*4.5. Protein Extraction*

The cells were washed three times with phosphate-buffered saline (PBS) chilled to 4 ◦C. Whole-cell proteins were extracted with M-PER Mammalian Protein Extraction Reagent (78503, Thermo Fisher Scientific, Waltham, MA, USA), containing protease and phosphatase inhibitor (Roche, Mannheim, Germany), at 4 ◦C for 30 min. Then, the samples were centrifuged at 14,000× *g* for 10 min, and the supernatant was transferred to a new tube for analysis. Nuclear proteins were extracted in accordance with the instructions of the Nuclear and Cytoplasmic Protein Extraction Kit (P0027, Beyotime Biotechnology, Shanghai, China) for detection of the activity of NF-κB.


**Author details**

Soichiro Okamoto1 , Yu Uneno2 \*, Masanori Mori3 , Takashi Yamaguchi4 and Nobuhisa Nakajima5

1 Asoka Vihara Hospital, Kyoto, Japan

2 Seirei Hospice, Seirei Mikatahara General Hospital, Shizuoka, Japan

3 Palliative Care Team, Seirei Mikataraha General Hospital, Shizuoka, Japan

4 Division of Palliative Care, Department of Medicine, Konan Hospital, Japan

5 Division of Community-based Medicine and Primary Care, University of the Ryukyus Hospital, Okinawa, Japan

\*Address all correspondence to<EMAIL_ADDRESS>
© 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, provided the original work is properly cited.

**51**

*Communication with Families in the Last Days of a Patient's Life and Optimal Delivery…*

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**2. Anthocyanin in pigmented corn**

for this foods around 9000–7000 B.P. [1]. In different myths, leyends and codices prehispanics civilizations Olmecas, Mayan and Mexican showing the prominent position of corn. For example, one myth the Mexica gods of corn: Tell us that corn was created after the goddess Centéotl sank into the ground to make vegetables to feed the people. It was in the wake of that event that cotton, huazantle, chia, sweet potato and corn began to grow from the ground. The Mexican

At this time, corn (*Zea mays*) is the most important cereal that is produced in the world, the white and yellow corns are more used, the world production of maize was 987 million metric tons (MMT) and the United States of America (USA) is the largest producer and Mexico is the

In the world, corn is generally used for animal feed and biofuels. In Mexico, this cereal is used for making foods; maize grains are consumed fresh (elotes and esquites, boiled grains) or processed in the form of dough or cornmeal for the preparation of some foods: dishes (tortillas), corn flakes (salads and sweets totopos), starch (atoles and pinole), tamale dough (tamales), fermented foods (pozol and atoles), boiled or steamed corn (pozole), soups (chilaquiles), bakery products and another foods. Some foods and grains of maizes are depicted in **Figure 1**.

The colorful corns are less common while the white and yellow are the most popular. All parts such as silk, cob, leaves, husk and kernel of corns have been used by people at remote time to Mesoamerican civilization, the pigment corns referred to as blue, red or purple corn are botanically the same species white and yellow. This cereal was used in the preparation to color foods and beverages. The interest on pigmented (blue, red and purple) corn is due to the bioactive compounds; these are anthocyanins, *p*-hydroxycinnamic acids, flavonoids and to minor proportion carotenoids, phytoesterols, vitamin E, lignans, policosanols and xylans. The purpose of this chapter is to provide an overview of bioactive compounds and of the

Indians called corn as "the plant of the gods" [2].

70 Corn - Production and Human Health in Changing Climate

**Figure 1.** Food products elaborated with pigmented corn.

sixth producing country [3].

Anthocyanins are the largest group of phenolic pigments responsible for the pink, red, purple and blue corns which is the cereal with most anthocyanin content [4]. For that reason, the pigmented corn has caught attention in research and production. There is a great diversity in types of corn including sweet corn, popcorn, pod corn, flint corn, flour corn, waxy corn and dent corn; everyone is able to have different variety of color as shown in **Figure 3**, which give us opportunity to get a great source of anthocyanins using the whole plant because, according with the variety of corn, the silk, corn husk and corn cob could have more anthocyanins than kernel, as we will see in later section.

