3. Gasoline-CNG mixtures in RCCI combustion system

The gasoline-CNG mixtures performance and combustion in RCCI combustion system from both methods are elaborated in this section. It explained the parameters affecting the combustion of low-reactive fuels and method in controlling the combustion process.

## 3.1. Gasoline-CNG combustion behavior in RCCI combustion-based engine

The degree of stratification of CNG in the total mixture was found to have significant effects on the maximum load in terms of the IMEP attainable and φTotal. The degree of stratification is determined by the injection timing with 300 BTDC representing the homogeneous mixture, while 120 BTDC represents the stratified mixture. The 300 BTDC has a very early injection timing, and the fuel is injected during intake valve is open. It is, therefore, the fuel that has sufficient time to be completely mixed with air and create a homogeneous mixture. While on the other hand, for 120 BTDC, the fuel is injected after intake valve closed and mixing time between fuel and air is very short and does not allow a complete mixing to take place.

Figure 11 shows that at 300 and 240 BTDC injection higher total equivalence ratios could be operated. But with 180 and 120 BTDC, the maximum operable φTotal was limited with reduced IMEP at a given φTotal when compared to the 300 and 240 BTDC cases.

The IMEP results show agreement with investigation from Genchi G and Pipitone E [22] where the increased composition of CNG produces higher IMEP. With the highest degree of stratification, although the maximum load was limited, there was no significant drop in the IMEP and the trend was similar to 300 and 240 BTDC conditions. The corresponding values of indicated thermal efficiencies are shown in Figure 12. The maximum load was observed to be limited by knocking when CNG was injected at 300 BTDC, and for the other cases, increasing CNG injection rate led to unstable operation or misfire.

Figure 11. Effect of degree of stratification of CNG on IMEP. (K—limited by knocking; mf—limited by misfire).

Figure 10. Injector control system interface.

Figure 9. Schematic diagram of the control system and data acquisition.

62 Improvement Trends for Internal Combustion Engines

From Figure 13, it can be seen that the ignition timing could be altered by changing the timing of injection of CNG at a given load. The ignition timing was determined by identifying the start of heat released rate and mass fraction burned derived from the pressure data where the 0% points before the continuous propagation of the mass fraction burned is determined as the start of ignition of the analyzed combustion cycles.

When the rate of CNG injection was increased, the ignition timing was delayed due to the higher octane number of CNG. Also, higher degrees of stratification resulted in higher increments in the delay of ignition timing as the CNG injection rate was increased. The slope of the curves was steeper when the injection timing was delayed. For a given increase in the CNG injection rate, the increase in delay in ignition timing was higher when the degree of stratification was increased. That is both the injection rate and the degree of stratification of CNG had significant effects on the ignition timing when operated with φg = 0.20. However, the maximum total equivalence

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It was found that the combustion duration was reduced when CNG injection rate was increased at 300, 240, and 80 BTDC. When CNG was injected at 180 and 120 BTDC, the combustion duration was marginally affected and it initially decreased up to certain values of

Figures 15–18 show the rate of heat release and pressure rise at various injection timings. Increasing the rate of CNG injection at 300 BTDC was limited by knocking as shown in Figure 14. But with later injection timings, with φg = 0.20, any increase in CNG injection rate resulted in a delayed autoignition and reduced peak pressure. Therefore, increasing CNG injection rate beyond certain levels led to misfire or no fire, thereby defining the maximum

As shown in Figure 15, when CNG injection rate was increased, it resulted in delayed ignition timing. Up to φTotal = 0.33, the resultant peak pressure increased, and with a further increase in CNG injection rate, it decreased. Also, above φ Total = 0.33, the delay in ignition timing was more significant and resulted in decreased peak pressures. As will be discussed later in this section, combustion efficiency of both the fuels increased and CH4 emissions decreased with

ratio was less than that obtained with CNG injection at 300 and 240 BTDC.

Figure 14. Effect of degree of CNG stratification of CNG on the combustion duration.

an increase in φTotal above 0.33 as shown in Figures 23 and 29.

CNG injection rate and then it increased again.

load limit.

Figure 12. Effect degree of CNG stratification on the indicated thermal efficiency.

Figure 13. Effect of degree of CNG stratification on ignition timing.

Figure 14. Effect of degree of CNG stratification of CNG on the combustion duration.

From Figure 13, it can be seen that the ignition timing could be altered by changing the timing of injection of CNG at a given load. The ignition timing was determined by identifying the start of heat released rate and mass fraction burned derived from the pressure data where the 0% points before the continuous propagation of the mass fraction burned is determined as

the start of ignition of the analyzed combustion cycles.

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Figure 12. Effect degree of CNG stratification on the indicated thermal efficiency.

Figure 13. Effect of degree of CNG stratification on ignition timing.

When the rate of CNG injection was increased, the ignition timing was delayed due to the higher octane number of CNG. Also, higher degrees of stratification resulted in higher increments in the delay of ignition timing as the CNG injection rate was increased. The slope of the curves was steeper when the injection timing was delayed. For a given increase in the CNG injection rate, the increase in delay in ignition timing was higher when the degree of stratification was increased. That is both the injection rate and the degree of stratification of CNG had significant effects on the ignition timing when operated with φg = 0.20. However, the maximum total equivalence ratio was less than that obtained with CNG injection at 300 and 240 BTDC.

It was found that the combustion duration was reduced when CNG injection rate was increased at 300, 240, and 80 BTDC. When CNG was injected at 180 and 120 BTDC, the combustion duration was marginally affected and it initially decreased up to certain values of CNG injection rate and then it increased again.

Figures 15–18 show the rate of heat release and pressure rise at various injection timings. Increasing the rate of CNG injection at 300 BTDC was limited by knocking as shown in Figure 14. But with later injection timings, with φg = 0.20, any increase in CNG injection rate resulted in a delayed autoignition and reduced peak pressure. Therefore, increasing CNG injection rate beyond certain levels led to misfire or no fire, thereby defining the maximum load limit.

As shown in Figure 15, when CNG injection rate was increased, it resulted in delayed ignition timing. Up to φTotal = 0.33, the resultant peak pressure increased, and with a further increase in CNG injection rate, it decreased. Also, above φ Total = 0.33, the delay in ignition timing was more significant and resulted in decreased peak pressures. As will be discussed later in this section, combustion efficiency of both the fuels increased and CH4 emissions decreased with an increase in φTotal above 0.33 as shown in Figures 23 and 29.

Figure 15. Pressure history and heat release rates with CNG injection at 240 BTDC.

