**Meet the editor**

Hyoung Woo OH is Professor of Mechanical Engineering at Chungju National University. He received his B.S., M.S. and Ph.D. in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST). His research areas are industrial applications of computational fluid dynamics (CFD) and turbomachinery design and performance analysis. Dr OH is the author or co-author of dozens of journal papers.

Contents

**Preface IX**

Chapter 1 **Computational Fluid Dynamics 1**

Victor Udoewa and Vinod Kumar

Chapter 2 **A Computational Fluid Dynamics Model of**

Chapter 3 **Advances in Computational Fluid Dynamics**

Enrique Rico-García, Irineo L. López-Cruz

Antonio Viviani and Giuseppe Pezzella

Chapter 5 **Air Movement Within Enclosed Road-Objects**

M. Muhasilovic, A. Mededovic, E. Gacanin, K. Ciahotny and V. Koza

and Hermann Nirschl

Chapter 7 **CFD and Thermography Techniques**

Chapter 8 **Computational Fluid Dynamics (CFD)**

**with Contra-Traffica CFD-Investigation 73**

Chapter 6 **Computational Fluid Dynamics (CFD) and Discrete**

Xiana Romani Fernandez, Lars Egmont Spelter

**Applied in Cooling Systems Designs 135**  Samuel Santos Borges and Cassiano Antunes Cezario

**Modeling of Photochemical Reactors 155**

Masroor Mohajerani, Mehrab Mehrvar and Farhad Ein-Mozaffari

Ali Hadi Ghawi and Jozef Kriš

and Abraham Rojano-Aguilar

Chapter 4 **Fluid Dynamics Analysis of**

**Flow and Settling in Sedimentation Tanks 19**

**Applied to the Greenhouse Environment 35**  Jorge Flores-Velázquez, Guillermo De la Torre-Gea,

**a Space Vehicle Entering the Mars Atmosphere 63**

**Element Method (DEM) Applied to Centrifuges 97**

## Contents

#### **Preface XI**

X Contents

## Preface

This book is intended for use as a reference text to help advanced scientists and research engineers solve numerous and diverse fluid flow problems using computational fluid dynamics (CFD). Each chapter has been contributed by the practiced experts in the field of fluid dynamics. This material covers a wide spectrum of CFD applications involving flow modeling in sedimentation tank, greenhouse environment, hypersonic aerodynamics, computational scheme, cooling systems design, photochemical reaction engineering, atmospheric reentry problem, fluid‐ structure interaction (FSI), atomization, hydraulic component design, air conditioning system, and industrial applications of CFD.

#### **Dr. Hyoung Woo Oh**

Department of Mechanical Engineering, Chungju National University, Chungju, Korea

**1**

*USA*

**Computational Fluid Dynamics**

*AAAS Science & Technology Policy Fellow, AAAS, 2009-2011,*

Computational Fluid Dynamics (CFD) is the emerging field of fluid mechanics in which fluid flow problems are solved and analyzed using computational methods and numerical algorithms. In fluid mechanics, there are generally three routes of work in the field, three ways to conduct experiments. The first category is theoretical, or analytical, fluid mechanics. Theoretical fluid mechanics includes theorizing, manipulating and solving equations with pen and paper. The Navier-Stokes equation governing incompressible fluid flow is an example of theoretical fluid mechanics. Secondly, many engineers and physicist work in the area of experimental fluid mechanics. Experimental fluid mechanics involves conducting actual physical experiments and studying the flow and the effect of various disturbances, shapes, and stimuli on the flow. Examples include waves generated by pools, air flow studies in actual wind tunnels, flow through physical pipes, etc. Lastly, a growing number of engineers, mathematicians, computer scientists, and physicists work in the area of computational fluid dynamics (CFD). In CFD, you may still run an experiment of waves across water, an airplane in a wind tunnel, or flow through pipes, but now it is done through the computer Instead of actual, physical, 3D objects. A computer model is created, and computer programmers code the equations representing the physical laws that govern the flow of the molecules of fluid. Then the flow results (such as velocity and pressure) are output into files that can be visualized through pictures or animation so that you see the

In cases where an analytical, or theoretical, solution exists, CFD simulations and the mathematical models, which are coded in the computer program, are corroborated by comparison to the exact solutions. This comparative check is called validation. CFD is not yet to a point where solutions to problems are used without corroboration by existing, known, analytical or exact solutions when available. Validation is not to be confused with verification, however; validation is a check to make sure that the implemented, coded model accurately represents the conceptual, mathematical description and the solution intended to

Still, there are many times when there is no analytical solution. In these cases, one often uses a computational approach. In such cases without a known solution, CFD is used to

**1. Introduction**

be modeled.

result just as you do with physical experiments.

Victor Udoewa1 and Vinod Kumar2

*1George Washington University, USAID Development Engineer,*

*2Mechanical Engineering, University of Texas at El Paso,*


**3. Results**

### *3.1. Phase I Study*

In total, 12 patients were enrolled in the phase I study. None of the three patients in the cohort at level 1 developed DLT. When the docetaxel dose was elevated to level 2, one patient experienced DLT, a grade 3 nonhematologic toxicity. The patient had phenytoin intoxication, which might be due to impairment of the P450 metabolic pathway by S-1. Because the remaining three patients in the cohort at level 2 had no DLT, their docetaxel dose was elevated. At level 3, two patients had DLT, prolonged grade 3 myelosuppression, and grade 3 interstitial pneumonia. The safety committee emphasized the risk of interstitial pneumonia and recommended the termination of the phase I study at level 3. As a result, the MTD and recommended docetaxel dose were 60 mg/m2 and 50 mg/m2, respectively.


Some authors have pointed out that organic foods contain higher concentrations of nutritionally beneficial trace elements and lower concentrations of harmful heavy metals [9]. In fact, vegetables can uptake and retain these inorganic elements from the surrounding environment through their roots and leaves [10]. However, the data existing in the bibliography on this topic are inconclusive, and it is difficult to make a valid comparison between both vegetable groups due to the limited availability of well-controlled or paired studies [11].

Therefore, for all the above reasons, the objectives of this research were (a) to compare the concentrations of nine inorganic elements (Co, Cr, Cu, Cd, Fe, Ni, Mn, Pb and Zn), well known both for their nutritional and toxicologic role in 3 species of *Brassicaceae (Brassica rapa, Eruca vesicaria* and *Sinapis alba*) grown under both conventional and organic conditions during two agricultural seasons (2018/2019 and 2019/2020) on two different experimental farms; (b) to make a probabilistic estimation with computer software (@Risk) of the contributions of the inorganic elements present in these vegetables to the recommended intakes or to the toxicologic limits established for them. This was to find out whether organicallygrown *Brassicas* have a greater nutritional value than the conventionally-grown ones.

The novelty of this work lies in the fact that few long-term studies have been made comparing vegetables grown under organic and conventional conditions during two agriculture seasons.

#### **2. Material and Methods**

#### *2.1. Plant Material*

Genotypes selected from 3 plant species belonging to the *Brassicaceae* family were chosen based on previous studies showing their differences in their glucosinolates profile and trace elements concentration: *Brassica rapa* L. (turnip greens and top greens), *Eruca vesicaria* L. (rocket) and *Sinapis alba* L. (white mustard). Turnip greens are the young leaves harvested in the vegetative growth period, and turnip tops are the fructiferous stems with flower buds and the surrounding leaves that are consumed before opening and while still green. These plant species are well adapted to Mediterranean environmental conditions and have been obtained by the Plant Breeding Group at the Institute for Sustainable Agriculture (IAS-CSIC) after several generations of breeding for seed yield and glucosinolate content.

This material was sown and cultivated during two seasons, 2018–2019 and 2019–2020, in two Farms (I and II) in Southern Spain (see Figure 1).

The Farm I land (37◦51′ N, 4◦48′ W) is located in Córdoba, next to the Guadalquivir River, in a position of the first terrace (altitude of 106 m), with a deep soil (Typic Xerofluvent) of sandy-loam texture with high pH (around 8), intermediate organic matter content (1.6%), and high carbonate content (17%). The experimental plot size for conventional cultivation of *Brassicaceae* species on Farm I was 25 × 25 m. The climate is typically continental Mediterranean (Csa in Köppen's climate classification), with relatively cold winters, intensely hot dry summers and mean annual precipitations of 650 mm. On this Farm, the three Brassicaceae species were only grown under conventional conditions with herbicides and mineral fertilization being applied. In pre-sowing, an herbicide with triflularin as its active matter was used at a dose of 1.5 L/ha. Moreover, before sowing, a basic dressing with 8-15-15 bottom fertilizer was applied at a rate of 600 kg/ha. A top dressing (cover fertilization) with 300 kg/ha of Ammonium Nitrate was applied after the winter stop at the resumption of vegetative growth.

**Figure 1.** Two Farms in the Southern Spain (A: Farm I. Alameda del Obispo (Córdoba). *Brassicaceae* grown in Conventional Cropping System; B: Farm II. Ribera Alta. Alcalá la Real (Jaén). *Brassicaceae* grown in Conventional and Organic Cropping System. The distance between both Farms is 121 km.

