6. TPS modeling capabilities

The previously introduced modeling procedure has been applied on a conceptual RLV shape created with the model described in Section 4 and detailed in [20, 21]. Figure 6 shows a topological map obtained for an arbitrarily chosen distribution of stick primitives.

A local thickness is assigned on the nose, the leading edge, and the trailing edge. The topological map shown in Figure 6 creates a morphologically adaptive TPS on two RLV shapes with different dimensions: (RLV-1) with length ltot = 9.8 m, wingspan ws = 5.6 m, cabin height h = 1.6 m, and (RLV-2) with length ltot = 15 m, wingspan ws = 9.2 m, and cabin height h = 2 m. The parameters characterizing the distribution of thickness and of the materials are reported in Table 1. Figure 7a, b shows the application of TPS modeling over the first configuration (RLV-1), on leeward (a) and windward (b) surface, respectively. Different colors denote different values of thickness and are represented in a dimensional scale.

Parametric Integral Soft Objects-based Procedure for Thermal Protection System Modeling… DOI: http://dx.doi.org/10.5772/intechopen.85603

Figure 6. Topological map created to represent TPS thickness on different RLV configurations.

Figure 7.

Example of thickness and material distribution over RLV configuration (RLV-1): (a, b) thickness modulation [m]; (c, d) two material map (red/blue color indicates material 1/0, respectively).

with normalized parameters reported in Table 1.

min, ad 0.07 th'

min, ad 0.132 th"

Parameters adopted in the modeling of TPS configurations of Figures 7 and 8.

The previously introduced modeling procedure has been applied on a conceptual RLV shape created with the model described in Section 4 and detailed in [20, 21]. Figure 6 shows a topological map obtained for an arbitrarily chosen distribution of

max, ad 0.12

max, ad 0.25

Parameter Value Parameter Value st1, ad 0 mt1, ad 1 st2, ad 0.01 mt2, ad 0.01 st3, ad 0.05 mt3, ad 0.05 st4, ad 1 mt4, ad 1 st5, ad 0.8 mt5, ad 0.8 lt1, ad 1 mlt1, ad 1 lt2, ad 0.1 mlt2, ad 0.1 lt3, ad 1 mlt3, ad 1 lt4, ad 1 mlt4, ad 1.2 lt5, ad 1 mlt5, ad 1 pt1, ad 1 \_ \_ pt2, ad 0.2 \_ \_ pt3, ad 0.5 \_ \_ pt4, ad 0.2 \_ \_ pt5, ad 0.6 \_ \_ d1min, ad 0.5 d1max, ad 1 d2inin, ad 0.01 d2max, ad 0.3 d3min, ad 0.09 d3max, ad 1 d4min, ad 0.1 d4max, ad 0.5 d5min, ad 0.02 d5max, ad 0.5

Hypersonic Vehicles - Past, Present and Future Developments

A local thickness is assigned on the nose, the leading edge, and the trailing edge. The topological map shown in Figure 6 creates a morphologically adaptive TPS on two RLV shapes with different dimensions: (RLV-1) with length ltot = 9.8 m, wingspan ws = 5.6 m, cabin height h = 1.6 m, and (RLV-2) with length ltot = 15 m, wingspan ws = 9.2 m, and cabin height h = 2 m. The parameters characterizing the distribution of thickness and of the materials are reported in Table 1. Figure 7a, b shows the application of TPS modeling over the first configuration (RLV-1), on leeward (a) and windward (b) surface, respectively. Different colors denote differ-

ent values of thickness and are represented in a dimensional scale.

6. TPS modeling capabilities

stick primitives.

24

th'

th"

Table 1.

modeling procedure suggest that the present methodology can give support to a multidisciplinary analysis optionally included in a conceptual design framework. Further developments of the considered procedure are about to be integrated in a

Parametric Integral Soft Objects-based Procedure for Thermal Protection System Modeling…

This work was supported by the Universitá della Campania: "Luigi Vanvitelli."