**Figure 3.** Purple corn and Cacahuacintle corn with purple cornhusk and corn cob.


**1. Introduction**

Today's world is undergoing unprecedented changes in the 21th century. A new round of technological revolution and industrial transformation is in the ascendant, and intelligent networked new energy vehicles (NEVs) have become the strategic direction of global industrial development [1]. In February 2020, China's 11 national ministries and commissions, including the National Development and Reform Commission, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the Ministry of Transport, jointly issued the Smart Vehicle Innovation and Development Strategy in its "Strategic Vision", which points out that: "Looking forward from 2035 to 2050, China's standard intelligent vehicle system will be fully completed and more complete. The vision of a safe, efficient, green, and civilized intelligent vehicle system has been gradually realized, and the intelligent vehicle can fully meet the people's growing needs for a better life."

**Citation:** Li, L.; Coskun, S.; Wang, J.; Fan, Y.; Zhang, F.; Langari, R. Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples. *Energies* **2021**, *14*, 3431. https://doi.org/10.3390/en14123431

Academic Editor: Francis F. Assadian

Received: 16 May 2021 Accepted: 7 June 2021 Published: 10 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The application of traffic information in the design of hybrid electric vehicles (HEVs) energy management is one of the important objects, wherein the adjustment of power distribution ratio based on traffic information is achieved by predicting the change of vehicle speed [2,3]. In order to achieve the goals of "safe", "efficient", and "green" for future intelligent networked NEVs, it is of great significance to predict and plan their future speed [4,5], because the above three goals are closely related to the speed, as shown in Figure 1. "Efficient" means that the vehicles in the traffic flow move at high speeds, which makes the roads more efficient. A vehicle driving at a higher speed increases the probability of road traffic accidents, so in order to drive "safely", the vehicle often slows down in advance in the condition of high traffic risk. This process leads to the change of vehicle speed and the problem of optimal vehicle speed planning. Accurate speed prediction is the key to energy management, reduce emissions, and improved energy-saving control of NEVs [6,7], that is, "green" driving. Therefore, it is of great significance to accurately predict and reasonably plan vehicle speed for balancing the relationship among "safe", "efficient", and "green" of vehicles in the future traffic system.

**Figure 1.** The relationship between "safety", "efficiency", "green", and speed of intelligent networked vehicles.

The automobile industry is now undergoing an electrification revolution. Take China as an example, in 2018, the production and sales of NEVs reached 1.27 million and 1.256 million units, respectively. In 2019, the production and sales of NEVs reached 1.242 million units and 1.206 million units, respectively. From January to December in 2020, the production of NEVs reached 1.366 million units, and the sales volume reached 1.367 million units [8].

The above data is briefly described in Figure 2. On the whole, China's new energy vehicle production and sales are greatly affected by government policy. In 2019, due to the retreat of subsidy policies, the production and sales of NEVs decreased compared with 2018. Due to the impact of the epidemic, China restored the subsidy policy of NEVs in 2020, and the production and sales increased accordingly.

**Figure 2.** Comparison of production and sales volume of new energy vehicles in China in recent three years.


*2.2. Measures*

Online Hate Involvement. To measure online hate involvement, three items were adopted from the work of Hawdon et al. [2]. For assessing those who were bystanders of online hate, participants were asked: "How often in the past 12 months have you observed hateful or degrading writing or speech online, which inappropriately attacks certain groups of people or individuals because of their sex, religious affiliation, race, or sexual orientation?".   Participants rated each item on a scale of 0 (never) to 4 (very frequently).

Toxic Online Disinhibition. The four-item Toxic Online Disinhibition Scale assessed the extent to which adolescents believed that they were less inhibited while interacting or engaging in certain behaviors online, with response options ranging from 0 (definitely do not believe) to 4 (definitely do believe) [22]. A confirmatory factor analysis revealed a good fit of the toxic online disinhibition scale: *χ*2 = 29.33, *df* = 8, *p* < 0.001, comparative fit index (*CFI*) = 0.99, Tucker–Lewis index (*TLI*) = 0.99, root mean square error of approximation (*RMSEA*) = 0.04, and standardized root mean square residual (*SRMR*) = 0.01. A mean score was computed by averaging all items. Cronbach's alpha was 0.79.

Control Variables. Participants were asked for their age and sex to determine demographic characteristics. Migration background was assessed by asking which language is mainly spoken at home. Family socioeconomic status was measured with the Family Affluence Scale (FAS) [24]. The FAS was trichotomized into low, medium, and high socioeconomic status.


