**2.3 Alkaloids**

The most valuable and essential plant compounds are alkaloids, which are also powerful medicinal agents [64]. The alkaloid family is the largest class of secondary plant chemicals with one or more nitrogen atoms, typically combined as part of a cyclic structure, which consists of around 5500 identified compounds. At least one nitrogen atom with a basic nucleus is present [65]. Other elements found in alkaloids include carbon, hydrogen, oxygen, sulphur, chlorine, bromine, and phosphorus. These organic substances are neutral or only slightly acidic. A few synthetic substances have structure similar to natural alkaloids. Numerous creatures, including fungus, bacteria, mammals, and plants, contain alkaloids [66]. It has numerous medicinal effects, including anti-malarial, anti-asthmatic, anti-cancer, cholinomimetic, vasodilatory, anti-arrhythmic, analgesic, anti-bacterial, and antihyperglycemic effects. Several alkaloids have stimulant and psychoactive effects on the central nervous system (CNS) [67]. Additionally, it has immunomodulatory effects. Immune effector cells trigger the main immunomodulatory pathways of alkaloids to cause autoimmune responses [68]. It interacts with the forskolin proteins, lysosomes, phagocyte vacuoles, and neutrophil, monocyte, and macrophage cytoskeleton filaments. In addition, it triggers the innate immune response by causing macrophages to perform phagocytic functions [69]. Moreover, active antigen-presenting cells (APCs) i.e. macrophages, contribute to the production of adaptive immune responses [70]. Potent alkaloids i.e., berberine (BBR) and colchicine are identified as potential immunomodulatory drugs.

Berberine (BBR), an isoquinoline alkaloid, is present in several Berberis species. Its chemical name is 5,6-dihydro-9,10-dimethoxybenzo[g]-1,3-benzodioxolo[5,6-a] quinolizinium compound. It has anti-diabetic, hepatoprotective, hypolipidemic, cancer-preventive, anti-hypertensive, anti-oxidant, anti-inflammatory, antidepressant, anti-diarrheal, and anti-microbial effects [71–73]. Multiple mechanisms of action are used to create these pharmacological effects. It interacts with multiple cellular kinases and signalling pathways. Some routes of the actions are interlinked with immunomodulatory pathways i.e., activation of nuclear factor erythroid-2 related factor-2 (Nrf2), MAPKs, NF-κB, & AMPK pathways; and expression of sirtuin 1 (SIRT1), & deacetylation of transcription factors of forkhead box O (FOXO) proteins [74]. In studies, BBR also lowers levels of proinflammatory cytokines such IFNγ, IL-17, IL-6, and TNF-α, which results in immunomodulatory effects in non-obese diabetic (NOD) mice [73]. The overexpression of NADPH oxidase 2/4 is also downregulated, which results in antioxidant effects [75]. Furthermore, BBR stimulates Nrf2 activities, which increases the production of antioxidant enzymes such as NADPH quinine oxidoreductase-1 (NQO-1) and heme oxygenase-1 (HO-1). Phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt), P38, and AMPK pathways are a few of the different cell signalling pathways that are promoted by Nrf2 [76]. As a result, it can be employed as an immunomodulatory agent to treat autoimmune diseases.

A significant natural alkaloid called colchicine is derived from the Colchicum autumnale plant (Colchicaceae family). Colchicine is a medication that has anti-inflammatory effects and is used to treat various inflammatory diseases including pericarditis, gout, and familial Mediterranean fever. It is known to impede microtubule polymerisation and reduce the growth of several types of cancer cells as its primary mechanism of action. Additionally, it has been discovered to decrease immunological responses such as neutrophil chemotaxis,

lysosome breakdown, and leukocyte adhesiveness [77]. The immune cell signals i.e., nucleotide-binding and oligomerization domain (NOD), leucine-rich repeat proteins (LRR)-containing protein 3 and pyrin domain proteins [NLRP3 sensors] is suppressed by colchicine as well. SARS-CoV-2 proteins are also capable of initiating and energising comparable pathways. The therapy of COVID-19 with colchicine has tragically failed [78]. Moreover, it inhibits the production of leukotriene B4, neutrophil chemotactic factor and IL-1, which cause the immune cell regulatory action [79–81]. According to a literature review, colchicine can restrict procollagen synthesis, increase collagenase activity, and block mast cell release of histamine. It can also reduce the production of TNF-α that is caused by LPS [82–84]. Hence, autoimmune illnesses can be managed using their immunomodulatory effects.

#### **2.4 Glycosides**

The secondary metabolites of plants that contain sugar are called glycosides, a portion to which non-sugar portions are joined. The binding between the sugar and nonsugar moiety leads to hemiacetal formation. It includes the alcoholic or phenolic hydroxyl group of nonsugar and the aldehyde or keto group of the sugar moiety. These agents play numerous beneficial activities in animals and humans; however, many plants accumulate these chemicals in an inactive form which can be activated by the action of enzymes in the body [85]. Glycosides may be classified depending upon the glycone and aglycone moieties, such as glucoside, fructoside, α-glycosides and βglycosides. Amygdalin and scrocaffeside-A are identified as primary bioactive immunomodulating agents. These substances primarily work to stimulate the immunological, cardiac, and central neurological systems. Furthermore, glycosides also show substantial antibacterial effects [86].

The bitter almond, apricot, plum, apple, and peach fruit kernels contain amygdalin, a cyanogenetic glycoside. Leukoderma, colorectal cancer, emphysema, leprosy, bronchitis, and asthma are all conditions frequently treated by amygdalin [87, 88]. By controlling T-cells, amygdalin has been shown to suppress inflammatory reactions and enhance immunomodulatory effects [89]. Additionally, it activates caspase-3, which suppresses Bcl-2-like protein 4 (Bax, proapoptotic protein) and B-cell lymphoma 2 (Bcl-2, an antiapoptotic protein) [88]. It stops the metastases of cancer cells via prevention of β1 and β4 integrins expression leading to suppression of the Aktmediated mammalian target of rapamycin (mTOR) pathway (immunomodulatory mediators). Moreover, it lowers the β-catenin, integrin-linked kinase (ILK), and focal adhesion kinase (FAK) expression in immune cells [90]. Consequently, amygdalin is also an immunomodulatory agent.