The authors declare that there is no conflict of interest regarding the publication

companion paper by the authors [23].

DOI: http://dx.doi.org/10.5772/intechopen.85603

Acknowledgements

Conflict of interest

of this chapter.

Author details

Aversa, Italy

27

Andrea Aprovitola, Luigi Iuspa and Antonio Viviani\*

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Engineering, Università degli Studi della Campania "L. Vanvitelli",

© 2019 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,

Figure 8.

Example of thickness and material distribution over RLV configuration (RLV-2): (a, b) thickness modulation [m]; (c, d) two material map (red/blue color indicates material 1/0, respectively).

It can be observed that the thickness map can be easily tuned up for best covering of regions where maximum heat loads occur (i.e., the nose and leading edge). Figure 7 shows the capability to create arbitrary seamless thickness distribution up to the value of the baseline thickness which has been arbitrarily set equal to thmin = 0.05 m (denoted in blue color). This corresponds to a region of the leeward surface not covered by the skin stick. Figure 7c, d shows the map of two different insulating materials created with Eq. (7). Red colors indicate material 1, which is placed on regions of the vehicle subjected to higher heat loads. Comparisons between Figure 7a, b and Figure 7c, d also exhibit the capability of the model to handle independently both the thickness and material distribution. Finally, Figure 8a, b and Figure 8c, d show the same blob distribution adopted either for thickness or material modeling applied on a different RLV configuration (RLV-2). The procedure creates, as it was expected, the same TPS distribution both for thickness or materials on two different shapes and is completely independent by their morphology.


**4.3 Phenolic and polyphenols compounds**

The secondary metabolites are polyphenols. They are a collection of chemical substances found in aquatic macrophytes and terrestrial plants. Phenolic compounds are present in edible plants, and their structure contains benzene [71]. Plants frequently contain phenolic chemicals. Polyphenols contain tannins, phenolic acids, flavonoids, tocopherols, and lignin.

**Table 3.** *Algal metabolites.*


*Edited by Francisco Veas*

The two volumes of Acute Phase Proteins book consist of chapters that give a large panel of fundamental and applied knowledge on one of the major elements of the inflammatory process during the acute phase response, i.e., the acute phase proteins expression and functions that regulate homeostasis. We have organized this book in two volumes - the first volume, mainly containing chapters on structure, biology and functions of APP, the second volume discussing different uses of APP as diagnostic tools in human and veterinary medicine.

Photo by Uguntina / Shutterstock

## Acute Phase Proteins as Early Non-Specific Biomarkers of Human and Veterinary Diseases

Acute Phase Proteins as Early Non-Specific Biomarkers of Human and Veterinary Diseases

ISBN 978-953-307-873-1

*Edited by Francisco Veas*


Introductory Chapter: Virtual Assistants

*Ali Soofastaei*

## **1. Introduction**

The application of Virtual Assistants (VAs) is growing fast in our personal and professional life. It has been predicted that 25% of households using a VA will have two or more devices by 2021 [1]. A virtual assistant is an intelligent application that can perform tasks or provide services for a person responding to orders or inquiries. Some VAs can understand and respond to human speech using synthesized voices. Users may use voice commands to request their VA to answer the questions, manage home appliances, control media playing, and handle other essential activities like email, creating the actions lists, and organize the meetings on calendars [2]. In the Internet of Things (IoT) world, an VA is a popular service to communicate with users based on voice command.

VA capabilities and usage are rapidly rising, thanks to new technologies reaching the people's requirements and a robust focus on voice user interfaces. Samsung, Google, and Apple each have a considerable smartphone user base. Microsoft's Windows-based personal computers, smartphones, and smart speakers have an intelligent VA installed base. On Amazon, smart speakers have a sizable installed base [3]. Over 100 million people have used Conversica's short message and email interface Intelligent Virtual Assistants (IVAs) services in their companies.

Famous virtual assistants like Amazon Alexa and Google Assistant are typically cloud-based for maximum performance and data management. Many behavioral traces, including the user's voice activity history with extensive descriptions, can be saved in a VA ecosystem's remote cloud servers during this process.