Farm II is located in the municipal district of Alcalá la Real (Jaén) (37◦27′ N 3◦55′ W, Spain) in the Sub-Baetic zone, next to the Velillos River (altitude 920 m) with a moderately stony structure and clay loam texture (Xerofluvent-Fluvisol calcareous) with high pH (8.2), high organic matter content (3%), and high carbonate content (16%). The experimental plot size for conventional and ecological cultivation of *Brassicaceae* species in Farm II were 25 × 25 m each. Both experimental plots were close together and separated only by a 2-mwide border. The climate is typically continental Mediterranean (Csa in Köppen's climate classification), with short summers, very hot, arid and mostly cloudless, winters are long, very cold and partially cloudy and mean annual precipitations of 650 mm. In this Farm, the three *Brassicaceae* species were grown both under conventional and organic conditions.

′ ′

The conventional cultivation conditions on Farm II were similar to those of the Farm I. In organic cultivation, neither herbicides nor mineral fertilizers were applied. Instead, only treatment with a mixture of goat and sheep manure was applied at a rate of 3 kg/m2 .

When plants from the different species reached their optimal moment of consumption (from 3 to 5 months after sowing), leaf samples from individual plants of each species were harvested, pooled, and processed for chemical analysis. The number of analyzed plants throughout the two-years duration of the study were turnip greens (*Brassica rapa*) (n = 60), turnip tops (*Brassica rapa*) (n = 85), *Eruca vesicaria* (n = 18), and *Sinapis alba* (n = 12). The higher number of harvested *Brassica rapa* samples is due to turnip greens and turnip tops having good commercial prospects, and their consumption, both fresh and processed, has considerably increased in the last years. Furthermore, *Sinapis alba* and *Eruca vesicaria* are currently minority crops consumed only in salads, although their consumption may increase in the future due to their special composition in glucosinolates with medicinal properties (Sinalbine in *Sinapis alba* and Glucorafanine in *Eruca vesicaria*).

Plants were thoroughly washed with tap water to remove dirt and dust, and they were finally rinsed with deionized water. Then, they were stored at −80 ◦C until freezedrying, which was done in Telstar® model Cryodos-50 equipment (Telstar, Terrasa, Spain). The freeze-dried samples were ground in a Janke and Kunkel Model A10 mill (IKA-Labortechnik, Staufen, Germany) for about 20 s, and stored in a desiccator until their analysis.

#### *2.2. Materials and Reagents*

All the reagents were of an analytical-reagent grade. Ultrapure water (18 MΩ/SCF) prepared with a Milli-Q Reference Water Purification (Millipore, Madrid, Spain) was used throughout the experiments. All the glassware and plastic containers were soaked in 50% nitric acid overnight, then in 20% hydrochloric acid for an additional night and rinsed three times with de-ionized water prior to use. Hyperpure nitric acid (65%) and hydrochloric acid (35%) were obtained from Panreac (Barcelona, Spain). Hydrogen peroxide (33%) was acquired from Sigma Aldrich (St. Loius, MO, USA).

Standard solutions for measuring the elements Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn were prepared immediately before use by dilution with distilled deionized water of 1000 mg/L standard solutions (Certipur-Merck, Darmstad, Germany).

#### *2.3. Trace Element Determination*

To determine the trace element content (Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) of *Brassicaceae* species, 0.5 g of freeze-dried sample was weighed in a porcelain crucible. Samples were incinerated in a muffle furnace at 460 ◦C for 15 h. The ash was bleached after cooling by adding 200 µL of hyperpure HNO3 and 1 mL of deionized water, drying this on thermostatic hotplates, and maintaining it in a muffle furnace at 460 ◦C for 1 h more. Ash recovery was performed with 100 µL of hyperpure HNO3, making up to 10 mL with deionized water. To Cd analysis, in order to avoid Cd volatilization, 0.5 g of freeze-dried sample was placed in a Teflon vessel. Then, 3 mL of of hyperpure HNO3 and 2 mL of H2O2 were added to each vessel and kept for 10 min at room temperature. After sealing the vessels hermetically, they were placed in a microwave oven (Multiwave GO, Anton Paar, Germany) and digested following the instrumental parameters indicated by the manufacturer. Every sample was diluted up to a volume of 20 mL with ultrapure water

Elemental analyses for Fe (λ = 248.3 nm; Slit width = 0.2 nm), Mn (λ = 279.5 nm; Slit width = 0.2 nm) and Zn (λ = 213.9 nm; Slit width = 0.7 nm) were performed by flame absorption atomic spectroscopy (FAAS) with a Varian Spectra AA-50B model, equipped with standard air-acetylene flame, and single- element hollow cathode lamps. Finally, electrothermal atomic absorption spectroscopy (ET-AAS) was used for the determination of Cd, Co, Cr, Cu, Ni and Pb (Agilent Technologies model 240Z AA with a graphite furnace and autosampler). This equipment was certified by an equipment qualification report (EQR; Agilent Technologies). In this latter, analytical methodology was developed following the instrumental parameters indicated by the manufacturer with slight modifications (Table 1). For Cd and Pb analysis, a chemical modifier (200 mL solution) was prepared containing a mixture of 0.1% Paladium matrix modifier 10 g/L (Merck, Spain) plus 0.06% Magnesium nitrate hexahydrate in 10 mL HNO3 hyperpure solution (69%). For each measurement, 15 µL of sample and 5 µL of modifier solution were injected. The accuracy and precision of the different analytical techniques used in determining trace element concentrations were validated by recovery experiments using Certified Reference Materials (Table 2).

**Table 1.** Instrumental conditions for Cd, Co, Cr, Ni and Pb analysis by ET-AAS in *Brassicaceae* samples.

**Table 2.** Analysis of certified references materials (mean ± standard deviation), limit of detection and limit of quantification.

\* Indicative value.

#### *2.4. Statistical Analyses and Risk Assessment*

The IBM SPSS 25 statistical software package was used for statistical analysis. The data were expressed as mean and standard deviation. Data were analyzed using ANOVA tests. Significant differences were considered when *p* < 0.05.

A probabilistic model was developed to estimate the intake level for Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn derived from feeding with Brassica vegetables. It should be pointed out that in developing the model, only the concentrations of trace elements for the *Brassica rapa* species were considered as it is the one most consumed in Spain, Portugal and Southern Italy, being part of very traditional recipes. Nowadays, turnip greens and turnip tops have good commercial prospects, and their consumption, both fresh and processed, has considerably increased in the last years. *Eruca vesicaria* and *Sinapis alba* have only eaten in small amounts in some salads, and they have a slightly spicy flavor like mustard greens.

This model followed a probabilistic approach in which variables were described by probability distributions, and they were fitted to concentration data obtained in this study for each element. Additionally, to estimate the intake level, serving size was considered assuming 15–20 g per day (around 150–200 g of fresh matter) of *Brassica rapa* (turnip greens or turnip tops). Daily intake was defined by a uniform distribution in the probabilistic model, meaning that all values in that range had the same probability to occur.

The probability distributions describing the trace element concentration data were fitted using @Risk v7.5 (Palisade, Newfield, NY, USA). The simulation ran with 10,000 iterations per element. The goodness of fit assessed how well the fitted distribution described the data; in this section, the Akaike Information Criterion (AIC) and Chi-square statistical tests were used. Additionally, the visual analysis was considered to assess the fit of the probability distributions to intake data. Data obtained through this probabilistic model was

compared to the Spanish DRI for adult population [12]. In the case of Cd, Ni and Pb [13–15] considering that they are heavy metals, tolerable intakes (µg/day) were considered.

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

*3.1. Trace Element Contents in Brassicaceae Species: Conventional Versus Organic*

The concentrations of Cu, Mn, and Zn for organic turnip greens (*Brassica rapa*) grown on Farm II were 7.4; 100.4 and 24.4 µg/g d.w (see Table 3). The concentrations of these trace elements in ones conventionally grown on the same farm were practically the same, with values of 7.5; 110.8 and 22.9 µg/g d.w. Conversely, there were statistically significant differences for Zn (*p* < 0.05) and Cu, Mn (*p* < 0.01) between the turnip greens grown on Farm I (conventional system) and those grown on Farm II (both conventional and organic systems). The highest concentrations of Cu and Zn (11.3; 38.9 µg/g d.w) and the lowest one Mn (56.0 µg/g d.w) found in turnip greens grown on Farm I compared to those analyzed on Farm II, demonstrate that, together with farming systems, the trace element concentrations in foodstuffs depend on many other factors, including soil characteristics, pollution from anthropogenic sources, genetic factors, seasonal influences and interactions between the elements [16].

**Table 3.** Total trace elements concentration (dry matter) in *Brassicaceae* species analyzed (mean ± standard deviation).