Picrorhiza scrophulariiflora roots contain the caffeoyl glycoside known as scrocaffeside-A. It effectively heals leukoderma, inflammatory illnesses, gastrointestinal & urinary disorders, scorpion stings, and snake bites [91, 92]. Additionally, it controls the immunological responses of splenocytes by triggering concanavalin-A and LPS interactions [93]. According to an in vitro investigation, scrocaffeside-A increases CD4/CD8 population and cytokine production in splenocytes, which activates peritoneal macrophages and natural killer cell activity. Furthermore, it has been observed that scrocaffeside-A exposure increases the production of IFN-α, IL-2, IL-4, and IL-12 in cultured splenocytes. It suggests that scrocaffeside-A stimulates the host's immune system [94] and as a result, scrocaffeside-A is regarded as an immunomodulating substance.


4. Shared-memory bus–based systems

surprising that processor utilization is quite sensitive to the values of h1, but is much less

Processor utilization as a function of h2, the hit rate of the second-level cache, is shown in Figure 3 for two values of the main memory access time, tm ¼ 25 and tm ¼ 50. Processor

Processor utilization as a function of the probability of pipeline stalls ps ¼ ps1 þ 2ps2 is shown

Again, processor utilization is rather insensitive to the probability of pipeline stalls as well as

utilization is rather insensitive to values of h2, and does not change much with tm.

Figure 3. Processor utilization as a function of second-level cache hit rate for h1 ¼ 0:9, ps ¼ 0:2.

Figure 4. Processor utilization as a function of probability of pipeline stalls for h1 ¼ 0:9, h2 ¼ 0:8.

in Figure 4 for three combinations of values of tc and tm.

sensitive to the values of tc.

80 Petri Nets in Science and Engineering

the values of tc and tm.

An outline of a shared-memory bus-based multiprocessor is shown in Figure 5. The system is composed of n identical processors, which access the shared memory using a system bus. To reduce the average access time to the shared memory, the processors use (multilevel) cache memories. It is assumed that memory consistency is provided by a cache coherence mechanism [16], which usually increases the miss ratio of accessing caches (and is otherwise not represented in the model).

A timed Petri net model of a shared-memory bus-based multiprocessor is shown in Figure 6. It contains models of n processors (only two are shown in Figure 6), which are copies of the model shown in Figure 1 except for the main memory (transition Tmem) which becomes shared memory in Figure 6. The remaining part of Figure 6 is modeling the bus that coordinates accesses of processors to the shared memory.

Figure 5. A shared-memory bus-based multiprocessor.

In Figure 7, the bus utilization approaches 100% for about five processors. Moreover, the degradation of processors' performance due to increasing waiting times for accessing the bus

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Figure 7. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:9, h2 ¼ 0:8, ps ¼ 0:2.

The average waiting time (in processor cycles) of accessing shared memory (i.e., the times from requesting memory access in place Pri to granting this access by an occurrence of Tai) is shown

Figure 8 shows that the waiting times increase almost linearly with the number of processors when this number is greater than 5, i.e., when the bus (and shared memory) is utilized in

Figure 8. The average waiting time for accessing shared memory as a function of the number of processors for h1 ¼ 0:9,

(and shared memory) is well illustrated in Figure 7.

almost 100%.

h2 ¼ 0:8, ps ¼ 0:2.

in Figure 8 as a function of the number of processors in the system.

Figure 6. A timed Petri net model of bus-based shared-memory multiprocessor.

When a processor i, i ¼ 1, …, n, requests an access to shared memory, place Pri becomes marked. If the bus is available (i.e., if place Bus is marked), the occurrence of transition Tai indicates that processor i begins its access to shared memory. Transitions Ta1, ..., Tan constitute a conflict class with a fair resolution of conflicts (i.e., all conflicting processors have the same probability of being selected for accessing memory). In real systems, accessing the shared bus is often based on priorities assigned to processors; such priorities could easily be represented using inhibitor arcs in Petri nets.

Place Pmem collects memory access requests from all processors (occurrences of transitions Tai). The end of memory access (i.e., the end of the occurrence of Tmem) is indicated by an occurrence of transition Tei of the processor which initiated memory access. The occurrence of Tei also returns a token to Bus, allowing another access to shared memory to be executed.

Figure 7 shows the utilization of processors and the bus as functions of the number of processors in a shared-memory system for the values of modeling parameters shown in Table 1.

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Figure 7. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:9, h2 ¼ 0:8, ps ¼ 0:2.

In Figure 7, the bus utilization approaches 100% for about five processors. Moreover, the degradation of processors' performance due to increasing waiting times for accessing the bus (and shared memory) is well illustrated in Figure 7.

The average waiting time (in processor cycles) of accessing shared memory (i.e., the times from requesting memory access in place Pri to granting this access by an occurrence of Tai) is shown in Figure 8 as a function of the number of processors in the system.

Figure 8 shows that the waiting times increase almost linearly with the number of processors when this number is greater than 5, i.e., when the bus (and shared memory) is utilized in almost 100%.

When a processor i, i ¼ 1, …, n, requests an access to shared memory, place Pri becomes marked. If the bus is available (i.e., if place Bus is marked), the occurrence of transition Tai indicates that processor i begins its access to shared memory. Transitions Ta1, ..., Tan constitute a conflict class with a fair resolution of conflicts (i.e., all conflicting processors have the same probability of being selected for accessing memory). In real systems, accessing the shared bus is often based on priorities assigned to processors; such priorities could easily be

Place Pmem collects memory access requests from all processors (occurrences of transitions Tai). The end of memory access (i.e., the end of the occurrence of Tmem) is indicated by an occurrence of transition Tei of the processor which initiated memory access. The occurrence of Tei also returns a token to Bus, allowing another access to shared memory to be executed.

Figure 7 shows the utilization of processors and the bus as functions of the number of processors in a shared-memory system for the values of modeling parameters shown in Table 1.

represented using inhibitor arcs in Petri nets.

82 Petri Nets in Science and Engineering

Figure 6. A timed Petri net model of bus-based shared-memory multiprocessor.