Therefore, it can be concluded that, above φTotal = 0.33, the peak pressure was reduced due to delayed ignition, and the combustion was more complete with increase in injection rate at 240 BTDC. That is, increasing φg above 0.33 resulted in reduced peak pressures without a decrease in thermal efficiency as shown in Figure 12. Heat release rates increased with an increase in CNG injection rate up to φTotal = 0.40 above which it reduced again. Above φTotal = 0.42, increasing CNG injection rate resulted in misfire or no fire, and both gasoline and CNG combustion was quenched.

emissions as shown in Figures 23 and 29. This suggests that the degree of stratification created at 180 BTDC injection results in deterioration in combustion and leads to decrease in thermal efficiency as shown in Figure 12. Similar, trends were observed with CNG injection at 120

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When CNG injection was retarded to 80 BTDC, increase in injection rate resulted in significant delay in ignition; however, there was less noticeable effect on peak pressures up to φTotal = 0.28. Increasing φTotal above resulted in a more significant delay in ignition and peak pressure, and heat release rates increased. Thermal efficiency and combustion efficiency increased primarily due to remarkable increase in completeness of CNG combustion as suggested by CH4 emissions

As shown in Figure 19, increasing CNG injection rate at 240 BTDC resulted in delayed ignition. At φTotal = 0.28 and 0.33, there was a slight increase in burning rate at the last stage of combustion compared to combustion with pure gasoline. At 180 and 120 BTDC, there was no significant effect on the burning rate of the fuels due to increase in CNG injection rate, but it caused a significant delay in ignition as shown in Figures 20 and 21. Similar results were obtained with CNG injection at 80 BTDC; however, at φTotal = 0.28 and 0.33, the combustion

was slower at the initial stages and was faster at latter stages as shown in Figure 22.

BTDC when φTotal was increased above 0.24 as shown in Figure 16.

Figure 16. Pressure history and heat release rates with CNG injection at 120 BTDC.

as shown in Figure 29.

With CNG injection at 180 BTDC, increase in CNG injection rate resulted in a more significant delay in ignition timing. There was a marginal increase in peak pressure when φTotal was increased to 0.26 above which it reduced again. Thermal efficiency and combustion efficiency increased primarily due to the remarkable increase in the completeness of CNG combustion as suggested by CH4 emissions as shown in Figure 29. Heat release rate increased with an increase in CNG injection rate as shown in Figure 17. However, increasing CNG injection rate above φTotal = 0.26 resulted in decreased overall combustion efficiency and high CH4

Figure 16. Pressure history and heat release rates with CNG injection at 120 BTDC.

Therefore, it can be concluded that, above φTotal = 0.33, the peak pressure was reduced due to delayed ignition, and the combustion was more complete with increase in injection rate at 240 BTDC. That is, increasing φg above 0.33 resulted in reduced peak pressures without a decrease in thermal efficiency as shown in Figure 12. Heat release rates increased with an increase in CNG injection rate up to φTotal = 0.40 above which it reduced again. Above φTotal = 0.42, increasing CNG injection rate resulted in misfire or no fire, and both gasoline and CNG

Figure 15. Pressure history and heat release rates with CNG injection at 240 BTDC.

With CNG injection at 180 BTDC, increase in CNG injection rate resulted in a more significant delay in ignition timing. There was a marginal increase in peak pressure when φTotal was increased to 0.26 above which it reduced again. Thermal efficiency and combustion efficiency increased primarily due to the remarkable increase in the completeness of CNG combustion as suggested by CH4 emissions as shown in Figure 29. Heat release rate increased with an increase in CNG injection rate as shown in Figure 17. However, increasing CNG injection rate above φTotal = 0.26 resulted in decreased overall combustion efficiency and high CH4

combustion was quenched.

66 Improvement Trends for Internal Combustion Engines

emissions as shown in Figures 23 and 29. This suggests that the degree of stratification created at 180 BTDC injection results in deterioration in combustion and leads to decrease in thermal efficiency as shown in Figure 12. Similar, trends were observed with CNG injection at 120 BTDC when φTotal was increased above 0.24 as shown in Figure 16.

When CNG injection was retarded to 80 BTDC, increase in injection rate resulted in significant delay in ignition; however, there was less noticeable effect on peak pressures up to φTotal = 0.28. Increasing φTotal above resulted in a more significant delay in ignition and peak pressure, and heat release rates increased. Thermal efficiency and combustion efficiency increased primarily due to remarkable increase in completeness of CNG combustion as suggested by CH4 emissions as shown in Figure 29.

As shown in Figure 19, increasing CNG injection rate at 240 BTDC resulted in delayed ignition. At φTotal = 0.28 and 0.33, there was a slight increase in burning rate at the last stage of combustion compared to combustion with pure gasoline. At 180 and 120 BTDC, there was no significant effect on the burning rate of the fuels due to increase in CNG injection rate, but it caused a significant delay in ignition as shown in Figures 20 and 21. Similar results were obtained with CNG injection at 80 BTDC; however, at φTotal = 0.28 and 0.33, the combustion was slower at the initial stages and was faster at latter stages as shown in Figure 22.

again. That is, up to a certain value of CNG injection rate, CNG reduced the combustion

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temperature and led to formation of higher amounts of NO2.

Figure 18. Pressure history and heat release rates with CNG injection at 80 BTDC.

Figure 19. Mass fractions burned with CNG injection at 240 BTDC.

Figure 17. Pressure history and heat release rates with CNG injection at 180 BTDC.

As shown in Figure 23, with an increase in φTotal by CNG injection at 300, 240, and 80 BTDC, combustion efficiency increased. The highest increment was obtained with CNG injection at 80 BTDC for a given increase in φTotal due to mixture stratification. However, CNG injection at 180 and 120 BTDC, combustion efficiency increased initially but decreased again and was below 80% for all φTotal.

The exhaust gas temperature was observed to increase as the CNG injection rate was increased as shown in Figure 24. The increase in exhaust gas temperature with increasing CNG injection rates at 180 and 120 BTDC was less than that observed with increasing CNG injection rates at 300, 240, and 80 BTDC. When the fuels were homogeneously mixed, it resulted in higher exhaust gas temperatures due to rapid burning. Similarly, when CNG was highly stratified, it also led to higher exhaust gas temperatures.

Figure 25 shows the indicated specific NOx (ISNOx) emissions. The NOx emissions were marginally affected and were around the same levels for all test conditions. However, different trends were observed at different injection timings and CNG injection rates.