The Cu, Mn and Zn in turnip tops grown under the three experimental conditions were Farm I, conventional, 8.1; 24.2 and 29.9 µg/g d.w. respectively; Farm II, conventional 4.6; 25.5 and 25.1 µg/g d.w.; Farm II, organic, 9.0; 24.8 and 31.2 µg/g d.w. The concentrations in the turnip tops of the three trace elements were lower than those analyzed in the corresponding turnip greens. This could be explained by considering that turnip greens are the vegetative *Brassica rapa* leaves, whereas the turnip tops are the fructiferous stems with flower buds and the surrounding leaves. Previous studies have indicated that metals tend to accumulate preferentially in roots rather than in storage organs or fruits [17,18]. Moreover, unlike what happens to the turnip greens, statistically significant differences were found for Cu (*p* < 0.05) and Zn (*p* < 0.01) between the conventional and organic turnip tops grown on Farm II, the highest concentrations being found in the organic ones. These results are in agreement with those reported by Kelly and Bateman [9] for similar studies made with other vegetables species (tomatoes and lettuces)

Cu, Mn and Zn concentrations in *Sinapis alba* were 6.0; 17.8 and 23.8 µg/g d.w for the organic cropping system and 5.6; 18.7 and 25.9 µg/g d.w for the conventional one. Similarly, concentrations for these trace elements in *Eruca vesicaria* were 10.2; 48.1 and 33.0 µg/g d.w for the organic system and 9.3; 33.2 and 34.2 µg/g d.w for the conventional one. Zn

concentrations found in these *Brasicaceae* species are in agreement with those reported by Cámara–Martos et al. [3] in a previous study (*Sinapis alba* 20.8 µg/g d.w; *Eruca vesicaria* 23.5 µg/g d.w). However, our work again failed to find statistically significant differences in the concentrations of these trace elements between organic and conventional agriculture.

The results appearing in the bibliography do not show any clear trend for these trace elements either. Krejcova et al. [19], in conventionally-grown carrots, have shown a higher content of Mn and Cu but a lower content of Zn than in organic ones. Hadayat et al. [18], for organic lettuce, potato and carrot reported higher Cu contents than those in the same conventional vegetables. However, for conventional lettuce and carrot, higher Zn concentrations than those in the organic ones, were also found.

On the other hand, although in our study no differences were found in Mn concentrations between conventionally-grown *Brassicas* and organic ones, some authors have demonstrated that a lower mean concentration of Mn in organic crops is a common pattern. This could be due to the high concentrations of *arbuscular mycorrhizal fungi* in organic soils [9,20]. Although, this aspect has not been completely clarified. Other studies have shown that Mn is used as an additive to livestock feed supplements and, in turn, that this trace element would be present in the manure used in organic farming [9].

Co concentrations in turnip greens (*Brassica rapa*) were very similar in the three studied conditions (Farm I conventional 0.23 µg/g d.w; Farm II conventional 0.28 µg/g d.w and Farm II organic 0.22 µg/g d.w, with no significant differences between them (Table 3). Regarding Cr contents in turnip greens, in organic plants harvested on Farm II, lower concentrations (1.20 µg/g d.w) (*p <* 0.01) than in conventional ones of Farm I (2.41 µg/g d.w) and Farm II (2.06 µg/g d.w) were found. These results are in agreement with those reported by Krejcova et al. [19], who also found higher Cr concentration in conventional carrots (0.059 µg/g) than in organic carrots (0.046 µg/g). We have presumed that the main chemical form in which Cr is found in *Brassicaceae* vegetables, would be Cr (III). This chemical form is considered as being a beneficial element for human health, and according to several previous research works [21,22], it is the main chemical form (unlike Cr(VI) in which Cr is found in waters and foods. Therefore, according to their Co and Cr content, organic turnip greens would not have a higher nutritional value than conventional grown ones.

For the same reason as that already mentioned for the previous trace elements, Co and Cr concentrations in turnip tops (*Brassica rapa*) also decreased with respect to the corresponding turnip greens (*Brassica rapa*) (Farm I, conventional, 0.14 and 0.87 µg/g d.w.); (Farm II, conventional, 0.16 and 0.52 µg/g d.w.); (Farm II, organic, 0.19 and 0.48 µg/g d.w.) Again we find differences in Cr concentrations between turnip tops grown on the two Farms but not between the cropping systems (conventional versus organic). Thus, there were significant statistical differences for Cr contents (*p* < 0.05) between conventional turnip tops grown on Farm I and conventional turnip tops grown on Farm II. We also found statistically significant differences for Cr contents (*p* < 0.01) between conventional turnip tops grown on Farm I and organic ones grown on Farm II. Therefore, this again indicates that the total inorganic element content in vegetables does not only depend on the farming system [16].

*Sinapis alba* showed higher Cr concentrations (*p* < 0.01) for conventionally produced vegetables (0.51 µg/g d.w.) than organically ones (0.26 µg/g d.w.). Co contents for this *Brassicaceae* species were below the quantification limit (LOQ < 0.07 µg/g). On the other hand, Co and Cr contents in *Eruca vesicaria* were Farm I, conventional, 0.39 and 7.17 µg/g d.w; Farm II, conventional, 0.26 and 3.29 µg/g d.w, and, Farm II, organic 0.44 and 5.17 µg/g d.w. Cr contents in *Eruca vesicaria* are slightly higher with those reported in a previous study (2.59 µg/g) [3]. In addition, there were no statistically significant differences for both elements between organic and conventional *Eruca vesicaria*. Previous studies have reported that there is no clear trend in this matter. Thus, Hadayat et al. [18] gave higher Co contents in conventional potato, onion, tomato and carrot versus organic ones, whereas Cr contents were higher for organic onion, carrot and potato.

Regarding Fe, we found statistically significant differences (*p* < 0.01) between organic turnip greens (*Brassica rapa*) (0.10 mg/g d.w.) and conventional ones (0.19 µg/g d.w.) from Farm II; and between organic *Sinapis alba* (0.07 mg/g d.w.) and conventional one (0.15 mg/g d.w.) from Farm II (Table 3). Similarly, Krejcova et al. [19] have also reported higher Fe content for conventional carrots (5.24 µg/g) versus organic ones (4.96 µg/g). Nevertheless, for the rest of the vegetable *Brassicaceae studied*, the differences between conventional and organic cultivation were scant. In relation to this latter aspect, Kelly and Bateman [9] observed only minor variations in Fe concentrations between tomatoes and lettuces cultivated on organic and conventional farms.

While the above trace elements have a clear nutritional role, Pb and Cd are considered to be heavy metals that have harmful effects on the environment and human health. With respect to Ni, although the nutritional and/or toxicologic role of Ni in humans is unclear, in animal models, severe Ni deficiency can affect vision, Fe metabolism, and Na homeostasis [23]. However, high concentrations of this element can also affect vital processes in plants and induce toxic effects at morphologic, physiologic and biochemical levels [24].

For this latter element, we found statistically significant differences (*p* < 0.05) for Ni content in turnip tops (*Brassica rapa*) grown by organic agriculture (0.85 µg/g d.w.) on Farm II and turnip tops grown under conventional agriculture (1.02 µg/g d.w.) on the same Farm, with the lowest concentrations in organic ones (see Table 4). Similarly, Krejcova et al. [19] have also reported a lower Ni content in organic carrots (0.79 µg/g) than in conventional ones (1.58 µg/g). Other vegetables such as tomato and onion have also shown lower Ni contents when they are grown by organic systems. Conversely, other vegetable foodstuffs such as organic wheat (semolina samples) have shown higher Ni content than conventional wheat.

**Table 4.** Total heavy metals concentration (dry matter) in *Brassicaceae* species analyzed (mean ± standard deviation).

Ni values for conventional turnip tops grown on Farm I were (0.81 µg/g d.w.) and Farm II (1.02 µg/g d.w.) with statistically significant differences (*p* < 0.05) between them. Trace element content in organic *Sinapis alba* was below the quantification limit (LOQ < 0.18 µg/g) whereas Ni values for *Eruca vesicaria* ranged between 1.76–3.99 µg/g d.w without significant differences between conventional and organic system. These results are in agreement with those found in a previous study [3] for this latter *Brassica* specie (1.12 µg/g).

Regarding Pb, there were statistically significant differences (*p* < 0.05) between organic turnip greens (*Brassica rapa*) (0.33 µg/g d.w.) and conventional ones (0.62 µg/g d.w.) from Farm II (Table 4). Nevertheless, for the rest of the *Brassicaceae* studied, the differences between conventional and organic cultivation were scant. The influence of the soil in which the plants are grown has also been demonstrated. Thus, we found statistically significant differences (*p* < 0.01) for *Eruca vesicaria* between conventional plants grown on Farm I (3.40 µg/g d.w.) and plants grown on Farm II (0.55 µg/g d.w.). Furthermore, while for organic and conventional turnip tops grown on Farm II, Pb concentrations were below the quantification limit (LOQ < 0.160 µg/g), concentrations in conventional turnip tops grown on Farm I reached mean values of 0.90 µg/g d.w.

There are no conclusive results regarding a lower Pb content in vegetables grown through organic agriculture. Thus, Hadayat et al. [18] found lower Pb concentrations in organic tomato, lettuce, onion and carrot but not in potato. Krejcova et al. [19] reported higher Pb contents in conventional carrots (0.064 µg/g) versus organic ones (0.043 µg/g). However, Zaccone et al. [16] found higher contents of this heavy metal in organic wheat (94 µg/g) versus that in conventionally grown wheat (82 µg/g). Finally, Karavoltsos et al. [25] have indicated that, although the majority of organic vegetables may have lower Pb content, organic agriculture as such does not necessarily reduce the content of this heavy metal in organically cultivated products.