Figure 8. The average waiting time for accessing shared memory as a function of the number of processors for h1 ¼ 0:9, h2 ¼ 0:8, ps ¼ 0:2.

The number of processors for which the bus is used to almost 100% can be estimated by the

Performance Analysis of Shared-Memory Bus-Based Multiprocessors Using Timed Petri Nets

np ¼ 1 þ ps1 þ 2∗ps2 þ ð Þ 1 � h1 ∗ð Þ tc þ ð Þ 1 � h2 ∗tm ð Þ 1 � h1 ∗ð Þ 1 � h2 ∗tm

0:1∗0:2∗25 ¼ 1:2 þ 1:0

There are several ways in which the number of processors can be increased in bus-based systems without sacrificing the processors' performance. The simplest approach is to introduce the second bus which allows two concurrent accesses to shared memory, provided the memory is dual port (it allows two concurrent accesses). Figure 11 outlines a dual bus shared-

Petri net model of a dual bus system is the same as in Figure 6; the only difference is the initial marking of place Bus, which now requires two tokens to represent two concurrent accesses to shared memory. It should be observed observed that, for a small number of processors, the utilization of each bus in Figure 12 is one half of that in Figure 7, and also the number of processors that can be used in such a dual bus system without degradation of their perfor-

The results shown in Figure 12 are very similar to those shown in Figure 9. The second bus in a shared-memory system allows to perform two concurrent accesses to the shared memory. From a single processor's performance point of view, this effect is similar to reducing two

1 þ 0:1 þ 0:1 þ 0:1∗ð Þ 5 þ 0:2∗25

: (3)

85

0:5 ¼ 4:4: (4)

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following formula:

memory system.

For the case shown in Figure 7, this number is:

For the case shown in Figure 9, this value is 8.2.

mance is twice as large as in a single bus system (Figure 7).

Figure 11. A dual bus shared-memory multiprocessor.

Figure 9. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:9, h2 ¼ 0:9, ps ¼ 0:2.

If the value of the second-level cache hit rate, h2, increases (and the other parameters do not change), the number of accesses to main memory is reduced, so that the performances of processors and the whole system improve. Figure 9 shows the utilization of processors and the bus as functions of the number of processors in the system for h2 ¼ 0:9. It also shows that the reduced (in comparison with Figure 7) utilization of the bus allows the increase of the number of processors without significant degradation of their performance.

By a similar argument, reduced hit rate at the first-level cache, h1, increases the number of accesses to the second-level cache as well as to the main memory, and this results in reduced performance of the system. Figure 10 shows the utilization of processors and the bus as functions of the number of processors in the system for h1 ¼ 0:8. It provides a good illustration of the degradation of performance when compared with Figure 7.

Figure 10. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:8, h2 ¼ 0:8, ps ¼ 0:2.

The number of processors for which the bus is used to almost 100% can be estimated by the following formula:

$$n\_p = \frac{1 + p\_{s1} + 2 \ast p\_{s2} + (1 - h\_1) \ast (t\_c + (1 - h\_2) \ast t\_m)}{(1 - h\_1) \ast (1 - h\_2) \ast t\_m}. \tag{3}$$

For the case shown in Figure 7, this number is:

$$\frac{1+0.1+0.1+0.1\*(5+0.2\*25)}{0.1\*0.2\*25} = \frac{1.2+1.0}{0.5} = 4.4.\tag{4}$$

For the case shown in Figure 9, this value is 8.2.

If the value of the second-level cache hit rate, h2, increases (and the other parameters do not change), the number of accesses to main memory is reduced, so that the performances of processors and the whole system improve. Figure 9 shows the utilization of processors and the bus as functions of the number of processors in the system for h2 ¼ 0:9. It also shows that the reduced (in comparison with Figure 7) utilization of the bus allows the increase of the

Figure 9. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:9, h2 ¼ 0:9, ps ¼ 0:2.

By a similar argument, reduced hit rate at the first-level cache, h1, increases the number of accesses to the second-level cache as well as to the main memory, and this results in reduced performance of the system. Figure 10 shows the utilization of processors and the bus as functions of the number of processors in the system for h1 ¼ 0:8. It provides a good illustration

Figure 10. Processor and bus utilization as functions of the number of processors for h1 ¼ 0:8, h2 ¼ 0:8, ps ¼ 0:2.

number of processors without significant degradation of their performance.

of the degradation of performance when compared with Figure 7.

84 Petri Nets in Science and Engineering

There are several ways in which the number of processors can be increased in bus-based systems without sacrificing the processors' performance. The simplest approach is to introduce the second bus which allows two concurrent accesses to shared memory, provided the memory is dual port (it allows two concurrent accesses). Figure 11 outlines a dual bus sharedmemory system.

Petri net model of a dual bus system is the same as in Figure 6; the only difference is the initial marking of place Bus, which now requires two tokens to represent two concurrent accesses to shared memory. It should be observed observed that, for a small number of processors, the utilization of each bus in Figure 12 is one half of that in Figure 7, and also the number of processors that can be used in such a dual bus system without degradation of their performance is twice as large as in a single bus system (Figure 7).

The results shown in Figure 12 are very similar to those shown in Figure 9. The second bus in a shared-memory system allows to perform two concurrent accesses to the shared memory. From a single processor's performance point of view, this effect is similar to reducing two

Figure 11. A dual bus shared-memory multiprocessor.

Figure 12. Processor and bus utilization as functions of the number of processors—dual bus system with h1 ¼ 0:9, h2 ¼ 0:8, ps ¼ 0:2.

times the number of accesses to the shared memory, and this is the effect of reducing two times the miss rate for the second-level cache (which is shown in Figure 9).

If dual port memory cannot be used, the shared memory can be split into several independent modules which can be accessed concurrently by the processors provided that the bus is also split into sections associated with each module, with processors accessing all such sections, as shown in Figure 13 for four independent memory modules. The main difference between a multibus system (Figure 11) and a system with split bus is in accessing the shared memory; in a multiple bus system, the whole shared memory is accessed by each bus, while in a split bus system (Figure 13), each section of the bus accesses only one memory module. In the system

Figure 14. Petri net model of a

shared-memory

multiprocessor

 with multiple memory modules.