Increasing the CNG injection rate resulted in a drastic increase in the NO2/NOx ratio up to a certain point and then it decreased. As shown in Figure 26, the ratio of NO2/NOx almost doubled when the CNG injection rate was increased to around φTotal = 0.33 before decreasing again. That is, up to a certain value of CNG injection rate, CNG reduced the combustion temperature and led to formation of higher amounts of NO2.

Figure 18. Pressure history and heat release rates with CNG injection at 80 BTDC.

As shown in Figure 23, with an increase in φTotal by CNG injection at 300, 240, and 80 BTDC, combustion efficiency increased. The highest increment was obtained with CNG injection at 80 BTDC for a given increase in φTotal due to mixture stratification. However, CNG injection at 180 and 120 BTDC, combustion efficiency increased initially but decreased again and was

The exhaust gas temperature was observed to increase as the CNG injection rate was increased as shown in Figure 24. The increase in exhaust gas temperature with increasing CNG injection rates at 180 and 120 BTDC was less than that observed with increasing CNG injection rates at 300, 240, and 80 BTDC. When the fuels were homogeneously mixed, it resulted in higher exhaust gas temperatures due to rapid burning. Similarly, when CNG was highly stratified, it

Figure 25 shows the indicated specific NOx (ISNOx) emissions. The NOx emissions were marginally affected and were around the same levels for all test conditions. However, different

Increasing the CNG injection rate resulted in a drastic increase in the NO2/NOx ratio up to a certain point and then it decreased. As shown in Figure 26, the ratio of NO2/NOx almost doubled when the CNG injection rate was increased to around φTotal = 0.33 before decreasing

trends were observed at different injection timings and CNG injection rates.

Figure 17. Pressure history and heat release rates with CNG injection at 180 BTDC.

below 80% for all φTotal.

68 Improvement Trends for Internal Combustion Engines

also led to higher exhaust gas temperatures.

Figure 19. Mass fractions burned with CNG injection at 240 BTDC.

Figure 20. Mass fractions burned with CNG injection at 180 BTDC.

Figure 21. Mass fractions burned with CNG injection at 120 BTDC.

The indicated specific CO (ISCO) emissions were reduced significantly as the mixture was enriched with CNG by direct injection at all injection timings as shown in Figure 27. However, the reduction obtained was the highest when CNG was injected at 300 and 240 BTDC. Any increase in CNG injection rate at later injection timings resulted in less reductions in CO emissions. The lowest reduction was obtained at the injection timing of 80 BTDC, as the high degree of stratification of CNG limited the availability and distribution of oxygen and temperature differences in the CNG and air particles.

injection at 80 BTDC. Higher degrees of stratification of CNG resulted in more complete

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Figure 29 shows the mass ratio of flow rates of CH4 in the exhaust emissions and mass flow rate of CNG injected into the cylinder. At a given constant gasoline equivalence ratio of φg = 0.20, CNG direct injection at 80 BTDC resulted in the least emission of CH4. Therefore, the combustion of CNG was more complete when it was stratified. CNG injection at 300 and 240 BTDC resulted in moderate levels of CH4 emissions, and highest values were obtained with CNG injection at 180 BTDC. This was due to the turbulence created and mixing condi-

tions in the cylinder when the piston changed its direction at 180 BTDC.

Figure 22. Mass fractions burned with CNG injection at 80 BTDC.

Figure 23. Effect of degree of stratification on combustion efficiency.

combustion.

The HC emissions were found to be significantly affected by the degree of stratification of CNG as shown in Figure 28. The highest reduction in HC emissions was obtained with CNG Reactivity Controlled Compression Ignition (RCCI) of Gasoline-CNG Mixtures http://dx.doi.org/10.5772/intechopen.72880 71

Figure 22. Mass fractions burned with CNG injection at 80 BTDC.

Figure 23. Effect of degree of stratification on combustion efficiency.

The indicated specific CO (ISCO) emissions were reduced significantly as the mixture was enriched with CNG by direct injection at all injection timings as shown in Figure 27. However, the reduction obtained was the highest when CNG was injected at 300 and 240 BTDC. Any increase in CNG injection rate at later injection timings resulted in less reductions in CO emissions. The lowest reduction was obtained at the injection timing of 80 BTDC, as the high degree of stratification of CNG limited the availability and distribution of oxygen and temper-

The HC emissions were found to be significantly affected by the degree of stratification of CNG as shown in Figure 28. The highest reduction in HC emissions was obtained with CNG

ature differences in the CNG and air particles.

Figure 21. Mass fractions burned with CNG injection at 120 BTDC.

Figure 20. Mass fractions burned with CNG injection at 180 BTDC.

70 Improvement Trends for Internal Combustion Engines

injection at 80 BTDC. Higher degrees of stratification of CNG resulted in more complete combustion.

Figure 29 shows the mass ratio of flow rates of CH4 in the exhaust emissions and mass flow rate of CNG injected into the cylinder. At a given constant gasoline equivalence ratio of φg = 0.20, CNG direct injection at 80 BTDC resulted in the least emission of CH4. Therefore, the combustion of CNG was more complete when it was stratified. CNG injection at 300 and 240 BTDC resulted in moderate levels of CH4 emissions, and highest values were obtained with CNG injection at 180 BTDC. This was due to the turbulence created and mixing conditions in the cylinder when the piston changed its direction at 180 BTDC.

Figure 24. Effect of CNG injection on the exhaust gas temperature.

Figure 25. Effect of degree of CNG stratification on NOx emission.

#### 3.2. Gasoline-CNG combustion behavior in constant volume combustion chamber

The effect of injection gap on the gasoline-compressed natural gas mixture (GCNG) mixture combustion is discussed below. The injection gap alteration gave direct impact on the mixture distribution inside the combustion chamber. There are five injection gaps tested, 0, 5, 10, 15, and 20 ms. These injection gaps are expected to be able to give direct control to the mixture distribution inside the chamber.

all combustion stages. While on the contrary, longer injection gap reduces the combustion efficiency, maximum pressure, THR and longer combustion delay for 90% GCNG. However, the trends of the combustion duration are similar, longer duration for longer injection gap. Figure 31 confirmed the variation of injection gap effect to the combustion process of the GCNG mixture. The turning point is shown between 70 and 80% GCNG mixture compositions. For all the mixture above 80% shows decreasing combustion efficiency with the increase in injection gaps which is contrary with the mixtures below 70% that show an increment of combustion efficiency with the increase of injection gaps. These differences

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may cause by the mixture distribution inside the chamber.

Figure 27. Effect of degree of CNG stratification on CO emissions.

Figure 26. Effect of degree of CNG stratification on NO2 formation.