The VAs story started in the 1910s, and the growth of technology has supported VAs' improvement. The application of Artificial Intelligence (AI) also was a turning point in VAs journey. Using AI to develop the VAs was a great jump to increase the VAs' capabilities. Currently, VAs use narrow AI with limited options. However, using general AI in the near future can be a revolution to improve the quality of VAs' services.

### **2. Backgrounds**

#### **2.1 Investigational years: 1910s: 1980s**

In 1922, an interesting toy named Radio Rex was introduced that was the first voice-activated doll [4]. A toy in the dog shape would appear from its den the moment it was given a name.

Bell Labs introduced the "Audrey," which was an Automatic Digit Identification device in 1952. It took up a six-foot-high relay rack, used much power, had many wires, and had all of the issues that come with complicated vacuum-tube

electronics. Despite this, Audrey was able to discriminate between phonemes, which are the basic components of speech. However, it was restricted to precise digit identification by assigned speakers. As a result, it may be utilized for voice dialing. However, push-button dialing was generally less expensive and faster than pronouncing the digits in order [5].

Another early gadget that could carry out digital language identification was Shoebox voice-activated calculator that IBM developed. It was revealed to the public for the period of the 1962 Seattle World's Fair after its first market debut in 1961. This initial machine, which was built nearly twenty years earlier than the first Personal Computer made by IBM and debuted in 1981, was capable of detecting sixteen verbal phrases and the numbers 0 through 9.

ELIZA, the first Natural Language Processing (NLP) application or chatbot, was invented by MIT in the 1960s. ELIZA was designed in order to "show that manmachine interaction is essentially superficial" [6]. It applied configuration matching and replacement procedures in written reactions to simulate conversation, creating the impression that the machine understood what was being said.

The ELIZA was designed by professor Joseph Weizenbaum. During the ELIZA development period, Joseph's assistant has requested that he leave the room so that she and ELIZA can chat. Professor Weizenbaum later remarked, "I had no idea that brief exposures to a really simple computer software might cause serious delusional thinking in otherwise normal people [7]." The ELIZA impact, or the tendency to instinctively believe machine activities are equal to people's behaviors, was called after this. Anthropomorphizing is a phenomenon that occurs in human interactions with VAs.

When DARPA funded a five-year Speech Understanding Research effort at Carnegie Mellon in the 1970s, the goal was to reach a vocabulary of 1,000 words. Participants included IBM, Carnegie Mellon University (CMU), and Stanford Research Institute, among many others.

The result was "Harpy," a robot that could understand speech and knew around 1000 words, roughly equivalent to a three-year vocabulary. To reduce voice recognition failures, it could also analyze speech that followed pre-programmed vocabularies, pronunciations, and grammatical patterns to determine which word sequences made sense when spoken.

An improvement to the Shoebox was released in 1986 with the Tangora, a speech recognition typewriter. With a vocabulary of 20,000 words, it was able to anticipate the most likely outcome based on its information. Because of this, it was given the name "Fastest Typewriter. As part of its digital signal processing, IBM used a Hidden Markov model, which integrates statistics into the Using this strategy, you may anticipate which phonemes will follow a given phoneme. However, every speaker was responsible for training the typewriter to recognize his or her voice and halt in.

#### **2.2 The beginning of intelligent virtual assistants: 1990s: Present**

To compete for customers in the 1990s, companies such as IBM, Philips, and Lemont & Hauspie began integrating digital voice recognition into personal computers. The first smartphone introduced in 1994, the IBM Simon laid the groundwork for today's smart virtual assistant.

In 1997, Dragon's Biologically Talking application was able to detect and transcribe natural human speech at a pace of 100 words per minute, with no gaps between syllables. Biologically Talking is still accessible for download, and many doctors in the United States and the United Kingdom continue to use it to keep track of their medical records.