**2.1. Modulation scheme**

The automotive 24 GHz radar sensor allows a simultaneous and unambiguous measurement of target range *R* and radial velocity *v*r even in multiple target situations. This is achieved by combining the advantages of the Frequency Shift Keying (FSK) waveform and the Frequency Modulated Continuous Waveform (FMCW) in a so called Multi Frequency Shift Keying (MFSK) waveform [19], which is already used in commercial automotive radar sensors to enable Adaptive Cruise Control (ACC) or Blindspot Detection (BSD) [20], [21]. Applying an FSK waveform, the target range *R* and radial velocity *v*r can be measured. However, there is no range resolution. Multiple objects measured at the same spectral line in the Doppler spectrum result in an unusable range information, as the determination procedure assumes a single target. To mitigate this drawback, the FMCW waveform resolves targets in range *R* and velocity *v*r. Limitations will occur in this case in multi target situations due to ambiguous measurements. The specific MFSK waveform is applied in the 24 GHz Radar sensor for a range and Doppler frequency measurement even in multi target situations with a bandwidth of *f*sweep = 150 MHz and a resulting range resolution of Δ*R* = 1.0 m. It is a classical step-wise frequency modulated signal with a second linear frequency modulated signal in the same slope but with a certain frequency shift *f*step integrated into this waveform in an intertwined way. The chirp duration is denoted by *T*CPI = 39 ms which results in a velocity resolution of Δ*v* = 0.6 km/h.

It is important to notice that this waveform is not processed by a matched filter or analyzed by an ambiguity function. Instead it is processed in a non-matched filter form to get an unambiguous and simultaneous target range and Doppler frequency measurement with high resolution and accuracy. The echo signal of the stepwise and intertwined waveform is downconverted by the corresponding instantaneous transmit frequency into baseband and sampled at the end of each short frequency step. This time discrete signal is Fourier transformed separately for the two intertwined signals to measure the beat frequency *f*B which is simultaneously influenced by the target range *R* and radial velocity *v*r.

2 Will-be-set-by-IN-TECH

24 and 77 GHz band are also strong candidates for automotive safety systems. Compared with vision systems, they have some additional important advantages of robustness in all weather conditions, simultaneous target range and radial velocity measurement and a high update rate. These properties are especially important for pedestrian recognition, as the object

Object class? Object class?

**Figure 1.** Daily traffic situation in an urban area with an oncoming vehicle and pedestrians walking on

This chapter presents the modulation scheme of an automotive radar sensor and explains the features of pedestrians and vehicles by which a robust classification is possible in an urban

The automotive 24 GHz radar sensor allows a simultaneous and unambiguous measurement of target range *R* and radial velocity *v*r even in multiple target situations. This is achieved by combining the advantages of the Frequency Shift Keying (FSK) waveform and the Frequency Modulated Continuous Waveform (FMCW) in a so called Multi Frequency Shift Keying (MFSK) waveform [19], which is already used in commercial automotive radar sensors to enable Adaptive Cruise Control (ACC) or Blindspot Detection (BSD) [20], [21]. Applying an FSK waveform, the target range *R* and radial velocity *v*r can be measured. However, there is no range resolution. Multiple objects measured at the same spectral line in the Doppler spectrum result in an unusable range information, as the determination procedure assumes a single target. To mitigate this drawback, the FMCW waveform resolves targets in range *R* and velocity *v*r. Limitations will occur in this case in multi target situations due to ambiguous measurements. The specific MFSK waveform is applied in the 24 GHz Radar sensor for a range and Doppler frequency measurement even in multi target situations with a bandwidth of *f*sweep = 150 MHz and a resulting range resolution of Δ*R* = 1.0 m. It is a classical step-wise frequency modulated signal with a second linear frequency modulated signal in the same slope but with a certain frequency shift *f*step integrated into this waveform in an intertwined way. The chirp duration is denoted by *T*CPI = 39 ms which results in a velocity resolution of

It is important to notice that this waveform is not processed by a matched filter or analyzed by an ambiguity function. Instead it is processed in a non-matched filter form to get an unambiguous and simultaneous target range and Doppler frequency measurement with

area from a moving vehicle with a mounted 24 GHz radar sensor, see Figure 1.

classification should be available immediately and at any time.

the sidewalk.