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Figure 13. A shared-memory multiprocessor with multiple memory modules.

times the number of accesses to the shared memory, and this is the effect of reducing two times

Figure 12. Processor and bus utilization as functions of the number of processors—dual bus system with h1 ¼ 0:9,

If dual port memory cannot be used, the shared memory can be split into several independent modules which can be accessed concurrently by the processors provided that the bus is also split into sections associated with each module, with processors accessing all such sections, as shown in Figure 13 for four independent memory modules. The main difference between a multibus system (Figure 11) and a system with split bus is in accessing the shared memory; in a multiple bus system, the whole shared memory is accessed by each bus, while in a split bus system (Figure 13), each section of the bus accesses only one memory module. In the system

the miss rate for the second-level cache (which is shown in Figure 9).

Figure 13. A shared-memory multiprocessor with multiple memory modules.

h2 ¼ 0:8, ps ¼ 0:2.

86 Petri Nets in Science and Engineering

Figure 14. Petri net model of a shared-memory multiprocessor with multiple memory modules.

shown in Figure 13, up to four (the number of memory modules) memory accesses can be performed concurrently, but if two (or more) processors request access to the same memory module, the requests are served one after another.

for example, the probabilities of accessing consecutive memory modules by each processor

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89

Figure 15 shows the utilization of processors and busses as functions of the number of pro-

In Figure 15, even for 20 processors, the average utilization of the bus is close to 80%, so the

The chapter uses timed Petri nets to model shared-memory bus-based architectures at the level of instruction execution to study the effects of modeling parameters on the performance of the system. The models are rather simple with straightforward representation of modeling parameters. Performance results presented in this chapter have been obtained by the simulation of developed Petri net models. However, the model shown in Figure 7 has only 10 states, so its analytical solution (for different values of modeling parameters) can be easily obtained and compared with simulation results to verify their accuracy. Table 2 shows such a comparison of processor utilization for several combinations of parameters h1 and h2. In all cases, the

The models of multiprocessor systems are usually composed of many copies of the same submodel of a processor and possibly other elements of the system. Colored Petri nets [17] can significantly simplify such models by eliminating copies of similar subsystems. Analysis of

Finally, it should be noted that the performance of real-life multiprocessor systems very rarely can be described by a set of parameters that remain stable for any significant period of time. The basic parameters like the hit rates depend upon the executed programs as well as their data, and can change very quickly in a significant way. Consequently, the characteristics presented in this chapter can only be used as some insight into the complex behavior of

h1 h2 Simulated results Analytical results

0.8 0.8 0.3341 0.3333 0.8 0.9 0.3846 0.3846 0.9 0.8 0.4763 0.4762 0.9 0.9 0.5255 0.5263

colored Petri nets is, however, much more complex than that of ordinary Petri nets.

could be used to model sequential processing of large arrays, and so on.

simulation-based results are very close to the analytical ones.

cessors in a system outlined in Figure 13.

system can accommodate more processors.

5. Concluding remarks

multiprocessor systems.

Table 2. A comparison of simulation and analytical results.

Petri net model of a system outlined in Figure 13 is shown in Figure 14 where only two processors and two memory modules are detailed.

In Figure 14, there is a free-choice place Pri for each processor i, i ¼ 1, …, n. This free-choice place selects the requested memory module by transitions Tij, j ¼ 1, …, 4, and forwards the memory access request to the selected memory module (place Pij). If the selected module is available, i.e., if place Busj is marked, the access to shared memory is initiated by the occurrence of Taij. When this memory access is completed, the occurrence of Teij releases the memory modules (by returning a token to Busj) and resumes instruction execution in the processor that requested the memory access.

If memory module is not available when it is requested, the memory access is delayed (in Pij) until the requested module becomes available.

It is possible that more than one processor becomes waiting for the same memory module. The selection of the processor which will get access first is random with the same probability assigned to all waiting processors. In real systems, there is usually some priority scheme that determines the order in which the waiting processors access the bus. Such a priority scheme could easily be modeled if it is needed (for example, for studying the starvation effect which can be created when the system is overloaded).

In Figure 14, the selection of memory modules is random, with the same probabilities for all modules. If this policy is not realistic, a different memory accessing policy can be implemented,

Figure 15. Processor and bus utilization as functions of the number of processors—system with four memory modules and h1 ¼ 0:9, h2 ¼ 0:8, ps ¼ 0:2.

for example, the probabilities of accessing consecutive memory modules by each processor could be used to model sequential processing of large arrays, and so on.

Figure 15 shows the utilization of processors and busses as functions of the number of processors in a system outlined in Figure 13.

In Figure 15, even for 20 processors, the average utilization of the bus is close to 80%, so the system can accommodate more processors.


**2.2 Objective evaluation (technology)**

Diagnostic equipment can help to identify different degrees of hearing loss in a more quantitative fashion. There are several techniques that are implemented in medical equipment for screening of hearing. The most widely used initial screen involves a puretone, air-conduction hearing test or audiometry, followed by tympanometry, otoacoustic emissions, multilingual speech audiometry, and stem cell evoked potentials.

Hearing testing is conducted in the audiometry testing chamber; a special sound booth is built into this room, which must be constructed based on testing guidance described in the ASTM E336 "Standard Test Method for measurement of Airborne Sound Insulation in Buildings" (ASTM E336–10, 2011). This triangular-shaped booth is designed to ensure that the sound levels inside are sufficiently quiet to permit accurate hearing-threshold measurements. In addition to the sound booth, the examination room possesses several other features designed to reduce sound levels in the room further, as shown in Figure 3. These include sound dampening materials on the interior walls of the examination room and a rubber seal on the exterior door.

Fig. 3. Anaechoic Chamber for audiometry test.

Technology for Hearing Evaluation 9

Generally, an audiometric system controlled by a microprocessor (Penhaker & Kijonka, 2011) is composed of two channels: one for test ear stimulus, and other as a non-test ear

Fig. 5. A patient's Békésy test (Gutierrez et al., 2009)

masker, as shown in the block diagram of Figure 6.