*Introductory Chapter: Virtual Assistants DOI: http://dx.doi.org/10.5772/intechopen.100248*

In 2001, Colloquies released Smarter Child on AIM and MSN Messenger, among other platforms. "Smarter Child" can play games and check the weather as well as seek up data. It can even speak with others to a certain extent, even if it is text.

Siri, which debuted on October 4, 2011, as an option of the iPhone 4S, was the first innovative digital VA to be placed on a smartphone [8, 9]. Siri was built when Apple Inc. purchased Siri Inc. in 2010, a spin-off of SRI International, a research institute financed by DARPA and the US Department of Defense [10]. It was created to make texting, making phone calls, checking the weather, and setting the alarm easier. In addition, it can now make restaurant recommendations, perform Internet searches, and offer driving directions.

Amazon debuted Alexa alongside the Echo in November 2014. Later, in April 2017, Amazon launched a facility that allows users to create conversational interfaces for any VA or interface.

From 2017 till 2021, all the VAs mentioned above have been developed, and there are the more intelligent VAs using for individuals and professional activities. The companies in different areas use the VAs to improve the quality of their decisions at different levels, from operation to the high management level.


**2.3 Assessing status and trends**

*Water Quality - Science, Assessments and Policy*

**Physical habitat parameters**

C)

Dissolved oxygen—DO (mg/L)

Total organic carbon—TOC (%) % Silt/clay (grainsize) **Water-quality parameters** Chlorophyll *a* (μg/L) Ammonium (mg N/L) Nitrate plus nitrite (mg N/L)

Dissolved inorganic nitrogen—

Total nitrogen—TN (mg N/L) Dissolved inorganic phosphate—

Total phosphorus—TP (mg P/L)

Diversity and abundance of fish **Human health indicators**

Diversity and abundance of benthic macroinvertebrates

PCBs, PBDEs and PFCs in fish tissue (Great Lakes only)

*reported as dry weight of sediments and wet weight of tissue.*

Temperature (°

Secchi depth (m)

Nitrite (mg N/L)

DIN (mg N/L)

DIP (mg P/L)

*Hyalella azteca*

*a*

**Table 1.**

**Biotic conditions**

**Sediment toxicity** Amphipod survival bioassay Estuarine test organisms: *Leptocheirus plumulosus Eohaustorius estuarius* Great Lakes test organism:

Salinity (ppt)

pH

**National Coastal Condition Assessment Indicators**

**Physical habitat parameters Ecological contaminants in sediments and fish tissue**a

mirex, trans-nonachlor

Zn

**Metals (μg/g)**: Ag, Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, Sn, Tl,

**PAHs (ng/g)**: acenaphthene, acenaphthylene, anthracene, benz(a) anthracene, benz(a,e)pyrene, benzo(b,k,b+k)flouranthene, benzo(g,h,i)perylene, biphenyl, chrysene, dibenz(a,h) anthracene, fluoranthene, fluorene, indeo(1,2,3-c,d)pyrene, naphthalene, 1-methylnaphthalene, 2-methyl naphthalene, 2,6-dimethylnaphthalene, 2,3,5-trimethylnaphthalene, perylene, phenanthrene, 1-methylphenanthrene, pyrene, total PAHs

**PCB congeners (ng/g)**: 8, 18, 28, 29, 44, 52, 66, 87, 101, 105, 118, 128,

**Pesticides (ng/g)**: aldrin, chlordane (alpha-, gamma-, oxy-), dieldrin, dibenzothiophene, DDD (2,4'; 4,4'), DDE (2,4'; 4,4'), DDT (2,4'; 4,4'), endosulfan I & II, endrin, heptachlor, heptachlor epoxide, hexachlorobenzene, hexachlorohexane (alpha-, beta-, delta-), lindane,