The effect of injection gaps is shown in Figure 30. It shows two mixture compositions, 50 and 90% GCNG composition. The injection gap gives different effect between the two compositions. In 50% GCNG, longer injection gap gives higher combustion efficiency, maximum pressure, total heat released (THR) and shorter delay. Furthermore, it also shows longer duration for Reactivity Controlled Compression Ignition (RCCI) of Gasoline-CNG Mixtures http://dx.doi.org/10.5772/intechopen.72880 73

Figure 26. Effect of degree of CNG stratification on NO2 formation.

Figure 27. Effect of degree of CNG stratification on CO emissions.

3.2. Gasoline-CNG combustion behavior in constant volume combustion chamber

distribution inside the chamber.

Figure 24. Effect of CNG injection on the exhaust gas temperature.

72 Improvement Trends for Internal Combustion Engines

Figure 25. Effect of degree of CNG stratification on NOx emission.

The effect of injection gap on the gasoline-compressed natural gas mixture (GCNG) mixture combustion is discussed below. The injection gap alteration gave direct impact on the mixture distribution inside the combustion chamber. There are five injection gaps tested, 0, 5, 10, 15, and 20 ms. These injection gaps are expected to be able to give direct control to the mixture

The effect of injection gaps is shown in Figure 30. It shows two mixture compositions, 50 and 90% GCNG composition. The injection gap gives different effect between the two compositions. In 50% GCNG, longer injection gap gives higher combustion efficiency, maximum pressure, total heat released (THR) and shorter delay. Furthermore, it also shows longer duration for all combustion stages. While on the contrary, longer injection gap reduces the combustion efficiency, maximum pressure, THR and longer combustion delay for 90% GCNG. However, the trends of the combustion duration are similar, longer duration for longer injection gap. Figure 31 confirmed the variation of injection gap effect to the combustion process of the GCNG mixture. The turning point is shown between 70 and 80% GCNG mixture compositions. For all the mixture above 80% shows decreasing combustion efficiency with the increase in injection gaps which is contrary with the mixtures below 70% that show an increment of combustion efficiency with the increase of injection gaps. These differences may cause by the mixture distribution inside the chamber.

Figure 28. Effect of degree of CNG stratification on HC emissions.

Figure 30. Effect of injection gaps on the combustion characteristics of GCNG mixture for 50 and 90% composition at

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Figure 31. Effect of injection gap to the combustion efficiency for various mixture compositions at lambda 1.

lambda 1.

Figure 29. Effect of injection timing on the emission of CH4 with φg = 0.20.

The mixture distribution inside the chamber for GCNG mixture 30 and 90% with 0 and 20 ms injection gaps are depicted in Figure 32. In the figure, highly stratified mixture for 30% GCNG mixture with 0 ms injection gap. The stratification is marked by darker color on the bottom of the chamber that indicates high density fluid (gasoline). The image shows that most of the gasoline was collected at the bottom of the chamber because of the momentum of CNG injection that prevents the gasoline from reaching the top side of the chamber. 20 ms injection gap, on the other hand, shows better fuel mixing shown by fairly similar image intensity throughout the chamber.

The injection gaps for 30% GCNG mixture improve the mixing rate thus increase the combustion performance. Furthermore, the gasoline fuel is mainly accumulating at the bottom side

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Figure 30. Effect of injection gaps on the combustion characteristics of GCNG mixture for 50 and 90% composition at lambda 1.

The mixture distribution inside the chamber for GCNG mixture 30 and 90% with 0 and 20 ms injection gaps are depicted in Figure 32. In the figure, highly stratified mixture for 30% GCNG mixture with 0 ms injection gap. The stratification is marked by darker color on the bottom of the chamber that indicates high density fluid (gasoline). The image shows that most of the gasoline was collected at the bottom of the chamber because of the momentum of CNG injection that prevents the gasoline from reaching the top side of the chamber. 20 ms injection gap, on the other hand, shows better fuel mixing shown by fairly similar image intensity

The injection gaps for 30% GCNG mixture improve the mixing rate thus increase the combustion performance. Furthermore, the gasoline fuel is mainly accumulating at the bottom side

throughout the chamber.

Figure 28. Effect of degree of CNG stratification on HC emissions.

74 Improvement Trends for Internal Combustion Engines

Figure 29. Effect of injection timing on the emission of CH4 with φg = 0.20.

Figure 31. Effect of injection gap to the combustion efficiency for various mixture compositions at lambda 1.

Figure 32. Mixture distribution for 30 and 90% mixture composition at 0 and 20 ms injection gaps.

which also has average low temperature compared to the top one. It makes the vaporization rate of the gasoline took longer time which also elongates the combustion delay as shown in Figure 33.

shows that 0 ms injection gap have higher gasoline population compared to 20 ms injection gap thus the longer time required for vaporization process. This is the main reason for the lower combustion output as well as longer combustion delay for 0 ms compared to 20 ms

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Figure 34. Combustion sequence for GCNG at 60/40 mixture composition and lambda 1.

The combustion sequence for GCNG at 0 and 20 ms injection gaps is depicted in Figure 34. The flame speed of 20 ms injection gap is faster than 0 ms injection gap with 37.02 m/s at the first 0.5 ms and 15.9 at the first 1 ms after start of combustion (SOC), while 0 ms injection gap with 30.56 m/s and 16.9 m/s at 0.5 ms and 1 ms, respectively. Figure 34 also reveals the difference in the flame color for the two injection gaps. A 20 ms injection gap shows light blue color yet with

injection gap.

Figure 33. Combustion delay relative to SOI.

The injection gaps at 90% GCNG mixture, on the other hand, have similar liquid fuel distribution as in Figure 32 where both injection gaps show concentrated fuel distribution at the top of the chamber. In spite of the similarity, the 0 ms injection gap shows the higher intensity of the liquid fuel (darker region) on the top side of the chamber compared to 20 ms injection gap. It Reactivity Controlled Compression Ignition (RCCI) of Gasoline-CNG Mixtures http://dx.doi.org/10.5772/intechopen.72880 77

Figure 33. Combustion delay relative to SOI.

which also has average low temperature compared to the top one. It makes the vaporization rate of the gasoline took longer time which also elongates the combustion delay as shown in

Figure 32. Mixture distribution for 30 and 90% mixture composition at 0 and 20 ms injection gaps.

The injection gaps at 90% GCNG mixture, on the other hand, have similar liquid fuel distribution as in Figure 32 where both injection gaps show concentrated fuel distribution at the top of the chamber. In spite of the similarity, the 0 ms injection gap shows the higher intensity of the liquid fuel (darker region) on the top side of the chamber compared to 20 ms injection gap. It

Figure 33.