Fig. 6. General block diagram of an audiometer

The testing suite consists of two adjoining rooms: one of single-wall construction, and the other, of double-wall construction. The double portion of the suite is the subject of the noise reduction test that includes the following: 1) generating a high-volume controlled sound field outside the testing chamber; 2) measuring the sound pressure level at the outer wall; 3) measuring the attenuated sound-pressure level of the controlled sound field inside the test chamber, and 4) subtracting internal from the external sound-pressure levels while making corrections for the interior ambient sound pressure level (Acoustic Systems MD, 2000).

#### **2.2.1 Pure-tone audiometry and logoaudiometry**

The Audiometry study is used to diagnose the degree and type of hearing loss by determining the faintest tones that a person can hear at selected pitches; the measuring equipment utilized is called audiometer. This device emits a pure tone by means of a fixedfrequency oscillator calibrated at the decibel hearing level (National Health and Nutrition Examination Survey, 2003); frequencies (125-8000 Hz) and intensities (0-110 dB) which can be varied. This unit is controlled by a microprocessor that is sometimes available as a handheld Windows-driven instrument and possesses high accuracy and efficiency (Khandpur, 2005). Such systems can be also employed with bone vibrators to test conductive hearing mechanisms. A pair of headphones is attached, and a test subject feedback button indicates when the subject has heard the sound. On the one hand, audiometry measures hearing thresholds and oral audiometry or logoaudiometry (Aguilera et al., 1997), considered to be a subjective evaluation, is defined by the capacity to evaluate the human speech understanding consists of supplying the patient with words at different energies that aid in recognizing acoustical features and in providing clues concerning the etiology of the hearing loss. On the other hand, audiometry includes supraliminal tests such as SISI*,* Fowler, Tone Decay, or Békésy, to suggest the sensorineural damage (Ghani, 2005). Figures 4 and 5 ilustrate the SISI and Békésy tests.

Fig. 4. A patient's SISI Graph with score value (Gutierrez et al., 2009)

The testing suite consists of two adjoining rooms: one of single-wall construction, and the other, of double-wall construction. The double portion of the suite is the subject of the noise reduction test that includes the following: 1) generating a high-volume controlled sound field outside the testing chamber; 2) measuring the sound pressure level at the outer wall; 3) measuring the attenuated sound-pressure level of the controlled sound field inside the test chamber, and 4) subtracting internal from the external sound-pressure levels while making corrections for the interior ambient sound pressure level (Acoustic Systems MD, 2000).

The Audiometry study is used to diagnose the degree and type of hearing loss by determining the faintest tones that a person can hear at selected pitches; the measuring equipment utilized is called audiometer. This device emits a pure tone by means of a fixedfrequency oscillator calibrated at the decibel hearing level (National Health and Nutrition Examination Survey, 2003); frequencies (125-8000 Hz) and intensities (0-110 dB) which can be varied. This unit is controlled by a microprocessor that is sometimes available as a handheld Windows-driven instrument and possesses high accuracy and efficiency (Khandpur, 2005). Such systems can be also employed with bone vibrators to test conductive hearing mechanisms. A pair of headphones is attached, and a test subject feedback button indicates when the subject has heard the sound. On the one hand, audiometry measures hearing thresholds and oral audiometry or logoaudiometry (Aguilera et al., 1997), considered to be a subjective evaluation, is defined by the capacity to evaluate the human speech understanding consists of supplying the patient with words at different energies that aid in recognizing acoustical features and in providing clues concerning the etiology of the hearing loss. On the other hand, audiometry includes supraliminal tests such as SISI*,* Fowler, Tone Decay, or Békésy, to suggest the sensorineural damage (Ghani, 2005).

**2.2.1 Pure-tone audiometry and logoaudiometry**

Figures 4 and 5 ilustrate the SISI and Békésy tests.

Fig. 4. A patient's SISI Graph with score value (Gutierrez et al., 2009)

Fig. 5. A patient's Békésy test (Gutierrez et al., 2009)

Generally, an audiometric system controlled by a microprocessor (Penhaker & Kijonka, 2011) is composed of two channels: one for test ear stimulus, and other as a non-test ear masker, as shown in the block diagram of Figure 6.

Fig. 6. General block diagram of an audiometer

Technology for Hearing Evaluation 11

when new patient data is introduced, the information of the previous test is lost. There is no database and the hardcopy audiograms are not designed for long term storage because the

This limitation is a problem for the specialist, because he/she needs to store the patient's test data so that this information can be used in clinical and research audiometric protocols. Some authors (Gutiérrez et al., 2009) have described the design and implementation of a communication and graphical module for transmitting, processing, printing, and storing the patient's special audiometric studies as a substitute for the NOAH-3™ System (see Figure 7).

The Impedance Audiometry or most commonly used Tympanometry, is a magnitude that examines the condition of the middle ear, the status and mobility of the eardrum (tympanic membrane). The equipment produces variations of air pressure in the ear canal by means of a probe measuring the middle ear's acoustic resistance. The secondary purpose of this examination is to evaluate the acoustic reflex pathway which includes the 7th and 8th cranial nerves and the brain stem. Furthermore, tympanometry permits a distinction between sensorineural and conductive hearing loss when results are not apparent via Weber and Rinne testing. It is also helpful in the diagnosis of otitis media by demonstrating the presence of middle-ear effusion. When sound impacts on the eardrum, part of the sound is absorbed and sent via the middle to the inner ear, while the remaining part of the sound is reflected. When the eardrum is inflamed over a long period of time, it can become stiff and heavy and the majority of the sound is reflected; therefore less sound reach the inner ear. Among other things, tympanometery assesses cochlear integrity and evaluates acoustic impedance, i.e., the degree of difficulty that the middle ear and ossicles encounter for the passage of sounds, as a result of the mass, stiffness and ossicular disruption of the auditory system. If there is fluid behind the ear-drum, it will not move back and forth from its resting position when pressure is applied. Tympanometry is thus affected by the mass, mobility,

The instrument, or tympanometer, applies air pressure to the eardrum and measures the reflected sound. Inside the instrument's probe, a small loudspeaker is installed that emits a tone, typically at 226 Hz, through a tube into the auditory canal in front of the eardrum. The canal's air pressure is altered between +200 and -400 decapascals (dapa), at which the sound strikes the tympanic membrane, causing middle-ear vibration, which in turn results in the perception of hearing. Some of this sound is reflected back and picked up by another tube, which is connected to the microphone inside the probe that receives the sound. Together with a third tube, all three are inserted nearly into the eardrum and are made airtight against outside pressure by the ear tip. A manometer and a pump, which can produce both positive and negative pressure, are connected to tube **C**. Less sound is reflected into the microphone when the eardrum is stiff and the eardrum transmits the majority of the sound via the middle to the inner ear. Highest compliance is normally reached with air pressure that corresponds to the outside pressure. During tympanometric measurement, a continuous change in positive and negative pressure is produced by the instrument's pump in the outer middle auditory canal. This test should not be performed in infants below 7 months of age because the suppleness of the external canal's cartilage may produce

and resistance systems of the external and middle-ear cavities.

audiometer has a thermal printer.