138, 153, 170, 180, 187, 195, 201, 206, 209, total PCBs

The field data were submitted as either physical or electronic data sheets to NCCA headquarters for compilation. Preserved water, sediment, and fish samples were shipped to approve national or state laboratories for analysis and results were submitted to NCCA headquarters. Each site generated hundreds of field and laboratory data values that were organized into files by type (e.g., field data, water quality data, benthic census data, etc.), and maintained as "raw files" in a centralized database by information management specialists. The raw files were then subjected to a stringent two-phase QA review process, first checking for basic compliance with submission requirements (e.g., proper units, range checks, and conformity with standard taxonomic terminology). Any revisions to the raw files were carefully documented, and finalized files were made available at the NCCA

*Indicators measured in the National Coastal Condition Assessment (NCCA) surveys.*

Mercury in fish plugs; algal toxins (microcystin and cylindrospermopsin) and enterococcus in water

*National Oceanic and Atmospheric Administration National Status and Trends Program analytes. Concentrations*

One of the hallmarks of the NCCA, and the NCA which preceded it, has been the emphasis on the cooperation and participation of the states and tribes in planning and conducting the assessment within their respective jurisdictions. Not only are states and tribes key to survey implementation, they are the entities responsible

**136**

public website [26].

Following the lead of earlier EPA coastal surveys, the NCCA approach had two primary goals regarding assessment: (1) evaluate the *status* of four major components of coastal ecosystems—the water column, sediment, and benthic and fish communities, and (2) ascertain how conditions *change over time (i.e., trends)*. For each of the key assessment components, conditions were evaluated based on a suite of core indicators and indices constructed from them. For instance, water quality was assessed using five measured indicators (concentrations of nutrients, chlorophyll, and dissolved oxygen, and water clarity) and a water quality index was then crafted from the five components. The assessment process first evaluated conditions at each site, rating each indicator and index as good, fair, or poor based on regionally determined assessment thresholds. Details regarding the indicators, indices, and thresholds used in assessments are presented in Section 3 of this chapter.

Once sites were evaluated, regional and national conditions were calculated. Recall that the survey design process had assigned each site a weighting factor equal to the area represented by the station. Regional assessments were then expressed as the percent of the region in good, fair, poor, or unassessed condition. For instance, the percent area of the Pacific coast in good condition was simply calculated as the sum of weighting factors associated with Pacific sites rated as good, divided by the total area of the Pacific coastal region (sum of all Pacific site weights). Assessments were calculated for the nation, for the five primary reporting regions (**Figure 2**), and for any state or designated research area containing a statistically-sufficient number of sites.

The survey design procedure further provided a measure of the uncertainty in the condition estimates, expressed as the 95th percentile confidence interval (CI), which was calculated as the binomial proportion confidence interval adjusted for possible spatial gradients in indicator measurements [23, 27]. Operationally, the confidence intervals were calculated using a complex computer-intensive algorithm, coded in the R-programing language, available at EPA's Aquatic Resource Monitoring website [25].

As the number of surveys conducted increases, the NCCA documents change over time. Typically, trends have been evaluated by analyzing what happens at an individual location, much as a physician monitors trends in the weight of an individual patient. In contrast, trends for NCCA were evaluated at the population level, i.e., trends in the proportion of sites in good condition. These population level trends were evaluated by noting statistically significant changes, i.e., condition estimates displaying non-overlapping CIs, determined over a series of comparable surveys. Since the early 1990s, coastal survey methods have evolved significantly over time. In some cases, new analyses can be applied to old data. In other cases, methodological differences have precluded trend analyses over the entire 30-year period. Eventually, trends in national assessments will reflect only NCCA surveys conducted from 2010 onward.


**Author details**

Nihal Seden Ear Nose Throat Clinic, Istanbul Training and Research Hospital, University of Health Sciences, Istanbul, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 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.


**3. Discussion**

Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer-related death among women worldwide [28]. Despite the long list of drugs that have been used in the treatment of breast tumors, marked drug multi-resistance during treatment remains one of the main problems facing the clinic today when applying a therapy against breast cancer. Among various mechanisms of chemo-resistance, resistance due to the increased expression of P-gp is the best characterized and it is considered the most important in cancer therapy. The significance of the above mentioned is also supported by the fact that more than 50% of existing anticancer drugs are going to P-gp mediated efflux [29]. Since the results of clinical trials using P-gp inhibitors have not been very promising, the search for new modulators which selectively inhibit P-gp activity without significant negative side effects is currently under high interest and hopes are pinned on the application of some plant polyphenols [11].