Another heavy metal whose consumption is aimed to reduce with the development of organic agriculture is Cd. Nevertheless, in the present study, we only found statistically significant differences for Cd levels (*p* < 0.01) between organic (0.07 µg/g d.w.) and conventional (0.23 µg/g d.w.) (Table 4) *Sinapis alba* grown on Farm II. For the rest of the *Brassicaceae* cultivated on Farm II, Cd values were very similar both in organic and conventional plants. Comparing our results with those in a previous study, Hadayat et al. (2018) reported lower Cd concentrations in organic tomato, onion, carrot and potato but not in lettuce. Cámara–Martos et al. [26] for infant foods, such as weaning jars, made with organic vegetable ingredients, it was also found that Cd concentrations were considerably lower than those reported in weaning formulas which were not categorized as organic. Krejcova et al. [19] showed slightly higher mean Cd concentrations in conventional carrots (0.066 µg/g) versus organic ones (0.060 µg/g), and Hoefkens et al. [11] indicated significant higher or lower concentrations and even non-significant differences in Cd concentrations, depending on the food matrix.

A factor that again influenced the Cd concentrations in *Brassicaceae* was the soil in which they were grown. Thus, we found statistically significant differences in Cd levels (*p* < 0.05) for conventional *Eruca vesicaria* grown on Farm I (0.72 µg/g d.w.) and those conventionally grown on Farm II (0.48 µg/g d.w.)

According to Karavoltsos et al. [25], organic agriculture could eventually lead to the production of foodstuffs with a lower heavy metal content, although organic agriculture as such is not able to secure low metal contents in its products. Our results show that this final content is also influenced by other factors such as soil, vegetable variety or even the presence of these elements in the air and in irrigation waters.

#### *3.2. Probabilistic Assessment: Conventional Versus Organic*

As already mentioned in the Material and methods section, a probabilistic model approach was developed to estimate the intake level of trace elements, which were derived from the consumption of 15–20 g (around 150–200 g of fresh matter) of these *Brassicas*. It should be pointed out that in developing the model, only the concentrations of trace elements for the *Brassica rapa* species (turnip greens and turnip top) were considered as they are the one most consumed.

Dietary reference intakes (DRI) for the Spanish population were considered [12]. There is not DRI for Co; however, this element represents approximately a 4.3% of vitamin B12. Considering a DRI for vitamin B12 between 2–2.4 µg, this corresponds to around 0.10 µg/day [27]. In the case of Ni [14] and Cd [13], considering that they are heavy metals,

the tolerable intake (TI) of 2.8 µg/kg body weight·day and 2.5 µg/kg body weight·week (0.36 µg/kg body weight·day) was used. For Pb a benchmark dose (BMDL01) for cardiovascular effects (1.50 µg/kg body weight·day) was considered [15]. It should also be noted that the present statistical tool was completed using the variability of inorganic elements present in plants as well as the variability of the *Brassica* vegetable ingested. Both aspects determine the total intake of the inorganic elements.

Thus, the results obtained from the simulation of the probabilistic model with conventional *Brassicas,* indicated values for Co, Cr, Cu, Fe, Mn and Zn of 2.58 µg, 17.41 µg, 0.14 mg, 1.74 mg, 0.55 mg and 0.56 mg, respectively, for 50th percentile (Figure 2). This shows that the intake of trace elements of at least half of the population consuming these conventional *Brassicas* will not be lower than these values. That intake fulfills Co DRI and complies with the following percentages of DRI for studied elements: Cr 69.6%, Cu 12.7%, Fe: 9.7%, Mn 30.6% and Zn 8.0%. When the same simulation of the probabilistic model is developed with concentrations belonging to organic *Brassicas*, the values obtained for 50th percentile were 3.61 µg, 9.60 µg, 0.12 mg, 1.20 mg, 0.65 mg and 0.49 mg for Co, Cr, Cu, Fe, Mn and Zn, respectively (Figure 3), which satisfy similarly to the conventional *Brassicas* the DRI percentages for these trace elements.

Regarding heavy metals, we have considered intakes for 95th percentile as being the most unfavorable situation. Thus, Ni, Pb and Cd intakes for 95th percentile with conventional *Brassicas* were 39.00, 47.34 and 6.82 µg, respectively (Figure 4), whereas intakes with organic *Brassicas* were 25.70, 15.55 and 4.56 µg for Ni, Pb and Cd, respectively (Figure 5). According to these values, organic *Brassicas* led to a decrease in the intake of these three elements metals. However, when these results were expressed as percentages of TI for these elements, we only found slight differences (mainly in the case of Pb) between both agriculture systems. The TI percentages for a mean body weight of 70 kg per person were 19.9, 45.1 and 27.1% for Ni, Pb and Cd, respectively, with conventional *Brassicas,* and 13.1, 14.8 and 18.1 % for organic *Brassicas*. These results indicate that Cd, Ni and Pb contents in vegetable *Brassica rapa* harvested under both conventional and organic farming conditions are below the accepted safety limits and do not represent any toxicologic risk.

**Figure 2.** Simulated data and fitted probabilistic distribution for Co, Cr, Cu, Fe, Mn and Zn in conventional *Brassicas*.

**Figure 3.** Simulated data and fitted probabilistic distribution for Co, Cr, Cu, Fe, Mn and Zn in organic *Brassicas*.

**Figure 4.** Simulated data and fitted probabilistic distribution for Cd, Ni and Pb in conventional Brassicas.

**Figure 5.** Simulated data and fitted probabilistic distribution for Cd, Ni and Pb in organic Brassicas.

**Author Contributions:** F.C.-M., conceptualization, methodology, validation, formal analysis, investigation, resources, writing, visualization, supervision, project administration, funding acquisition; J.S.-M., formal analysis; L.R.-P., investigation, resources, supervision; J.B.-H., resources, supervision; A.d.H.-B., conceptualization, methodology, investigation, resources, writing, visualization, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Project "Desarrollo de Alimentos Funcionales y Nutracéuticos Con Efectos Antioxidantes, Antitumorales y Cardiosaludables a partir de una Selección de Crucíferas Mediterráneas" Ref. UCO–1261749-R of the University of Cordoba-Regional Council for the Economic and Knowledge of Junta de Andalucía, which was co-financed by the European Regional Development Fund (ERDF 2014–2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Acknowledgments:** The authors thank Diana Badder, English speaker, for linguistic review of the manuscript.

**Conflicts of Interest:** Declarations of interest from all the authors: none.

#### **References**

*Review*


**4. Applications in neuromorphic computing**

Recent implementations of emerging computing capabilities leverage on the capability offered by non-volatile memory (NVM) storage and information processing. Two main approaches have been proposed:

Energy efficiency benefits of array computing have also been demonstrated with various technologies (including spin-torque oscillators) in relatively small-scale circuits. For example, in [89], a data clustering algorithm mapped to a memristive array was demonstrated. In [90], the FPAA architecture was used to implement neuronal array-based sparse coding, applicable in the early stages of visual processing. Furthermore, integration of memristors and an FPAA circuit was demonstrated in small scale in [91]. Cognitive computing algorithms, which can be mapped to array processing/associative memory architectures, have also been described [92–94]. However, hardware architectures and design tools to realize these algorithms energyefficiently on a relevant scale do not currently exist.

The challenge in the application of memristor technology for large-scale memory-based computing architectures is the development of new physical device models for memristor devices and analyzes the adaptation of algorithms with respect to device variation and scalability. Since the discovery of memristive behavior at the nanoscale at Hewlett Packard laboratories in 2008, the scientific community has devoted a large deal of efforts to derive suitable models that capture the nonlinear dynamics of memristors. Pickett's model is a reference model that is well suited for describing the physical mechanisms at the origin of memristor dynamics. Simplified versions aiming at fitting the behavior of Pickett's model are the TEAM and V-TEAM models, Biolek's model and the boundary condition model for a comprehensive review [95]. It is worth noting that such models are not oriented toward nonlinear circuit synthesis. In order to effectively analyze the dynamic behavior of memristors, and also in view of their simulation and emulation, it is fundamental to develop circuit memristor models, that is, models obtained by interconnecting basic nonlinear blocks. This will be pursue along the lines of the general method for device modeling in [96] and exploiting recent techniques for the identification of switching and PWA (piece-wise-affine) systems.