**2.2.2 Tympanometry**

misleading results.

An audiogram is a chart that depicts the results of the audiometric study; on the x-axis, frequencies are represented in Hz, and on the y-axis, the patient's hearing ability is expressed in dB. Two charts are usually employed: one for the right ear, and one for the left. The right ear is graphed with a circle in red color, the blue color plot the left ear with an X. Symbols and notes should correspond to the recommendations and standards of the American Speech Language Hearing Association (ASHA).

ASHA guidelines for manual pure-tone threshold audiometry *(*Campbell et al., 2010*);* these guidelines contain procedures for performing a hearing diagnosis and for monitoring standard pure-tone threshold and include manual air-conduction and bone-conduction, masking. From the inception of modern audiometric technology, staff at the Department of Audiology at the Massachusetts Eye and Ear Infirmary began using an extension of the standard symbols for designating "response at limit" and responses found using speakers (Halpin, 2007). These symbols represent new exploratory developments and are not part of the ASHA standard symbol.

The NOAH-3™ System (Hearing Instrument Manufacturers' Software Association, 2000) is a database that integrates software applications from the several manufacturers of audiologic devices, including Madsen™ (Madsen Electronics*,* 2004), Amplaid™, Interacustic™, Benson Medical™ . The main purpose of NOAH is to control data exchange from an audiometer to a personal computer, among other applications. The transmitted information is introduced into a database that is manipulated and processed to obtain audiograms, tables, measurements, and statistics, as well as to save and print the patient's study.

Fig. 7. Overview Producer-Consumer Architecture for the Audiometer Interface.

The NOAH-3™ System does not work with certain special tests (SISI, Békésy) and some audiometers, for example, Madsen™ (Gutiérrez et al., 2009), and it is not possible to transfer data for processing or printing these special tests. When the audiometer is turned off, or

An audiogram is a chart that depicts the results of the audiometric study; on the x-axis, frequencies are represented in Hz, and on the y-axis, the patient's hearing ability is expressed in dB. Two charts are usually employed: one for the right ear, and one for the left. The right ear is graphed with a circle in red color, the blue color plot the left ear with an X. Symbols and notes should correspond to the recommendations and standards of the

ASHA guidelines for manual pure-tone threshold audiometry *(*Campbell et al., 2010*);* these guidelines contain procedures for performing a hearing diagnosis and for monitoring standard pure-tone threshold and include manual air-conduction and bone-conduction, masking. From the inception of modern audiometric technology, staff at the Department of Audiology at the Massachusetts Eye and Ear Infirmary began using an extension of the standard symbols for designating "response at limit" and responses found using speakers (Halpin, 2007). These symbols represent new exploratory developments and are not part of

The NOAH-3™ System (Hearing Instrument Manufacturers' Software Association, 2000) is a database that integrates software applications from the several manufacturers of audiologic devices, including Madsen™ (Madsen Electronics*,* 2004), Amplaid™, Interacustic™, Benson Medical™ . The main purpose of NOAH is to control data exchange from an audiometer to a personal computer, among other applications. The transmitted information is introduced into a database that is manipulated and processed to obtain audiograms, tables, measurements,

Fig. 7. Overview Producer-Consumer Architecture for the Audiometer Interface.

The NOAH-3™ System does not work with certain special tests (SISI, Békésy) and some audiometers, for example, Madsen™ (Gutiérrez et al., 2009), and it is not possible to transfer data for processing or printing these special tests. When the audiometer is turned off, or

American Speech Language Hearing Association (ASHA).

and statistics, as well as to save and print the patient's study.

the ASHA standard symbol.

when new patient data is introduced, the information of the previous test is lost. There is no database and the hardcopy audiograms are not designed for long term storage because the audiometer has a thermal printer.

This limitation is a problem for the specialist, because he/she needs to store the patient's test data so that this information can be used in clinical and research audiometric protocols. Some authors (Gutiérrez et al., 2009) have described the design and implementation of a communication and graphical module for transmitting, processing, printing, and storing the patient's special audiometric studies as a substitute for the NOAH-3™ System (see Figure 7).


**2. Stimuli-responsive microgels**

Stimuli-responsive properties can be incorporated into gels. Microgels may respond to a number of stimuli like pH, ionic strength, specific ions, external fields, and temperature [14–17]. Such DDSs are designed whether to target tissues, to reach specific intracellular locations, or to promote drug release [18]. Brief overview of the types of stimuli-responsive microgels is given below.


**4. BACs and marsupial linkage maps**

Linkage (genetic) maps are a useful resource as they provide information not only on the order of genetic markers on a chromosome but on the location and frequencies of crossover events. Such maps are even more valuable if the maps are anchored to chromosomes and integrated with available genome assembly and/or cytogenetic mapping data. Linkage maps have been constructed for two marsupial species, opossum (Samollow et al., 2007) and the wallaby (Wang et al., 2011b). BACs containing markers at the ends of linkage groups have been used to cytogenetically assign these groups to chromosomes and determine the genome coverage of the linkage maps (Samollow et al., 2007; Wang et al., 2011b). The opossum linkage map was integrated with the genome assembly and cytogenetic map by FISH-mapping 34 BAC clones from the ends of linkage groups (Duke et al., 2007; Samollow et al., 2007). A sophisticated approach was used in the marker selection for construction of the wallaby linkage map to facilitate the integration of cytogenetic and linkage map data. Three strategies were developed to fill gaps in the 1st generation linkage map (Zenger et al., 2002) using information from BACs. The first strategy involved identifying microsatellites in BACs that had been previously assigned to chromosomes by FISH. The second strategy identified microsatellites within BAC end sequences and the third used the wallaby genome sequence to identify microsatellite markers near BACs that had been mapped by FISH. This resulted in a linkage map that could easily be incorporated with the physical map data to generate an integrated map (Wang et al., 2011a, 2011b). Information from the integrated map has been used to improve and anchor the tammar wallaby genome assembly (Renfree et al., 2011).