Δ*v* = 0.6 km/h.

**2.1. Modulation scheme**

**Figure 2.** MFSK waveform principle with two intertwined transmit signals.

$$f\_B = -\frac{2v\_\mathrm{r}}{\lambda} - \frac{2R \cdot f\_{\mathrm{sweep}}}{\mathrm{c}} \cdot \frac{1}{T\_{\mathrm{CPI}}} \tag{1}$$

In any case, a single target will be measured and will be detected on the same spectral line at position *f*B for the two intertwined signals. Therefore, after the detection procedure the phase difference ΔΦ between the two complex-valued signals on the spectral line *f*B will be calculated. The step frequency *f*step between the intertwined transmit signals determines the unambiguous phase measurement ΔΦ in the interval [−*π*;*π*). This phase difference ΔΦ again is influenced by the target range *R* and radial velocity *v*r described in Equation (2).

$$
\Delta\Phi = -\frac{2\pi}{f\_{\text{sample}}} \cdot \frac{2v\_{\text{r}}}{\lambda} - \frac{4\pi R \cdot f\_{\text{step}}}{c} \tag{2}
$$

The target range *R* and radial velocity *v*r can be determined by solving the linear equation described in Equation (1) and (2) in an unambiguous way. In this case, ghost targets are completely avoided since this waveform and signal processing combines the benefits of linear FMCW and FSK technology. The system design and the sensor parameters can be determined like in a linear FMCW radar system. The range and velocity resolution Δ*R* and Δ*v* are determined by the bandwidth *f*sweep of the radar sensor and the chirp duration *T*CPI as described in Equation (3) and (4), respectively.

#### 4 Will-be-set-by-IN-TECH 244 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications Pedestrian Recognition Based on 24 GHz Radar Sensors 5

$$
\Delta R = \frac{c}{2} \cdot \frac{1}{f\_{\text{sweep}}} \tag{3}
$$

pedestrian, the velocity profile is less extended due to the moving direction of the pedestrian. Furthermore, the extension depends mainly on the azimuth angle under which the pedestrian is measured. In contrast, the radar echo signal in case of a vehicle shows a very narrow (*point*

Additionally, a *point shaped* range profile will occur in the case of a longitudinally or laterally moving pedestrian as the physical expansion is small compared to the range resolution of Δ*R* = 1.0 m. In contrast, a vehicle shows an *extended* range profile, due to several reflection points spaced in several range cells. The measurement result of a single observation is shown

0 4 8 12

Rprofile,ped

v

profile,veh

Pedestrian Recognition Based on 24 GHz Radar Sensors 245

Rprofile,veh

Velocity [m/s]

Under the use of an MFSK modulation signal, a range profile and the velocity profile can be extracted from a series of received signals as shown in Figure 4. As an example, four consecutive range and velocity measurements are depicted in a range Doppler diagram. The red dots show a longitudinally walking pedestrian, the blue crosses an in front moving vehicle. The figures depicted are based on radar measurements taken in an urban area with an ego speed of 50 km/h. It can be observed that neither velocity profile nor a range profile can be seen in the first measurement, consequently, those feature values are zero. In the second measurement, however, several range and velocity measurements allow to calculate an extended range profile for the vehicle and an extended velocity profile for the pedestrian.

(a) Measurement 1 (b) Measurement 2 (c) Measurement 3 (d) Measurement 4

*shaped*) velocity profile due to a uniform motion.

12

**Figure 3.** Range profile and velocity profile of a single measurement.

**Figure 4.** Sequence of range and velocity measurements.

16

v

profile,ped

20

Range [m]

24

in the range Doppler diagram in Figure 4.

$$
\Delta v = -\frac{\lambda}{2} \cdot \frac{1}{T\_{\rm CPI}} \tag{4}
$$

The table below shows the system parameters of the automotive radar sensor in detail.

**Table 1.** 24 GHz Radar Sensor Parameters.

Classical UWB-Radar Sensors have a sweep bandwidth of *f*sweep = 2 GHz. Using such a bandwidth, a high range resolution is determined, which allows also pedestrian classification. The technical challenge in this chapter is to realize pedestrian recognition based on a 24 GHz radar sensor with a bandwidth of only 150 MHz. This sensor is used in automotive applications, therefore an extension of the signal processing in terms of pedestrian classification is desirable.