed cervical spine resulting from sufficient pre-impact awareness [16]. Considering that the peak impact force that we calculated was nine times higher than the maximum force tolerated by the human neck, if Morbidelli's bike had struck Rossi at neck level, he would have had little chance of surviving the impact. On the other hand, if this impact had been absorbed by his entire body, we estimate that the impact force would have been comparable to the impact of a 65 kg (V. Rossi's weight) body falling from a height of about 15 m (the fourth floor of a building). Although it is impossible to make further hypotheses regarding the possible consequences of the impact of Morbidelli's bike against Rossi because of the countless unpredictable variables in play, our calculations help us to imagine the severity of such an impact.

#### *Limits and Strengths of the Study*

Although the video frame rate limited precision to 0.03 s, we accounted for this error when calculating the speeds, showing that the upper and lower average error estimates were marginal and did not affect the quality of the analyses. In addition, the distances between the various points, though calculated precisely, taking into account the actual trajectories of the riders and their bikes, may contain some errors due to the different angles of the footage. Overall, our analysis allowed us to accurately describe the dynamics of the accident and its potentially catastrophic effects, while clearing up doubts regarding the speeds, distances, and trajectories of the riders who were involved.

#### **5. Conclusions**

This study, through a quali-quantitative analysis, helps us to gain a deeper understanding of the main causes and dynamics of the accident at the 2020 Austrian MotoGP Grand Prix, as well as the speeds of the motorcycles involved. Furthermore, we showed how V. Rossi narrowly avoided being struck by Morbidelli's bike and what the consequences of other potentially tragic scenarios would have been if Rossi had braked differently or if he simply had not had his teammate in front of him. In addition, we have shown that, in the Austrian circuit, overtaking on the outside of the track in the straightaway between turn 1 and turn 2 is very risky, in particular, if it is completed at the end of turn 2 or at the point we have identified as point A. Hence, in light of our findings, regarding the riskiness of such a maneuver, it may be advisable for riders to be particularly prudent when attempting such a maneuver or not to repeat the same or a similar trajectory in this section of the Red Bull Ring circuit to overtake another rider in future races. Overall, this method could be used in future investigations to

improve safety conditions and to gain a better understanding of the particular dynamics of specific sport motorcycle accidents.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-4601/17/21/7989/s1, Video S1: AUSGP2020.

**Author Contributions:** Conceptualization, M.G. and F.P.; methodology, M.G.; software, M.G.; formal analysis, M.G.; investigation, M.G.; resources, M.G.; E.G. and F.P.; data curation, M.G.; writing—original draft preparation, M.G.; E.G., V.N., and F.P; writing—review and editing, M.G.; E.G., V.N., S.A., and F.P.; supervision, F.P.; project administration, M.G.; funding acquisition, M.G. and F.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to thank Piero Sestili for his efforts during the project.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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