In this respect, metal electrode ions injection has also been proposed as a possible mechanism for the onset of switching, which would indicate the establishing of a CB mechanism [81]. Nevertheless, proofs of the actual onset of a VCM are provided through the observation of switching with C-AFM measurements, where no top electrode is present: analyses have been conducted both on the top surface of the anodic oxide [49], and on a lateral device, where no

Memristive nanotubes were also produced on titanium [86–88]. In these cases, either longer anodizing times (hours) and/or higher voltages (up to 120 V) are required, and thicker oxides, some hundreds of nanometers thick, are achieved. Yet, the limited adherence and mechanical stability of these oxides compared with compact ones, and the higher thickness introduced,

Recent implementations of emerging computing capabilities leverage on the capability offered by non-volatile memory (NVM) storage and information processing. Two main approaches

• Hybrid logic/memory integration: the logic and the memory layers are implemented in two different substrates or levels, typically an Application Specific Integrated Circuit (ASIC) is developed to emulate artificial neuron functionality while memory layers are integrated either on a separate chip or occupy a separated area. The main advantage lies in the improved communication bandwidth between the different logic and memory layers. • Logic-in-memory: memory elements are distributed in a circuit to play a role in the realization of the logic operations, aiming both at ultra-low power and highly expressive logic circuits. Thus, general-purpose computation functions can be implemented by configuring non-volatile switches. NVMs are naturally suited for performing implication logic instead than standard logic. Recently, stateful logic operations, for which memristor devices work as gates and latches that use resistance as a physical state variable, have been demon-

Energy efficiency benefits of array computing have also been demonstrated with various technologies (including spin-torque oscillators) in relatively small-scale circuits. For example, in [89], a data clustering algorithm mapped to a memristive array was demonstrated. In [90], the FPAA architecture was used to implement neuronal array-based sparse coding, applicable in the early stages of visual processing. Furthermore, integration of memristors and an FPAA circuit was demonstrated in small scale in [91]. Cognitive computing algorithms, which can be mapped to array processing/associative memory architectures, have also been described [92–94]. However, hardware architectures and design tools to realize these algorithms energy-

The challenge in the application of memristor technology for large-scale memory-based computing architectures is the development of new physical device models for memristor devices

electrode metals are available [85].

have been proposed:

strated [89].

make them less appealing for real applications.

54 Advances in Memristor Neural Networks – Modeling and Applications

**4. Applications in neuromorphic computing**

efficiently on a relevant scale do not currently exist.

There is an increasing interest in the implementation of oscillators using nanoscale devices as memristors. As remarked in [97], a source of controllable chaotic behavior that can be implemented by a single scalable electronic device and incorporated into a neural-inspired circuit may be an essential component of future computational systems. In this framework, the memristor is required to display a quasi-static voltage-current characteristic with a negative differential resistance (NDR). Various classes of relaxation oscillators displaying a tunable range of periodic and chaotic self-oscillations have been implemented during recent years and their importance in neuromorphic applications, such as pattern recognition and signal processing tasks in real time, have been demonstrated. They can also be used as core devices with a rich variety of nonlinear dynamics within the framework of reservoir computing architectures. Work so far has been mainly based on experimental and phenomenological observations of oscillations and complex phenomena, while a circuit model and a clear analytic understanding of the underlying nonlinear dynamics and bifurcations is basically missing. Recently, a new method, named Flux-Charge Analysis Method (FCAM), has been developed to effectively analyze a wide class of nonlinear circuits containing ideal memristors in the flux-charge domain [98]. FCAM permits to bring back the dynamic analysis to that of a lower-order circuit, with respect to that in the standard voltage-current domain, using flux and charge as state variables. This enables to obtain a clear picture of the dynamical behavior displayed by memristor circuits. In particular, some peculiar aspects, such as the presence of invariant manifolds and the coexistence of different dynamics for the same set of (fixed) circuit parameters, are singled out. Also, it is possible to assess the presence of a new interesting phenomenon of bifurcations which emerge without changing the system parameters, namely, bifurcations due to changing the initial conditions for the state variables for a fixed set of circuit parameters (BWP) [99]. Using FCAM, the dynamics of classes of oscillators and chaotic circuits with ideal memristors have been deeply analyzed assessing the occurrence of Hopf and period-doubling BWPs and quite rich complex dynamics. In addition, it has been shown that FCAM can be combined with techniques, such as the harmonic balance method citare, to effectively analyze and control such BWPs. Moreover, by suitably exploiting BWPs, it turned out that different chaotic dynamics in a class of Chua's oscillators can be programmed by means of suitable current or voltage pulses [100]. Synchronization aspects in arrays of coupled oscillators have been analyzed as well [101].


**2. Investigation of Shamir's protocol based on commutative cryptography**

Shamir's protocol was described in the book [6]. This protocol can be executed for any cryptosystem (both symmetric and asymmetric) if they satisfy the following condition:

$$f\_{K\_{\mathcal{A}}}\left(f\_{K\_{\mathcal{B}}}(\mathcal{M})\right) = f\_{K\_{\mathcal{B}}}\left(f\_{K\_{\mathcal{A}}}(\mathcal{M})\right),\tag{1}$$

where *f KA*ð Þ *KB* ð Þ *M* is encryption algorithm using the keys *KA* and *KB*, respectively and *M* is any message. If relation (1) is true for some encryption algorithm *f KA*ð Þ *KB* ð Þ *M* then can be performed protocol presented in **Figure 1**.

If the relation (1) is valid, then we get:

$$\mathbf{C}'\_{A} = f\_{K\_{A}}^{-1}(\mathbf{C}\_{\mathcal{B}}) = f\_{K\_{A}}^{-1}\left(f\_{K\_{\mathcal{B}}}\left(f\_{K\_{A}}(\mathbf{M})\right)\right) = f\_{K\_{A}}^{-1}\left(f\_{K\_{A}}\left(f\_{K\_{\mathcal{B}}}(\mathbf{M})\right)\right) = f\_{K\_{\mathcal{B}}}(\mathbf{M}) \tag{2}$$

On the next step user B decrypts *f KB* ð Þ *M* by the algorithm *f* �1 *KB* ð Þ� and gets the message *M*. But the following question arises—for which cryptosystems the relation (1) is valid? Let us call such encryption algorithms, for which (1) is true, *commutative encryption* (CE). It is easy to verify that for such cryptographic standard as AES, 3DES, GOST [7] the relation (1) is not fulfilled for all messages. Let us consider public key cryptosystems and, first of all, Rivest-Shamir-Adleman system (RSA). It is well known that such cryptosystem is determined by the following parameters: *p*, *q* different primes, *n* ¼ *pq modulo* ð Þ, *φ* ¼ ð Þ *p* � 1 ð Þ *q* � 1 , *e*—encryption key, 1≤ *e*≤*φ*, *d*—decryption key, *d* ¼ *e*�1*mod φ*, gcdð Þ¼ *e*, *φ* 1, 1≤ *M* ≤*n* � 1—integers. Encryption algorithm for RSA is *E* ¼ *Me mod n*; decryption algorithm is *M* ¼ *Edmod n*. Then for RSA condition (1) takes the form:

$$(\mathcal{M}^{\epsilon\_A} \bmod n)^{\epsilon\_B} \bmod n = (\mathcal{M}^{\epsilon\_B} \bmod n)^{\epsilon\_A} \bmod n \tag{3}$$

Since exponentiation by modulo *n* is a commutative operation, the relation (3) becomes trivially true. In fact

$$(\mathsf{M}^{\epsilon\_A} \bmod n)^{\epsilon\_B} \bmod n = \mathsf{M}^{\epsilon\_A \epsilon\_B} \bmod n = (\mathsf{M}^{\epsilon\_B} \bmod n)^{\epsilon\_A} \bmod n = \mathsf{M}^{\epsilon\_B \epsilon\_A} \bmod n.$$

On the one hand RSA system is self-sufficient one but on the other hand there may be situations where its public key *eB* is unknown for the user *A* or should be secure. Then protocol shown in **Figure 1** could have a reason.

*Example*. Let *p* ¼ 3, *q* ¼ 5, *M* ¼ 2, *eA* ¼ 3, *eB* ¼ 5. Then it is easy to verify that both parts of (3) are equal to 8.

Let us consider as next public key cryptosystem, Rabin's one, that is determined by the following parameters [7]: *p*, *q* are distinct primes (secret key), *nA B*ð Þ ¼ *pq modulo* ð Þ is public key. 0 ≤ *M* ≤*n* � 1, *E* ¼ *M*2 *mod n*. Then relation (1) takes the form:

$$\left(\mathrm{M}^2 \mathrm{mod}\, n\_{\mathrm{A}}\right)^2 \mathrm{mod}\, n\_{\mathrm{B}} = \left(\mathrm{M}^2 \mathrm{mod}\, n\_{\mathrm{B}}\right)^2 \mathrm{mod}\, n\_{\mathrm{A}}\tag{4}$$

It is easy to make sure that (4) is not satisfied, generally speaking. In fact, let us consider an example: *pA* ¼ 3, *qA* ¼ 5, *nA* ¼ 15, *pB* ¼ 11, *qB* ¼ 7, *nB* ¼ 77, *M* ¼ 5.

**Figure 1.**

*Protocol based on relation (1) where f* �1 *KA* ð Þ� *is decryption algorithm given the key KA.* Then it is easy to check that the left side of (4) is equal to 23, while the right side is equal to 10.