P-gp may affect the pharmacokinetic parameters of drugs or other compounds, possibly leading to modifications of their bioavailability as well as of their distribution, metabolism, elimination and toxicity (ADMET) [30]. Together with several CYP450 isoenzymes, P-gp is involved in drug-drug, food-drug and, finally, herb-drug interactions [31]. Herbal medicines may interact with drugs at the intestine, liver, kidneys, and targets of action. Importantly, many of these drugs have very narrow therapeutic indices. Most of them are substrates for P-gp. The underlying mechanisms for most described herb-drug interactions are not entirely understood, and several pharmacokinetic and/or pharmacodynamic interferences are often implicated in these interactions. In particular, enzyme induction and inhibition may play an important role in the occurrence of some herb-drug interactions. Because herb-drug interactions can significantly a ffect circulating levels of drug and, hence, alter the clinical outcome, the identification of herb-drug interactions has important implications [32]. As present results suggest the species *Ageratina havanensis* (Kunth) R. M. King & H. Robinson could be a source of new potential inhibitors of drug e fflux mediated by P-gp; focus on these aspects must be taking into account for future studies.

Today it is known that P-gp function and its regulation can be mediated via reactive oxidative species (ROS) [33–37]. Therefore, the transport of P-gp substrates may be altered by modulation of P-gp expression/activity under conditions of oxidative stress [38]. In this sense, natural antioxidants, such as polyphenols, could influence the function of this membrane transporter. It was reported that polyphenolic compounds, mainly flavonoids or their derivatives, can modulate the main ABC transporters responsible for cancer drug resistance, including P-gp [16,39–41].

The interest in health benefits of polyphenols has increased due to their powerful antioxidant and free radical scavenging activities observed in vitro [42]. Current evidence strongly supports a contribution of polyphenols, in particular flavonoids, to the prevention and therapy of cancer and other chronic diseases [1,43]. Literature data have shown that plant phenolic content and antioxidant activity depend on several factors, mainly environmental conditions [3]. Previous studies show that summer plants (reproductive stage) are richer in phenolic compounds than spring ones (vegetative stage), and consequently exhibit higher antioxidant activities [2,3]. Here, we evaluated the antioxidant potential of the *Ageratina havanensis* extracts by using three in vitro methods of well-established use to strongly predict the antioxidant capacity of several substances [22–24]. But surprisingly, the results of the performed studies showed the opposite, the concentration of flavonoids is higher in the vegetative state. In fact, we observed that *Ageratina havanensis* extracts possess higher reductive capacity in the vegetative period. This finding might be related to the content of sakuranetin and 7-methoxyaromadendrin, which is greater in the vegetative stage. The antioxidant capacity of sakuranetin has been previously demonstrated [44]. Indeed, here we observed that sakuranetin possesses the greatest reductive capacity of the analyzed samples. In order to evaluate the antioxidant capacity in a biological system, we used the lipid peroxidation model in brain homogenates. Rat brain homogenates exposed to oxygen spontaneously exhibit lipid peroxidation by a mechanism which is independent of superoxide and free hydroxyl radical production and whose initiation step may involve and iron-mediated cleavage of lipid hydroperoxides to yield peroxide or alkoxy radicals [45,46]. In this system, the extracts obtained from the flowering stage e ffectively inhibited TBARS generation with estimated values (see Table 1). This result could be explained in term of several mechanisms comprising among others, iron chelation, (at the initiation step) or a synergistic antioxidant activity (at the propagation step). In addition, previous studies demonstrated the metal chelation properties of polyphenols [47,48]. The present results suggest that *Ageratina havanensis* could be represent a source of antioxidants with potential neuroprotective actions. In summary, these results show for first time the antioxidant potential of the extracts obtained from the species *Ageratina havanensis*.