76 Improvement Trends for Internal Combustion Engines

Figure 34. Combustion sequence for GCNG at 60/40 mixture composition and lambda 1.

shows that 0 ms injection gap have higher gasoline population compared to 20 ms injection gap thus the longer time required for vaporization process. This is the main reason for the lower combustion output as well as longer combustion delay for 0 ms compared to 20 ms injection gap.

The combustion sequence for GCNG at 0 and 20 ms injection gaps is depicted in Figure 34. The flame speed of 20 ms injection gap is faster than 0 ms injection gap with 37.02 m/s at the first 0.5 ms and 15.9 at the first 1 ms after start of combustion (SOC), while 0 ms injection gap with 30.56 m/s and 16.9 m/s at 0.5 ms and 1 ms, respectively. Figure 34 also reveals the difference in the flame color for the two injection gaps. A 20 ms injection gap shows light blue color yet with

stages, while homogeneous mixture produces longer duration. This trend is applicable for

\*, Morgan Raymond Heikal1,3,

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both GCNG and DCNG mixtures.

, Abdul Rashid Abdul Aziz2

\*Address all correspondence to<EMAIL_ADDRESS>
3 Advanced Engineering Centre, University of Brighton, UK

ignition gasoline engine. JSAE Review. 1995;16(3):314

HCCI engine operating on a two-component fuel. SAE. 2010

SAE Technical Paper Series. vol. 2002-01-01; 2002

loads. Applied Energy. 2016;175:389-402

Journal of Engine Research. 2011;12(3):209-226

sandia.gov/ecn/tutorials/visualization.php

01-36; 1999. pp. 1999-01-3682

Ezrann Zharif Zainal Abidin<sup>1</sup> and Naveenchandran Panchatcharam<sup>4</sup>

1 Centre for Automotive Research and Electric Mobility, Universiti Teknologi Petronas,

[1] Aoyama T, Hattori Y, Mizuta J. An experimental study on premixed-charge compression

[2] Kokjohn SL, Hanson RM, Splitter D a, Reitz RD. Fuel reactivity controlled compression ignition (RCCI): A pathway to controlled high-efficiency clean combustion. International

[3] Musculus M. Combustion Regime Visualization. 2011. [Online]. Available: http://www.

[4] Stanglmaier RH, Roberts CE. Homogeneous charge compression ignition (HCCI): Benefits, compromises, and future engine applications. SAE Technical Paper Series. vol. 1999-

[5] Saitou K, Iijima A, Otagiri Y, Yoshida K, et al. A study of ignition characteristics of an

[6] Sjöberg M, Lars-Olof E, Eliassen T, Magnusson L, Ãngstrom HE. GDI HCCI: Effects of injection timing and air swirl on fuel stratification, combustion and emission formation.

[7] Agarwal AK, Singh AP, Maurya RK. Evolution, challenges and path forward for low temperature combustion engines. Progress in Energy and Combustion Science. 2017;61:1-56

[8] Wang Y, Yao M, Li T, Zhang W, Zheng Z. A parametric study for enabling reactivity controlled compression ignition (RCCI) operation in diesel engines at various engine

2 Institute of Transport and Infrastructure, Universiti Teknologi Petronas, Malaysia

Author details

Firmansyah<sup>1</sup>

Malaysia

References

4 Bharath University, India

Figure 35. Effect of injection gap for 60/40 gasoline/CNG composition at lambda 1.

lower intensity compared to 100% gasoline, while 0 ms injection gap shows yellow color. It can be assumed that the blue color is the product from the same reaction that generates hydroxyl peroxide and increases the combustion output of the mixture.

In 100% gasoline combustion, the blue flame occurs because of the homogeneous mixture that creates multipoint combustion behind the flame front that significantly increases the combustion output. A similar process occurs in the 20 ms injection gaps, mixture homogeneity is achieved with this mixture as the effect of CNG injection shown by longer combustion delay relative to the start of injection as depicted in Figure 35.

Injection gaps proved to have a direct influence on the fuel distribution inside the chamber, thus affecting the combustion characteristics of the mixture. The combustion process in the CVC is mostly affected by the characteristics of the fuel distribution inside the chamber at the time the combustion occurs. The injection gaps, in this case, highly affect the mixture distribution inside the chamber where longer gap promotes the mixing and create a mixture that is more homogeneous.


*2.3. Soil Parameters and Its Activity Concentrations of 226Ra and 228Ra*

Five soil core samples from the ground surface to 5 cm depth were collected using a stainless-steel soil sampler, which had a volume of 100 mL [26]. Dry bulk density, soil particle density, porosity, and soil textures were evaluated after drying samples for 24 h at 110 ◦C. The dry bulk density *ρ*b was calculated as the mass of the dried soil divided by the soil volume. The soil particle density *ρ*s was evaluated using a specific gravity bottle according to the test procedure of Japan Industrial Standards (JIS) A1202 [27]. The porosity ε was calculated by *ε* = 1−*ρ*b/*ρ*s. Soil particle size distribution was evaluated using a standard stainless-steel sieve for 0.075–2.0 mm particle size range and the sedimentation analysis for particles below 0.075 mm was made according to the test procedure of JIS A1204 [28] to determine the soil textures of the samples. The percentages of sand, silt, and clay for each soil sample were evaluated using the sample particle size distribution curves.

The activity concentrations of 226Ra and 228Ra were evaluated using a high-purity germanium semiconductor detector (GEM-100210, ORTEC, USA) with a relative efficiency of 30%, 1.85 keV energy resolution (FWHM) at 1.33 MeV of 60Co. The efficiency calibration of the detector was made using the standard volumetric sources which are contained 109Cd, 57Co, 139Ce, 51Cr, 85Sr, 137Cs, 54Mn, 88Y, and 60Co supplied by Japan Radioisotope Association. The detector was enclosed in a shielding made out of compacted lead of 10 cm of thickness. Each soil sample was enclosed in a cylindrical polypropylene container (U8 type container, 100 cm3) after drying for 24 h at 110 ◦C. The prepared soil sample was then enclosed in an air-tight container for 40 days to allow radioactive equilibrium between 226Ra and 222Rn to be reached. The measurement time was set as 80,000 s. In this study, the weighted average concentration of 214Pb and 214Bi were used as the 226Ra concentration in the soil samples by counting photons in the photoelectric peak channels of 352 keV for 214Pb and 609 keV for 214Bi. 228Ra was measured by counting photons in the photoelectric peak channel of 911 keV for 228Ac. The uncertainty for the activity concentration was evaluated taking into account the uncertainties of the counts for the sample and background. Coincidence summing, self-attenuation and decay corrections were applied using software (Gamma Studio, SEIKO EG&G, Tokyo, Japan).