In order to find out the reason of that fact, let us present both sides of (4) as the following values, respectively:

$$\begin{aligned} \left(\mathsf{M}^2 \boldsymbol{mod} \ n\_A\right)^2 \boldsymbol{mod} \ n\_B &= \left(\mathsf{M}^2 - n\_A \boldsymbol{l}\right)^2 \boldsymbol{mod} \ n\_B = \\ &= \left(\mathsf{M}^4 - 2\mathsf{M}^2 n\_A \boldsymbol{l} + n\_A^2 \boldsymbol{l}^2\right) \boldsymbol{mod} \ n\_B, \end{aligned} \tag{5}$$

where *l* is some integer.

$$\begin{aligned} \left(M^2 \bmod n\_B\right)^2 \bmod n\_A &= \left(M^2 - n\_B m\right)^2 \bmod n\_A = \\ &= \left(M^4 - 2M^2 n\_B m + n\_B^2 m^2\right) \bmod n\_A,\end{aligned} \tag{6}$$

where *m* is some integer too. We can see that in general case *nA* 6¼ *nB* the right sides of (5) and (6) are different values. It was mentioned in Section 1 that some PKC is vulnerable against QC's. Such of them that are resistant against QC's attacks were called *post quantum cryptosystems*. The most known among them is McEliece PKC [7]. The matrices that determine it are: *k* � *n* matrix *G*~ *A B*ð Þ ¼ *SA B*ð Þ*GA B*ð Þ*PA B*ð Þ—public key where *SA B*ð Þ is nonsingular random *k* � *k* matrix, *PA B*ð Þ is permutation random *n* � *n* matrix, *GA B*ð Þ is random Goppa code generating matrix. Matrices *SA B*ð Þ, *GA B*ð Þ, *PA B*ð Þ are believed as secret key jointly with a binary random vector *ZA B*ð Þ of the known length *n*, and with given weight *tA B*ð Þ. Encryption procedure is performed as follows:

$$E\_{A(B)} = M\tilde{G}\_{A(B)} \oplus Z\_{A(B)},\tag{7}$$

where " ⊕ " is bitwise modulo two addition. It follows from (7) that commutative property (1) for McEliece CS was looking as:

$$\mathcal{G}\_{K\_{\mathcal{A}}}\left(f\_{K\_{\mathcal{B}}}(\mathcal{M})\right) = \left(\mathcal{M}\breve{\mathcal{G}}\_{\mathcal{B}}\oplus Z\_{\mathcal{B}}\right)\breve{\mathcal{G}}\_{\mathcal{A}}\oplus Z\_{\mathcal{A}} = \mathcal{M}\breve{\mathcal{G}}\_{\mathcal{B}}\breve{\mathcal{G}}\_{\mathcal{A}}\oplus Z\_{\mathcal{B}}\breve{\mathcal{G}}\_{\mathcal{A}}\oplus Z\_{\mathcal{A}}\tag{8}$$

$$\mathcal{G}\_{K\_{\mathcal{B}}}\left(f\_{K\_{\mathcal{A}}}(\mathcal{M})\right) = \left(\mathbf{M}\mathbf{\tilde{G}}\_{A}\oplus \mathbf{Z}\_{A}\right)\mathbf{\tilde{G}}\_{B}\oplus \mathbf{Z}\_{B} = \mathbf{M}\mathbf{\tilde{G}}\_{A}\mathbf{\tilde{G}}\_{B}\oplus \mathbf{Z}\_{A}\mathbf{\tilde{G}}\_{B}\oplus \mathbf{Z}\_{B} \tag{9}$$

It is easy to see that the values in the right sides of (8) and (9) are, generally speaking, unequal one to another owing at least different vectors *ZA* and *ZB*. Hence McEliece CS does not correspond to CE algorithm. At a single glance stream cipher is looking as CE algorithm. In fact, the encryption procedure for this cipher is:

$$E(M, K) = M \oplus \chi(K),\tag{10}$$

where *E M*ð Þ ,*K* is a cipher text given secret key *K*, *γ*ð Þ *K* is an encrypting binary sequence (gamma) given the key *K*, " ⊕" is bitwise modulo 2 addition, *M* is the binary message to be encrypted. It follows from (10) that the condition (1) takes the following form for *γ*-based stream ciphers:

$$\mathbf{M} \oplus \boldsymbol{\chi}(\mathbf{K}\_A) \oplus \boldsymbol{\chi}(\mathbf{K}\_B) = \mathbf{M} \oplus \boldsymbol{\chi}(\mathbf{K}\_B) \oplus \boldsymbol{\chi}(\mathbf{K}\_A) \tag{11}$$

that is valid trivially. The use of stream ciphers as CE in protocol shown in **Figure 1** was looking as it is presented in **Figure 2**.

**Figure 2.** *Protocol of secret message transmission based on stream ciphers.*

However we can see from **Figure 2** that eavesdropper receiving *CB* and *C*0 *A* be able to bitwise add them by modulo 2 that gives the following binary sequence: *M* ⊕ *γ*ð Þ *KB* ⊕ *γ*ð Þ *KB* ⊕ *M* ⊕ *γ*ð Þ¼ *K*A *γ*ð Þ *KA* . Next, attacker add bitwise modulo 2 the last sequence *γ*ð Þ *KA* with the sequence *CA* intercepted during the first transmission session that gives *M* ⊕ *γ*ð Þ *KA* ⊕ *γ*ð Þ¼ *KA M* that is exactly the desired open sequence *M*. (It is worth to note that similar conclusion was mentioned in [6].)


*Supporting open minds since 2005*

Normal and Malignant B-Cell http://dx.doi.org/10.5772/intechopen.78445 Edited by Mourad Aribi

#### Contributors

Chulkova Svetlana Vasilievna, Lyudmila Yuryevna Grivtsova, Nikolay Nikolayevich Tupitsyn, Ivan Sokratovich Stylidi, Zamira Magometovna Galaeva, Željka Škunca, Olga Chernysheva, Alexander Popa, Stefan Patrascu, Ana Maria Patrascu, Ionela Rotaru, Valeriu Surlin, Subramanian Kalaivani Selvi, B.H. Srinivas, Sadhanandham Shrinuvasan, Mourad Aribi

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**1. Introduction**

Smart grid is a modern grid infrastructure with high efficiency, reliability, and safety, which is based on renewable energy, automatic control, and modern communication technology [1,2]. An ancillary service is indispensable in an electric service, and plays a vital role in providing strong support for power transmission and power system operation. Ancillary service includes services related to frequency stability. The stability of grid frequency is closely related to the operation of power market and the equipment safety of the power generation side and power consumption side. Ancillary service mainly includes: Frequency adjustment, automatic generation control, spinning reserve, and peaking services.

The mismatch between the power supply side and the area power consumption can affect the frequency of the power system. Hence, the load frequency regulation is necessary in power systems, in order to maintain power balance under normal conditions [3]. Grid frequency can be used to evaluate power quality. The main way to adjust the frequency of the power system is to change the generated output power and manage the loads in demand side. Reasonable control of temperature control loads can provide adjustable buffer energy for power systems [4]. A great deal of potential electrically-thermal energy is stored in all sorts of heat buffers equipment, such as heating, air conditioning units, and fridges [5]. The advantages of aggregated thermostatically controlled loads (TCLs) to take part in power grid frequency regulation are as follows: Firstly, the TCLs which can store massive energy are of wide distributions; secondly, the control method of them is simple, fast, and real-time; thirdly, the aggregated TCLs can generate a continuous reaction without considering the discrete characteristics of the individual load

control [6]. The management of TCLs is one of frequency adjustment strategies with extremely high feasibility to ensure frequency stability and improve power quality.

Recently, the bilinear partial differential equation (PDE) model was developed to provide effective control of aggregated TCLs [7]. Two control methods, based on the combination of estimator and controller, were utilized to control TCLs and effectively track demand curve [8]. A centralized and distributed algorithm based on the state space model of heating ventilation and air-conditioning (HVAC) was proposed to reduce power fluctuations and improve satisfaction of demand side [9]. A model predictive control was described and applied to demand-side response services in [10]. A distributed model predictive control (DMPC) method based on an optimized aggregation model was applied to provide frequency regulation services [11]. A Fokker–Planck diffusion model and a direct load control algorithm were developed in [12]. A model of aggregate homogeneous TCLs with uniform variation of temperature set-point was developed, and a linear quadratic regulator (LQR) has been designed in [13]. In [14], the authors proposed a novel causal method based on a parametric second-order model to forecast the energy conservation. In [15], a linear optimization model was built to provide frequency regulation services for power systems while also providing short-term demand response management. In [16], the authors proposed several switched control strategies for aggregate HVACs to provide demand-side frequency regulation. Although various aggregation characteristics of TCLs have been extensively studied, modeling precision still needs to be farther improved. As a matter of fact, it is hard to establish accurate mathematical models or physical models for aggregated TCLs because of various assumptions and computational complexity in the modeling process.

In recent years, artificial intelligence has developed rapidly with the characteristics of bionics and intelligence. This paper proposes a fuzzy neural network control method which adjusts TCLs based on the input and output data of TCLs instead of the aggregated TCL model. This method can reduce tracking errors and computational complexity, because it draws the advantages–logic reasoning capability of fuzzy control and self-learning capability of the neural network [17]. This study has the following contributions:

The rest of this paper is organized as follows. Section 1 introduces the thermal dynamics of individual TCLs and the frequency regulation problem. Section 2 describes the system structure, algorithm, and optimization of the fuzzy neural network control in detail. The simulation results are shown in Section 3. Finally, the conclusions are summarized in Section 4.