3.3.2. Analytical Comparison of Nusselt Number Nu with Re and Pressure Drop Δ*p* with *v*

As shown in Figure 9, the pressure drop across the different models of radiators increases with increasing inlet flow, and the trend of increase can be seen as approximately linear. This is due to the fact that at low inlet flow rates, the dominant flow pattern within the radiator is the laminar flow. As the inlet flow rate increases, the flow pattern within the radiator gradually changes to turbulent flow. The filling of the metal foam increases the flow disturbance within the radiator. This ultimately leads to an increase in the resistance to fluid flow, which, in turn, increases the pressure drop between the inlet and the outlet. For the same inlet flow rate, the pressure drop in the electronic radiator increases with the number of fins contained. However, at a certain inlet flow rate, the difference in pressure drop between different types of electronic heat sink is not significant. This is because the pressure drop generated by the fluid flowing through the electronic heat sink depends primarily on the metal foam itself. Moreover, the increase in the number of fins in practice results in the decrease in the fin spacing, thus, in effect, changing the width of the contact cross-section of the fluid in the direction of movement. This ultimately leads to an uneven velocity distribution of the fluid at the inlet. The pressure drop generated by the electronic heat sink also depends on the pore density of the filled metal foam. The greater the pore density, the more chaotic the metal foam's own skeleton structure becomes, which, in turn, generates greater pressure losses.

Equation (11) was used to obtain the relation curve between the *Nu* and *Re* of the radiator. As shown in Figure 10, the Nusselt number of the electronic heat sink filled with metal foam is significantly higher than the Nusselt number of the electronic heat sink not filled with metal foam. This indicates that the heat transfer capability of the heat sink filled with metal foam is enhanced. The heat transfer strength of each type of heat sink also differs under different conditions of the Reynolds number. As it can be seen from Figure 10, the Nusselt number increases with the increasing Reynolds number. Additionally, the Nusselt number increases with the increasing pore density at different pore densities, while it increases and then decreases with the number of fins contained in the electronic heat sink. As can also be concluded from Figure 10 that the Nusselt number reaches a maximum when the number of fins is three. This trend is consistent with that shown by the convective heat transfer coefficient.

Equation (16) was used to obtain the radiator resistance coefficient *f* and *Re* relation curve. As shown in Figure 11, the frictional drag coefficient gradually decreases as the Reynolds number increases. As *Re* > 3000, the trend of the decreasing frictional drag coefficient becomes progressively slower. The coefficient of frictional resistance and the Reynolds number follow the same trend for electronic heat sinks with and without metal foam filling. The drag coefficient of the electronic heat sink with metal foam filling is significantly higher than that of the electronic heat sink without metal foam filling, due to the structure of the filling metal foam itself.

**Figure 9.** Radiator pressure drop Δ*p* and *v* curve.

**Figure 10.** Relation curve between *Nu* and *Re* of radiator.

**Figure 11.** Radiator resistance coefficient of *f* and Re relation curve.


**Author details**

Nikolay Britun1 \* and Tiago Silva2

1 University of Mons, Mons, Belgium

2 University of Lisbon, Lisbon, Portugal

\*Address all correspondence to<EMAIL_ADDRESS>
© 2018 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.

**5**

*Introductory Chapter: Plasma Chemistry for Better CO2 Conversion*

[11] Kozák T, Bogaerts A. Splitting of CO2 by vibrational excitation in nonequilibrium plasmas: A reaction kinetics model. Plasma Sources Science and Technology. 2014;**23**:045004

[12] Ozkan A, Dufour T, Silva T, Britun N, Snyders R, Reniers F, et al. DBD in burst mode: Solution for more efficient CO2 conversion? Plasma Sources Science

and Technology. 2016;**25**:055005

[13] Indarto A, Yang DR, Choi JW, Lee H, Song HK. Gliding arc plasma processing of CO2 conversion. Journal of Hazardous Materials. 2007;**146**:309

[14] Silva T, Britun N, Godfroid T, Snyders R. Optical characterization of a microwave pulsed discharge used for dissociation of CO2. Plasma Sources Science and Technology. 2014;**23**:025009

[15] Chen G, Georgieva V, Godfroid T, Snyders R, Delplancke-Ogletree MP. Plasma assisted catalytic decomposition

[16] Chen G, Britun N, Godfroid T, Georgieva V, Snyders R, Delplancke MP. An overview of CO2 conversion in a microwave discharge: The role of plasma-catalysis. Journal of Physics D: Applied Physics. 2017;**50**:084001

[17] Britun N, Chen G, Silva T, Delplancke MP, Snyders R. Green Chemistry Processing and Synthesis. In: Karame I, Srour H, editors. Croatia,

Rijeka: InTech; 2017. pp. 3-27

Chemistry. 2012;**33**:1806

[18] van Rooiji GJ, Akse HN, Bongers WA, van de Sanden MCM. Plasma for electrification of chemical industry: a case study on CO2 reduction. Plasma Physics and Controlled Fusion. 2018;**60**:014019

[19] Bartolomei M, Pirani F, Laganà A, Lombardi A. A full dimensional grid empowered simulation of the CO2 + CO2 processes. Journal of Computational

of CO2. Applied Catalysis B: Environmental. 2016;**190**:115

*DOI: http://dx.doi.org/10.5772/intechopen.81921*

[1] Adamovich I, Baalrud SD, Bogaerts A, et al. The 2017 plasma roadmap: Low temperature plasma science and technology. Journal of Physics D: Applied Physics. 2017;**50**:323001

[2] Gibson ND, Kortshagen W, Lawler JE. Investigations of the 147 nm radiative efficiency of Xe surface wave discharges. Journal of Applied Physics. 1997;**81**:1087