#### *2.4. Comparison of the Exhalation Rates with the Other Methods*

#### 2.4.1. Accumulation Chamber with Scintillation Cell

The stainless-steel accumulation chamber was set on the ground surface. Radon gas exhaled from the ground was accumulated for 1.5 to 3 h. Then, the radon gas inside the accumulation chamber was collected into a scintillation cell (Pylon 300A, Pylon Electrics, Inc., Toronto, Ontario Canada) at a sampling flow rate of 0.5 L min−1 and a sampling time of 5 min. After 3.5 h, the alpha counts from radon gas in the scintillation cell were

measured using a portable radiation monitor (AB-5, Pylon Electrics, Inc., Toronto, Ontario Canada) [29]. The radon exhalation rate by grab sampling can be calculated by applying Equation (5).

$$E\_{\rm Rn} = \frac{(N - N\_{\rm b}) \cdot CF \cdot V \cdot \lambda\_{\rm Rn}}{S \cdot \left[1 - \exp\left(-\lambda\_{\rm Rn} \cdot t\right)\right]} \tag{5}$$

Here, *N* and *N*b are the count rates of the sample and background (cpm), *CF* is the conversion factor from count rate to radon concentration (27.0 Bq m−3 cpm−1) [30], *V* is the volume of the accumulation chamber (1.4 × 10−2 m3), *λ*Rn is the decay constant of radon (2.1 × 10−6 s−1), *S* is the area under the accumulation chamber (9.9 × 10−2 m2), and *T* is the accumulation time (s).

#### 2.4.2. In Situ Radon and Thoron Exhalation Rate Monitor

Radon and thoron exhalation rates from the ground was also measured with an *in-situ* radon and thoron exhalation rate monitor (MSZ). The details of the method have been described by Saegusa et al. [31]. The monitor was composed of an accumulation chamber (volume, 13 L), a ZnS(Ag) scintillation detector with an aluminized mylar sheet, a light guide, a photomultiplier tube, a pulse counting part and scaler, and a timer. The area of an acrylic board coated with ZnS(Ag) scintillator was 0.12 m2. Count rates were recorded over consecutive 30-s intervals during a total recording period of 30 min after the monitor was set up on the ground. The conversion factors from count rates of 10 min and 30 min to exhalation rates of radon and thoron were 0.521 ± 0.040 mBq m−2 s−1 cpm−1 and 18.1 ± 3.2 mBq m−2 s−1 cpm−1, respectively. The measurement uncertainties for radon and thoron exhalation rates using the MSZ have been reported as ~20% and ~6%, respectively [11].

#### **3. Results and Discussion**


2.1 Structural design of DH coil

In addition to these practical difficulties, the usage of a solenoidal coil for wraparound implants suffers from another implementational problem. In most cases, it is desirable to use a flexible PSC for the power transmitter because this planar coil can be easily integrated with a garment, a blanket or a bedding sheet, providing a high convenience for unobtrusive recharging of the implant. In order to achieve the maximum coupling between the transmission and reception coils, the solenoidal coil of the implant is expected to be oriented perpendicularly to the body surface (Figure 2a). This requirement is highly problematic because, as indicated by human anatomy [17], many tubular organs within the body are oriented parallel to the body surface, which provides the worst orientation to the PSC for power

To address these significant problems, we have developed an air-core DH coil for tubular implants [18, 19]. The new coil can be printed on a flat flexible printed circuit board (PCB) and installed on a tubular biostructure during surgery. As shown in Figure 3, when the DH coil is wrapped around a tubular organ parallel to the skin surface, the optimal coupling can be achieved with a PSC integrated with a

The implanted solenoid coil (a) perpendicular and (b) parallel to a planar transmitter (shown in blue).

A DH coil is wrapped around a biological structure (red curve) to serve as a power receiver.

transmission because of the weakest magnetic coupling (Figure 2b).

garment or a bedding sheet.

Recent Wireless Power Transfer Technologies

Figure 2.

Figure 3.

72

Figure 4a shows a set of parallel sinusoidal wires printed on both sides of a flexible PCB. This PCB is made of polyimide film which is characterized by high strength, low RF energy loss, small thickness, and high flexibility. The wires on two sides are connected to each other in series via a column of through holes (one of them is shown), forming closed loops. The current paths are denoted by the black arrows in Figure 4a. In addition to the DH coil, sensors, actuators, microprocessor and electronic elements (not shown) can be installed on the same flexible PCB. During surgery, the hermetically sealed PCB (using a biocompatible polymer material) is wrapped around the tubular structure at the position of interest forming a double helix winding along with all electronic components, as shown in Figure 4b. It can be observed that, after the tubular structure is formed, the closed loops on the two sides of the PCB form opposite tilt angles but maintain the clockwise/counterclockwise current direction.

The two-layer structure in Figure 4 can be extended to a multiple-layer structure using a PCB with more than two layers. Similarly, the 45° tilted angle in each layer can be modified to adapt to specific tubular organ orientation for optimal wireless power delivery.


**2. Related work**

There are already some approaches to network security SA: In the research of network security SA architecture, Kokkonen proposed in 2016 a network security SA architecture, which mainly includes information exchange module and emphasizes standardized information format [4]. In 2017, Eiseler proposed a network security SA architecture from the perspective of IT complexity [5]. The main idea is to abstract a layer of operation (decision) and the result of decision for decision makers from non-technical background. In the research of network security SA, in 2016, Yang et al. used SVM machine learning method for SA [6]. After being trained by classifier, the data can be used to predict the situation value. But the method has the defect that the situation is normalized and the information is not abundant enough. In 2016, KHALID et al., targeting data injection attacks, could lead to unreliability and insecurity of network physical infrastructure such as (WAMS),

### *Evaluation of Botnet Threats Based on Evidence Chain DOI: http://dx.doi.org/10.5772/intechopen.89564*

and make decisions and actions by acquiring, understanding, predicting, and making decisions [1]. The concept of Situational Awareness (SA) originates from the military demand in the 1980s, and with the rise of network, it was introduced by

SA should go through several steps, such as situation acquisition, situation understanding, situation prediction, situation visualization and so on [2, 3]. In the situation acquisition stage, there may be a lot of complex, repetitive, or even false alarm information. In addition, the existing SA methods use IDS, firewalls, virus detection and other tools data, based on time series, graph theory, Bayes, game theory and other methods, according to the network environment, the history of the attacker and the network ontology vulnerability; these are used to evaluate and predict the network security situation, without considering the emerging vulnerabilities and their SA. To solve the above problems, this chapter proposes a botnet SA method based on DS evidence theory. Compared with other SA methods, DS evidence theory not only can solve uncertainty problems, but it also does not need prior probability and conditional probability density. Therefore, we can manually assign it initial trust