**4.1. Concept and structure of TASQ**

Based on the idea that G-quadruplexes, mimicking the natural *horseradish peroxidase*, lead to an increase of the range of applications, mainly due to the higher stability of the DNA compared to the protein and permitting a use in a bigger range of experimental conditions (temperature, buffer, ion strength, etc.),[7, 23, 176] few research teams decided to develop Gquadruplex-mimicking systems.

Because the pivotal step of the catalytic cycle is based on the activation of hemin by interaction with one of the external G-quartet,[11] the group of Dr. D. Monchaud decided to synthesize the very first example of a water-soluble molecule, composed of four guanine residues, and able to form, intramolecularly, a synthetic G-quartet.[14]

**i.** Interestingly, the DNAzyme methodology was also used to detect bigger living systems, like the bacteria *Escherichia coli* O157:H7[162] or *Alicyclobacillus acidoterrestris*.[163] In the first example, authors used graphene oxide/thionine/gold nanoparticles coated SiO2 nanocomposites to immobilize DNA, while for the second one, a more classic approach was used, in which G-quadruplex–hemin complexes oxidize the colorless guaiacol,

**j.** Developed to target G-quadruplexes *in vivo*, *in cellulo*, or *in vitro* for biological applications, G-quadruplex ligands are molecules able to interact with G-quadruplexes and to stabilize them.[164–166] This ability can be evaluated, thanks to the DNAzyme process, because of a competition between the ligands researchers want to try, and the hemin. In other words, a good ligand takes the place of the hemin; hemin is subsequently not activated and, consequently, leads to a decrease of the signal intensity (measured by UV–Vis or by

**k.** To finish this laundry list, it is essential to mention other ingenious applications for the G-quadruplex DNAzymes, like the development of logic gates[170–173] as the INHIBIT one published by T. Li *et al.*,[174] or as the AND one proposed by J. Chen *et al*., described

To summarize, all these cases, which represent the range from the more applied to the more conceptual scientific applications of the same DNAzyme catalysis, illustrate how using DNA instead of enzyme to catalyze a reaction puts out a new avenue in terms of polyvalence. It is believed that this list will increase more and more in the next years. But the precise under‐ standing of the mechanism constitutes also an exciting challenge. On the one hand, this progress should offer scientists the possibility to fine-tune the experimental conditions (*e.g*., sequence of the G-quadruplex DNA strand(s), length of the loop(s), addition of a boosting agent, etc.). On the other hand, a better comprehension should help to enlighten chemists about the mechanistic aspect of the oxidation states of the hemin, in both biological and DNAzyme

**4. Synthetic G-quartet-based DNAzymes: template-assembled synthetic G-**

Based on the idea that G-quadruplexes, mimicking the natural *horseradish peroxidase*, lead to an increase of the range of applications, mainly due to the higher stability of the DNA compared to the protein and permitting a use in a bigger range of experimental conditions (temperature, buffer, ion strength, etc.),[7, 23, 176] few research teams decided to develop G-

Because the pivotal step of the catalytic cycle is based on the activation of hemin by interaction with one of the external G-quartet,[11] the group of Dr. D. Monchaud decided to synthesize

produced by the bacteria, to tetraguaiacol, which is amber.

456 Advanced Catalytic Materials - Photocatalysis and Other Current Trends

fluorescence).[61, 167–169]

systems.

**quartets (or TASQ)**

**4.1. Concept and structure of TASQ**

quadruplex-mimicking systems.

as a keypad lock security system.[175]

Historically, the very first observation of synthetic G-quartets was made by I. Bang in 1910, who was able to form gels from a concentrate solution of guanosine monophosphate. However, the hypothesis of the self-assembly of guanine derivatives to G-quartet arrangements was only published more than half a century later by M. Gellert, M. Lipsett, and D. Davies in 1962. The dried fibers obtained could be analyzed by X-ray diffraction and helped the authors to propose the initial supramolecular structure shown in Figure 1.[177] Interestingly, a recent study proved that the length of the fibers can reach from 8 nm to 30 nm, corresponding to from 24 to 87 stacked G-quartets, respectively.[178]

During the last decades, plenty of examples using synthetic G-quartets as a supramolecular motif were developed and well summarized in several reviews.[10, 179, 180] The applications of these systems concern pH-sensitive hydrogel probes,[181] synthetic transmembrane Na+ transporter,[182] and other ionic channels,[183–185] enantioselective systems controlled by the cation used,[186] combinatorial chemistry,[187] and also molecular electronics and liquid crystals.[179] Among all these examples, in which the elementary brick is made of one or two guanines, only few stem from a four-guanine-based compound. This assessment is inquisitive and nonintuitive because G-quartets are composed of four guanines. It was why the team of J. Davis developed in 2000 and 2003 1,3-alternate calix[4]arene derivatives functionalized by four guanines.[188, 189] This smart system in which two guanines are on one side, while the two others are on the other side of the calixarene template, was used as both cation (inside the G-quartet) and anion (thanks to the H-bonds between protons of the amide groups and the anion) receptors.[188] The formation of the G-quartet is intermolecular between two guanines from one molecule, and two guanines from another one. It took the scientific community until 2008 to propose the first intramolecular synthetic G-quartet molecules, termed TASQ.

The name "TASQ," for *template-assembled synthetic G-quartet*, was introduced by the research group of J. C. Sherman to describe molecules built around a template and functionalized by four guanines, able to interact each other to self-assemble into an intramolecular G-quartet.[12] This concept probably derived from a modification of the TASP (for *template-assembled synthetic peptide*) development,[190] in which four peptide sequences were used instead of DNA bases. The aim of these models was the understanding of protein interactions, thanks to their spatial proximity when grafted to the same scaffold.

Thus, the first TASQ were synthesized from a highly lipophilic calixarene moiety substituted by four 2′,3′-*O*-isopropylideneguanosine.[12] The intramolecular formation of the G-quartet was demonstrated by 1 H NMR, NOESY, COSY, and HMQC and was definitively proved by X-ray diffraction in 2012.[191] Notwithstanding, no application of these systems was publish‐ ed, and their hydrophobic properties were probably the reason for that. Interestingly, they rectified this point using first phosphate groups to functionalize the calixarene on the opposite side of the guanosines[192] and then, subsequently, with the phosphate group intercalating between the guanine moieties and the scaffold (due to the use of 3′-monophosphate guano‐ sines).[193]

**Figure 3.** Chemical structures of the first TASQ developed by J. C. Sherman (**A**), [12] RAFT-G4 (**B**), [13] DOTASQ-C5 in equilibrium between its open (left) and closed conformations (right) (**C**), [14] PNADOTASQ (**D**), [15] and PyroTASQ (E). [16]

In parallel, the group of E. Defrancq was focused on the functionalization of a cyclodecapeptide termed RAFT (for *regioselectivity addressable functionalized template*) by DNA strands.[127, 194– 196] This research led the team to synthesize a TASQ from a RAFT equipped with four guanosines.[13] Thanks to a collaboration with D. Monchaud's team, this molecule was studied as a DNAzyme (see section 4.2) and showed that the G-quartet intramolecular formation (confirmed by circular dichroism, and NMR studies) was able to catalyze the hemin oxidation/ reduction cyclic reaction.[197]

However, the very first example of a water-soluble TASQ was proposed by L. Stefan *et al*. in 2011.[14] The key point was the choice of the *ad hoc* template, able to drive the G-quadruplex formation, and being fully soluble in water. It was why the cyclen macrocycle was chosen, for its high solubility, its ability of metal chelation, and its C4-type symmetry, identical to the one of a G-quartet.[198, 199] Even if this last criteria was not a *sine qua non* condition, it seemed to be favorable, like in the calixarene-templated TASQ developed by J. C. Sherman's group. Thus, DOTASQ were born from a DOTA (for 1,4,7,11-tetraazacyclododecane-*N,N*′*,N″,N'''*-tetraace‐ tic acid) moiety in which the four "arms" were functionalized by four alkylguanine groups. The term DOTASQ is a portmanteau word created from both acronyms DOTA (*i.e*., the template) and TASQ (*i.e*., the supramolecular property of the molecule). For the sake of comparison, four DOTASQ were synthesized: DOTASQ-C1 and DOTASQ-C5 (C1 and C5 suffixes indicating the length of the alkyl chain), and the terbium(III) equivalents Tb.DOTASQ-C1 and Tb.DOTASQ-C5, respectively.[14]

Furthermore, another TASQ was developed with peptide nucleic acid guanine (also termed PNA guanines) arms and was called PNADOTASQ.[15]

The peroxidase-mimicking catalytic activities of these compounds were evaluated and are described in the next sections. As a remark, other TASQ were developed by D. Monchaud's team like PyroTASQ (pyrene as a template),[16] NaphtoTASQ (naphthalene as a template) [200], and also PorphySQ and PNAPorphySQ (porphyrin moieties as templates).[201, 202]


**1. Introduction**

Prices of commodity futures contracts with different maturities are linked through the forward curve. Understanding of the shape and the characteristics of the term structure is of utmost importance for storage decisions, hedging and roll-over strategies as well as calendar spread trading. Several strands of literature address this topic. The theoretical underpinning of the forward curve goes back to Working's (1949) theory of storage that establishes an equilibrium relation between nearby and distant futures contracts and explains storage under backwardation by the concept of convenience yield (Brennan 1958). In contrast, Keynes' theory of normal backwardation decomposes a futures price into an expected future spot price and an expected risk premium that risk-averse hedgers grant to speculators (Fama and French 1987). The statistical modelling of the forward curve has benefitted from Nelson and Siegel's (1987) proposal to describe the term structure parsimoniously in terms of level, slope and curvature. A dynamic version of this model has been introduced by Diebold and Li (2006). Applications to commodity futures can be found in Karstanje et al. (2017). An alternative method uses a set of state variables (factors), particularly spot price, convenience yield, and interest rate, to derive the forward curve under no-arbitrage conditions (Gibson and Schwartz 1990; Schwartz 1997). Applications of this approach to agricultural futures include Geman and Nguyen (2005) and Sorensen (2002), among others.