[3] Moutoulas C, Moisan M, Bertrand L, Hubert J, Lachambre JL, Ricard A. A high‐ frequency surface wave pumped He‐Ne laser. Applied Physics Letters. 1985;**46**:323

[4] Kelly-Winterberg K, Montie TC, Brickman C, Roth JR, Carr AK, Sore K, et al. Room temperature sterilization of surfaces and fabrics with a one atmosphere uniform glow discharge plasma. Journal of Microbiology and

[5] Rusanov VD, Fridman AA, Sholin GV. The physics of a chemically active plasma with nonequilibrium vibrational excitation of molecules. Soviet Physics

[6] Fridman AA. Plasma Chemistry. New York: Cambridge University Press; 2005

[7] Asisov RI, Vakar AK, Jivotov VK, et al. The translation is: Nonequilibrium plasma-chemical process of CO2

decomposition in a supersonic microwave discharge. In: Russian Proceedings of the USSR Academy of Sciences. 1983;**271**:94

[8] Guerra V, Silva T, Guaitella O. Living on mars: How to produce oxygen and fuel to get home. EPN. 2018;**49**:15

[9] Goede APH. CO2-neutral fuels. EPJ Web of Conferences. 2015;**98**:7002

[10] Silva T, Grofulović M, Klarenaar BLM, Morillo-Candas AS, Guaitella O, Engeln R, et al. Kinetic study of lowtemperature CO2 plasmas under nonequilibrium conditions. I. Relaxation of vibrational energy. Plasma Sources Science and Technology. 2018;**27**:015019

Biotechnology. 1998;**20**:69

Uspekhi. 1981;**24**:447

**References**

*Introductory Chapter: Plasma Chemistry for Better CO2 Conversion DOI: http://dx.doi.org/10.5772/intechopen.81921*


*3.2. Compression Molding Behavior of Substrate Block*

The curves of the force–displacement responses in the compression molding process of the substrate block of measured and simulated experiments under different loading speeds are displayed in Figure 5a,b. The compressive force slightly increased when the displacement was lower than 40 mm and then increased rapidly until it reached the set displacement in both the measured and simulated processes. The first stage (displacement < 40 mm) could be considered as the linear compression process, which was mainly due to the pore reduction caused by the discharge of air and moisture. The second non-linear stage was the result of realignment of substrate particles. In this stage, the pores between the particles were filled, and their contact areas and frictional forces were increased, leading to further compression difficulties and jumpy pressures. This compression behavior was similar with other researchers, including an initial linear stage followed by a non-linear stiffening stage [20]. There was no great difference in the force–displacement curves among different loading speeds in either the measured or simulated processes. Therefore, only one set of the loading speeds (500 mm·min−1) was selected for comparison (Figure 5c). It can be seen that the compression force began to increase after being compressed to 20 mm in the simulated process, while the force increased from the beginning of the compression in the measured process. It was due to the step of particle generation in the simulation. Before compression, the generated particles happened with a certain compression stack under their own gravity. Hence, in the beginning of the compression, the contact force between the top punch and particles was very small, and the two did not even touch. When pressing through this distance, the force of the simulation process increased significantly, and it, essentially, matched the actual process when the punch reached 50 mm. The comparison of the maximum compressive force of the measured and simulated tests is exhibited in Table 7. The maximum compressive force increased with the increasing loading speed both in the measurement and simulation. At higher loading speeds, the compressed displacement of the substrate particles per unit of time was larger, and then they will receive a larger compressive force. The simulated results under different loading speeds were all higher than the measured results, which was due to the unavoidable experimental errors in practical tests. However, the relative error was lower than 15%, especially at a loading speed of 500 mm·min−1 (5.45%), which demonstrated the DEM simulations had a good match with experimental measurements, and the DEM model could capture the compression behavior of particles.

The instant particle number at the set displacement and the total accumulated particle number of three selected sections are shown in Table 8 and Figure 6. When the top punch reached the instant of the set displacement, the center and quarter sections possessed similar particle numbers around 850, while those in the side section were only in the range of 650–690 at all different loading speed conditions. Additionally, the variable coefficient was larger at higher loading speeds. This phenomenon also happened in the total accumulated particle number, and the difference between the inside section (center and quarter) and side section was greater. The average and total accumulated particle numbers decreased with increasing loading speed. It was noted that when the displacement of the punch part exceeded 40 mm, there was a trend in the accumulated particle numbers of the quarter section being larger than the center section (Figure 6). As mentioned above, the particles were subjected to the non-linear stage of particle realignment, and the center particles started to spread around, causing the increasing particle number of the quarter section. Hence, at the instant of the set displacement, the quarter section had more particles. However, in total, the center section possessed the most total accumulated particles.

**Figure 5.** Compressive force–displacement curves of compression process: (**a**) measured, (**b**) simulated, and (**c**) comparison of measured and simulated process at 500 mm·min−1 loading speed.

Note: relative error = |simulated − measured|/measured × 100%.

**Table 8.** The instant particle number and average particle number of selected sections at set displacement.

The heights of the peat substrate blocks of different loading speeds after standing for 48 h were collected: 63 (±1) mm, 64 (±1) mm, 65 (±1) mm, and 67 (±1) mm, respectively. According to the length of the tube and the moving displacement of the punch, the theoretical height of a block should be 40 mm, which indicates that the substrate blocks expanded in the height direction after compression molding. Additionally, their surfaces have cracks of varying degrees: as the loading speed increased, the location of the crack was closer to the bottom (Figure 7). At slower loading speeds, the firmness of the block from bottom to top was better, which could be reflected by the particle number. Therefore, the higher the loading speed the easier it was for the blocks to expand after molding to form cracks from the bottom. However, in order to achieve a certain production efficiency in actual production, the compression speed is, generally, fast. Therefore, in order to reduce the expansion problem of the substrate block and ensure its quality, it is recommended to explore the molding quality through some methods, such as adding agglomerants.

**Figure 6.** Total accumulated particle number on selected sections of substrate block at different loading speed: (**a**) 50 mm·min−1, (**b**) 100 mm·min−1, (**c**) 200 mm·min−1, and (**d**) 500 mm·min−1.

**Figure 7.** Image of peat substrate blocks with different loading speed after standing 48 h.