Botnet SA integrates all kinds of botnet security elements to evaluate the security situation of the network in real time, which provides the basis for the network security analysis, and evaluates the network security more accurately, thus minimizing risks and losses from botnet threats. Botnet security SA plays an important role in improving the ability of network monitoring, emergency response and

1.we propose a method of botnet threat assessment based on evidence chain, which computes the target credibility to determine whether there is a threat in

2. the evidence chain method is applied to botnet to realize the situation of network security. DS evidence theory solves the uncertainty problem of

3. the experiment is carried out using the public data set of Nanjing University of Posts and Telecommunications (NJUPT). The results show that the network security situation assessment method proposed in this chapter is reasonable and effective, and can improve the accuracy of security situation prediction.

There are already some approaches to network security SA: In the research of network security SA architecture, Kokkonen proposed in 2016 a network security SA architecture, which mainly includes information exchange module and emphasizes standardized information format [4]. In 2017, Eiseler proposed a network security SA architecture from the perspective of IT complexity [5]. The main idea is to abstract a layer of operation (decision) and the result of decision for decision makers from non-technical background. In the research of network security SA, in 2016, Yang et al. used SVM machine learning method for SA [6]. After being trained by classifier, the data can be used to predict the situation value. But the method has the defect that the situation is normalized and the information is not abundant enough. In 2016, KHALID et al., targeting data injection attacks, could lead to unreliability and insecurity of network physical infrastructure such as (WAMS),

Tim Bass into the field of network security.

*Computer Security Threats*

based on our expertise and individual knowledge.

predicting the development trend of network security. The main contributions of this chapter are as follows:

the network;

network threat.

**2. Related work**

**40**

a wide-area monitoring system. In this chapter, a Bayesian based approximate filter (BAF) method [7] is proposed to minimize the impact of injection attack on oscillatory parameters, so as to improve the resistance of monitoring applications to data injection attacks. In 2016, in the HMM-based network security situation assessment method, Li et al. used to extract the observation values and model parameters by establishing the time period, which is an important factor affecting the real-time and accuracy of the evaluation. However, there are two problems: The results are as follows: (1) the size of the time period is given randomly by people, which cannot represent the security and real-time performance of the current network; (2) the state transition matrix and the observation symbol matrix are usually determined by experience and have strong abstractness. To solve this problem, Li et al. later trained the parameters of the HMM model by mixed multi-population genetic algorithm (MPGA) [8] to improve the reliability of the parameters and to solve the problem that the emergency situation could not be highlighted in a certain period of time. Experiments show that this method can reflect the current network security situation effectively and accurately. [9, 10] put forward the overall goal of network security SA, which is determined by scope, level, requirement and decision. The method of SA is classified from four aspects: data collection, decision making, analysis and visualization.

Through the research of network security SA, to a certain extent, the researchers give other researchers some practical methods, but these methods also have a limited scope of application. Most of the SA methods only consider the calculation of the threat situation caused by an external attack and ignore the problem of the security situation change caused by the insecurity of the system and the equipment itself. This chapter presents a method of network security SA based on evidence chain theory. DS evidence chain theory has many advantages in SA. Firstly, it does not require prior probability and conditional probability density. Secondly, sometimes the information provided by the sensor is not necessarily very accurate, and there may be a certain degree of fuzziness, and the DS evidence method can solve the uncertainty calculation problem. Finally, DS evidence theory can continuously narrow the scope of the hypothesis set by merging evidence. Its basic idea is to fuse several sub-evidences according to the Dempster formula, so as to further determine the possibility of the occurrence of certain propositions.


**5.3. Adiponectin**

228 Thyroid Hormone

**5.1. Leptin**

expenditure [83].

**5. Thyroid hormone effects on adipokines in weight loss**

important than the overall weight loss [179, 183].

typically accompany weight loss [184].

leptin and thyroid hormones.

**5.2. Resistin**

After weight loss, leptin levels decrease [178-180], as well TSH and T3 reduces to normal levels [92, 93, 112]. In starvation conditions, serum leptin levels decrease and thyroid hormones levels are quickly supressed, leading to a consequent reduction in energy

Varady et al. [181] studying severely obese women have suggested that a minimum weigh loss of 5% is required to improve adipokines profile, including the reduction of leptin levels. Not only a minimal weight loss is required but a maximal weigh loss beyond which further improvements in circulating adipokine levels are no longer observed has been suggested [179, 182]. The method or diet content by which weigh loss is achieved seems to be less

As mentioned before, studies investigating the correlation between thyroid hormones and leptin levels present conflicting results. Luvizotto et al. showed that administration of physiologic levels of T3 increases leptin mRNA expression with no influence on body weight in calorie-restricted obese rats [108], while administration of supraphysiological T3 dose promotes weight loss and diminishes serum levels and gene expression of leptin [108] (Figure 3). Again, as Syed et al. [106] reported, the effects of thyroid hormones in leptin concentrations might be indirect trough the regulation of fat mass (Figure 4). The decline of TSH release and T3 concentrations associated with decrease in leptin levels after weight loss may contribute to the compensatory reductions of energy expenditure and catabolism that

As both thyroid hormones and leptin have major roles in energy balance and regulation of body weight, an interaction between these hormones could not be discarded to achieve or maintain weight loss but further studies are necessary to elucidate the relationship between

Since obesity is considered a global epidemic and one of majors public health problem, affecting developed and developing countries [185, 186], and obese subjects may presente increased resistin levels [46], which can worse insulin resistance and inflammation [119], one of the most used strategies is the weight loss by reduction of caloric intake [187]. Caloric restriction affects the regulation of adipose tissue gene expression, normalizing the adipokines changes caused by obesity [10], while thyroid hormones play a central role in regulating adipose tissue metabolism [83], being related to body weight changes, thyroid hormones may therefore play a key role in the normalization of resistin in weight loss.

Nogueiras et al. [130] show a decrease in resistin mRNA expression in epididymal adipose tissue of pregnant and nonpregnant rats that were subjected to food restriction. Kim et al. [188] showed that resistin mRNA levels were decreased during fasting, but increased considerably Negative correlation between obesity and circulanting adiponectin has been well accept, and adiponectin concentration increases concomitantly to weight loss [190].