With the rise of the modern market microstructure, interest has shifted from the estimation of equilibrium relations towards the understanding of price discovery, i.e., the question of how new information is absorbed in asset prices and how this information is transferred along the forward curve. Since there is no explicit market microstructure theory designed for commodity futures with di fferent maturities, most studies in this area are non-structural and try to identify empirical patterns in

data. Mallory et al. (2015) use contemporaneous and time-lagged correlations of nearby and deferred futures contracts for corn to investigate the speed at which liquidity providers revise their beliefs in response to the occurrence of an information event. They find that the correlation of price revisions disappears even for short time lags and conclude that new information to the market is immediately transmitted across all contract maturities. Hu et al. (2017) pursue a similar objective, but instead of simple correlations, they apply co-integration techniques to explore price discovery among nearby and deferred futures contracts of corn and live cattle. They report a larger share of price discovery in nearer to maturity contracts. The dominance of nearer contracts, however, is less pronounced for live cattle than for corn, which is explained by di fferences in the storability of these commodities. Recently, Volkenand et al. (2019) investigate the duration dependence among agricultural futures with di fferent maturities, exploiting the fact that the time between market events (transactions or price changes) carries information (Easley and O'Hara 1992). They apply an autoregressive conditional duration (ACD) model to price durations for corn, wheat, live cattle, and lean hog. The authors report linkages between nearby and deferred futures contracts. They conclude that information is quickly processed along the forward curve.

The aforementioned studies rest on a traditional view of the price discovery mechanism according to which price revisions are driven by the arrival of exogenous information. This view has been challenged by the concept of market reflexivity (Soros 1987) which assumes that trading activity is also endogenously driven by positive feedback mechanisms. Sources of potential endogeneity encompass informational cascades leading to herding, as well as speculation based on technical analysis (e.g., momentum trading) and algorithmic trading (Filimonov et al. 2014). Furthermore, hedging strategies combined with portfolio execution rules can lead to self-excitement of price moves (Kyle and Obizhaev 2019). While this co-existence of exogenous and endogenous price dynamics contradicts the e fficient market hypothesis (Fama 1970), it can be helpful to understand puzzling phenomena on financial markets, such as "flash crashes" or excess volatility (Hardiman et al. 2013). The concept of market reflexivity has originally been introduced in a narrative, non-technical manner, but since then it experienced an underpinning by statistical methods that allow one to disentangle exogenous and endogenous sources of market activities and thus measure the degree of market reflexivity. More specifically, self-exiting Hawkes processes have been proposed as a device to quantify reflexivity (e.g., Filimonov and Sornette 2012). Bacry et al. (2016), for example, find that less than 5% of the price changes in the DAX (German stock index) and BUND (German Bond) futures markets are driven by external sources. In the context of commodity futures markets, Filimonov et al. (2014) find that reflexivity has increased since the mid-2000s to 70%. They trace this back to the increase in automated trading in the course of the transition to an electronic trading environment. In fact, automated trading generated about 40% of the total futures volume traded in the grain and oilseed markets between 2012 and 2014 (Haynes and Roberts 2015).

Despite the increasing interest in market reflexivity as an alternative to the prevalent tenet of market e fficiency and rational expectations, there exists no empirical study applying this concept to the forward curve of commodities. Against this backdrop, our objective is to examine price discovery in nearby and deferred agricultural futures contracts while explicitly taking into account potential market reflexivity. We apply a four-dimensional Hawkes model to storable and non-storable agricultural commodities. The Hawkes model allows us to divide the intensity of the trading activity in a futures contract with a certain maturity into three parts: a reaction on external sources like new information, market reflexivity, and reactions on trading activity in contracts of di fferent maturities. Using this approach, we review previous findings regarding price discovery in nearby and deferred futures contracts. In particular, we examine whether nearby contracts dominate deferred contracts in price discovery while accounting for potential market reflexivity (e.g., Gray and Rutledge 1971). We also explore whether price discovery and potential market reflexivity di ffer between storable and non-storable commodities. In line with Hu et al. (2017), we expect that dominance of nearby contracts in price discovery is more pronounced for storable commodities. Moreover, we conjecture

that commodities with a high share of automated trading, such as grains and oilseeds, show a higher level of endogeneity. Since market microstructure theory emphasizes the importance of the direction of the transactions in the price discovery process (cf. Glosten and Milgrom 1985), we differentiate between buyer- and seller-initiated transactions in our analyses.

The remainder of the paper is organized as follows: Section 2 explains the statistical methods used in the analyses. Section 3 presents the dataset and the results of the empirical application and Section 4 concludes.


**2. Applications of nanomaterials in HPLC stationary phases**

In conventional HPLC, the stationary phase (SP) plays a pivotal role in the separation technique [5]. The packing particles constituting the SP are of several micrometres in diameter with nanometre-sized pores [6]. Therefore, the industry has pushed researchers to investigate new packing materials as an attempt to achieve high throughput with robust analysis [6]. Packings in HPLC can be divided into three types-polymeric, inorganic, and hybrid materials. At present, inorganic materials, which include silica, hydroxyapatite, graphite, and metal oxides, etc. are widely used in research and applications [7]. Among these materials, silica is almost ideal support given its favourable characteristics, for example, good mechanical strength, high chemical and thermal stability, controllable pore structure and surface area, etc. [7]. Therefore, silica has been developed as the most widely used HPLC packing material [6, 7]. Throughout the years, many silica stationary phases (both porous and non-porous) have been commercialised and widely applied for analysis of pharmaceutical and biological samples [8].

#### **2.1. Porous and non-porous nanomaterials**

Non-porous and porous particles are the two major types of spherical packing materials used in HPLC [8]. The significant difference between both particles is that porous particles

**Figure 1.** The difference in surface area between porous and non-porous microspheres under scanning electron microspray [2].

have resistance to mass transfer contribution from the stagnant mobile phase in the pores [8]. Decreasing the particle size and increasing the diffusion coefficient can improve the mass transfer of solutes in the mobile phase [3, 5]. Non-porous particles can provide lower mass transfer resistance and higher efficiency than porous particles [8]. However, porous particles have higher surface areas (**Figure 1**) and can provide much higher sample loading capacity [8].

On the other hand, in porous particles, solutes transfer from the mobile phase exterior to the particles into the mobile phase within the pores to interact with the chiral stationary phase (CSP) [9]. Following this interaction, the solute molecule must diffuse out of the particle and continue its journey down the column ahead of the solute [5, 9]. This slow rate of mass transfer into and out of the porous particle is a source of HPLC band broadening [9]. **Figure 2** illustrates a reduction in particle size shortens the path length of this diffusion process, improves mass transfer, and provides better efficiency [9].

**Figure 2.** Smaller particle sizes increase efficiency and result in a wider range of flow rates [9].


**7. Ethical and social aspects**

The introduction of NIPT by the technique of NGS used in prenatal diagnosis has raised some ethical and social concerns. NIPT can theoretically provide information on the entire genome of the mother and the fetus with relative ease. In fact, NIPT has already revealed a small number of occult malignancies [66]. The sequenced DNA, however, could also reveal a BRCA mutation or mutations on genes encoding for neurodegenerative diseases such as Chorea Huntington that would have major consequences for the mother and the unborn child [67]. It becomes obvious that the professional societies and national guidelines need to carefully regulate which data will be analyzed, stored, and reported. Clearly, the mother needs to give written informed consent to each specific analysis that is performed and needs to approve any individuals or institutions that receive this type of information. Although most of today's available NIPT tests directly report to the physician who indicated the test there remains a concern that NIPT could be offered directly to the pregnant woman without a medical request or indication. It seems of highest importance that the expectant mother is appropriately counseled by a trained health care professional who can offer and discuss all implications for testing, provide for and interpret all options, discuss prognosis and can assist with the management of the pregnancy and the subsequent prenatal care [68,69]. An important further aspect is that adequate educational material is offered to health care professionals and to the public, as it will assist in avoiding misunderstandings about the technology and possible misuse, thereby ease public anxieties [70].