Experimental study on caloric restriction showed increased levels of circulating adiponectin [191]. Accordingly, Zhu et al. [194] showed that calorically restricted animals exhibited a significant increase in plasma adiponectin levels accompanied by significant decline in triglyceride levels, showing that adiponectin levels are inversely proportional to the degree of adiposity [192, 193].

Thyroid hormones perform a central role in adipose tissue metabolism regulation [83], which produces the biologically active substances adipocytokines, or adipokines, that include adiponectin [127, 194], indeed thyroid hormones share some physiological actions with adiponectin, such as reduction of body fat by increased thermogenesis and lipid oxidation [194].

The interaction between thyroid hormones and adiponectin concentration remains unclear. In humans, hyperthyroidism has been associated with both similar [128, 195] and elevated adiponectin concentrations [196], while experimental study with hyperthyroid rats found an increase in adiponectin serum concentration [133]. In agreement, some data shown that therapy to normalize hyperthyroidism significantly reduced circulating adiponectin levels [197]. In contrast Luvizotto et al. [189] show that thyroid hormone, at the doses of 5 and 25 μg T3 / 100 g BW, diminishes adiponectin gene expression, suggesting that thyroid hormone modulates negatively adiponectin expression in calorie-restricted obese rats (Figure 4).


**Human Enhancement Technologies and Our Merger with Machines**

Printed Edition of the Special Issue Published in *Philosophies* Woodrow Barfield and Sayoko Blodgett-Ford Edited by

www.mdpi.com/journal/philosophies


*2.2. Extended Structure*

The multistep DC-link circuit can be extended by keeping the proposed MLI's main bridge construction. This enables the proposed inverter to achieve a higher amount of voltage steps using a small quantity of power equipment. To extend the multistep DC-link circuit, the half-bridge power module and (*n*) multiple units of full-bridge power modules are connected following series connection to generate (*N* – 1) sequent voltage steps as depicted in Figure 2. For full-bridge power cells:

$$Vdc\\_fb1 = \mathfrak{Z}^{(0)}Vdc\tag{4}$$

$$Vdc\\_fbm = \mathfrak{Z}^{(n-1)}Vdc.\tag{5}$$

**Figure 2.** Proposed structure of three-phase *N*-level multilevel inverter.

For half-bridge power cells:

$$Vdc\\_hb = \mathfrak{Z}^{(n)}Vdc.\tag{6}$$

For a single DC voltage supply:

$$Vd\mathbf{c\\_s} = Vd\mathbf{c} + \sum\_{i=1}^{i=n} \mathbf{3^{(n-1)}}Vd\mathbf{c} = \left(\frac{\mathbf{1} + \mathbf{3^{(n)}}}{2}\right)Vd\mathbf{c}.\tag{7}$$

Hence, the highest number of voltage steps that can be achieved are:

$$N = \frac{3}{2} \left( 1 + \mathfrak{Z}^{(n)} \right). \tag{8}$$

Here, *N* = [6, 15, 42, 123, ........]. The total required numbers of IGBTs (*M\_sw*) and DC supplies (*M\_DC*) in terms of voltage steps are given as:

$$M\_-sw = 4\log\_3\left(\frac{2}{3}N - 1\right) + 14\tag{9}$$

$$M\_-DC = \mathcal{L}\log\_3\left(\frac{2}{3}N - 1\right) + 2.\tag{10}$$


**Section 3 Stromal Cell Therapies 141**

Chapter 7 **Mesenchymal Stromal Cells as a Therapeutic Intervention 143** Ivonne Hernandez Schulman and Joshua M. Hare

Preface

mal cell ontogeny *in vivo* remains challenging.

greater scientific audience.

cells as a double-edged sword.

Stromal cells are connective tissue cells of any organ, and they support the function of the parenchymal cells of that particular organ. Stromal/stromal stem cells are fundamentally a heterogeneous population of cells with contradictory differentiation potential depending upon their environmental niche. Stromal cell biology is not only intriguing, but equally stro‐

In recent years, there has been substantial advances in our understanding of stromal cell biology, especially stromal cell isolation, characterization, differentiation, and interactions in physiological (epithelial–stromal interactions) as well as pathophysiological (stromal–cancer interactions) contexts. In addition, stromal cells are also utilized more and more as a thera‐ peutic tool not only in the field of gene therapy but also in the translational field of tissue engineering and regenerative medicine. Therefore, the goal of this book is to consolidate the recent advances in the area of stromal/stromal stem cell biology, covering a broad range of interrelated topics in a timely fashion and to disseminate that knowledge in a lucid way to a

This book will prove highly useful for students, researchers, and clinicians in stem cell biolo‐ gy, developmental biology, cancer biology, pathology, oncology, as well as tissue engineering and regenerative medicine. This quick reference will benefit anyone desiring a thorough over‐ view of stromal cell structure, function, and its therapeutic implications in human diseases. The book consists of nine chapters, contributed by leading experts in basic science and clini‐ cal care, and is organized into three parts. The first part introduces the structure and func‐ tion of stromal/stromal stem cells. The second part of the book deals with stromal cell interactions, such as stromal–epithelial interactions and stromal–tumor interactions. Eventu‐ ally, in contrast, the third part explores the therapeutic potential of stromal/stromal stem

I would like to thank the staff of IntechOpen who have produced this book so efficiently, and in particular I am indebted to Danijela Vladika and Nina Kalinic, the publishing process managers, Romina Skomersic, the author service manager, and Anja Filipovic, the commis‐ sioning editor, for their valuable source of advice throughout the preparation of this book.

> **Valarmathi M. Thiruvanamalai, MD, PhD** Department of Biomedical Engineering School of Medicine and School of Engineering UAB | The University of Alabama at Birmingham

> > Birmingham, Alabama, USA

Finally, I dedicate this book to my father and the memory of my mother.


Benefits and Barriers of Physical Activity in Social Inclusion and Quality of Life in People with Serious Mental Disorders

*Cristina Méndez-Aguado, Adolfo J. Cangas, María Jesús Lirola, Juan Leandro Cerezuela and Andrés López-Pardo*


**Author details**

Rodney C. Jariremombe1,2

1 Bachelor of Science Honors Degree in Biological Sciences (Genetics and Microbiology), Harare, Zimbabwe

2 Safety, Health and Environmental Officer, Harare, Zimbabwe

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*Analyzing and Presenting Data with LabVIEW DOI: http://dx.doi.org/10.5772/intechopen.96130*

#### **Figure 32.**

*VI generating an html report.*

https://www.ni.com/en-tr/support/documentation/supplemental/06/the-ni-td ms-file-format.html.