*Edited by Kenneth Imo-Imo Eshiet*

Te stimulation of unconventional hydrocarbon reservoirs is proven to improve their productivity to an extent that has rendered them economically viable. Generally, the stimulation design is a complex process dependent on intertwining factors such as the history of the formation, rock and reservoir fuid type, lithology and structural layout of the formation, cost, time, etc. A holistic grasp of these can be daunting, especially for people without sufcient experience and/or expertise in the exploitation of unconventional hydrocarbon reserves. Tis book presents the key facets integral to producing unconventional resources, and how the diferent components, if pieced together, can be used to create an integrated stimulation design. Areas covered are as follows:

 • stimulation methods, • fracturing fuids, • mixing and behavior of reservoir fuids, • assessment of reservoir performance, • integration of surface drilling data, • estimation of geomechanical properties and hydrocarbon saturation, and • health and safety.

Exploitation of Unconventional Oil and Gas Resources: Hydraulic Fracturing and Other Recovery and Assessment Techniques is an excellent introduction to the subject area of unconventional oil and gas reservoirs, but it also complements existing information in the same discipline. It is an essential text for higher education students and professionals in academia, research, and the industry.

Published in London, UK © 2019 IntechOpen © claffra / iStock


**2. Materials and method**

**1. Introduction**

possible to further increase existing cultivating areas.

8 Physical Methods for Stimulation of Plant and Mushroom Development

cover into agricultural land requires great investment.

in the most populous parts of the world will double by 2025 [1].

Plants comprise the source of life on earth. In total, 90% of the energy and 80% of the protein consumed by humans are of plant origin. The remaining energy and protein requirements are met by animal products. Thousands of people die every year in many parts of the world due to hunger and malnutrition. It is necessary to increase crop production so that human beings can feed on a sufficient and balanced diet to sustain their existence on Earth. This can only be achieved by increasing the amount of yield obtained from each unit area of land, since it is not

It is estimated that world population will increase by 1.5% per year to 8 billion in 2020 and 11 billion in 2050 [1]. The area of land covering the Earth is 14 billion hectares. Currently, 10% of this land area is cultivated. About 20% of the world's land is covered with pastures, 20% with mountains, 20% with glaciers, and 20% with deserts. The remaining 10% of the area has a very shallow soil cover. Given the impossibility of agricultural activities in mountains and glacier-covered areas, there are areas of potential agriculture, such as marshlands, deserts, or areas with insufficient land cover. It is largely impossible to use pastures that cover rugged and very sloping areas as cultivating fields. The conversion of deserts and inadequate land

In parallel with increasing population, agricultural areas are being used for other nonagricultural purposes (settlement, road, factory, etc.) or are shrinking rapidly due to erosion, salinization, acidification, intensive agriculture, and overgrazing. It is estimated that agricultural land per capita, which is now 0.26 hectares, will decrease to 0.15 hectares by 2050. In addition, the availability of water resources for modern agriculture will become difficult due to increased water consumption and increasing water pollution [1]. It is expected that food requirements

The yield in agricultural production declines due to biotic and abiotic stress factors. Developing a resistant or tolerant cultivar against these stress factors is the main goal of plant breeding. Chemical methods are commonly used to combat biotic stressors (diseases and pests) that reduce crop production. However, the use of chemicals in agriculture causes an extra financial burden and pollutes the environment. In Turkey, 2.3 million tons of chemical fertilizer and 25,000 tons of pesticides (insecticides, fungicides, and herbicides) were used according to the data from 2013. In the last 25 years, it has emerged that the unconscious use of fertilizers and chemicals applied in plant production has negatively affected long-term ecological balance. For example, it has been determined that overused nitrogen fertilizers are washed from the soil and pollute drinking water and the seas, while the nitrogen components that are escaping from the gaseous state are adversely affecting the ozone layer, which protects the earth from harmful rays of the sun. In addition, herbicides and insecticides applied to combat weeds and pests have been shown to destroy the natural equilibrium in agricultural areas, causing the emergences of new diseases and pesticides. It has also been understood that certain chemicals, which have permanent effects, accumulate in plants, and this negatively affects the health of people and animals fed on those plants. As a result, it is

The study conducted by Taher et al. [5] was carried out in the research fields of the Faculty of Agriculture, Ankara University in the years of 2013 and 2014. Oil-type sunflower cultivars

• Third treatment: A total of four leaves were removed from the plant. Two of these leaves were taken from the below of the head, and the other two were taken from the middle of the plant. • Fourth treatment: A total of six leaves were removed from the plant. Three of these leaves were selected from the below of the head, and the remaining three from the middle of the plant. Plants were irrigated during development according to water need of the plants. During the application of irrigation, the most attention was given to watering each parcel equally. After the flowering and fertilization has been completed, the heads of the plants from which the measurements were taken were covered with paper bags to protect the seeds from damage by

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birds (**Figure 3**). Plants were harvested when 80% of sunflower heads were brown.

**Figure 2.** The places from where leaves were removed in "star-shaped head stage" (from below the head and from the

middle of the plant).

**Figure 1.** Sowing plan for sunflower cultivars according to the "randomized complete block, split-plots" design with three replications.

"08-TR-003," "TR-3080," and "TARSAN-1018" obtained from "Trakya Agricultural Research Institute" were used in the study. Soil of trial field was plowed 30 cm in depth in fall before winter. In spring, it was plowed again for 10–15 cm in depth to make soil ready for sowing. Sowing was performed in the first week of April with spaces of 70 cm inter-row and 25 cm on-row. Three seeds were put in each dibbling to guarantee the emergence. Two weeks after emergence, two of the plants were eliminated and only one plant left in each dibbling. For all defoliation treatments, plots were fertilized with 14 kg/da diammonium phosphate (DAP) before sowing. During growing, weed control was achieved by hand in experimental field. The study sowing plan is given in **Figure 1**.

When plants reached to "star-shaped head stage," which is the beginning of reproductive period, defoliation was carried out and the plants were labeled. Half of the leaves were removed from just below the head, while the other half was removed from the middle part of the plant for each defoliation treatment (**Figure 2**). Four different defoliation treatments were performed. They were:

Plants were irrigated during development according to water need of the plants. During the application of irrigation, the most attention was given to watering each parcel equally. After the flowering and fertilization has been completed, the heads of the plants from which the measurements were taken were covered with paper bags to protect the seeds from damage by birds (**Figure 3**). Plants were harvested when 80% of sunflower heads were brown.

"08-TR-003," "TR-3080," and "TARSAN-1018" obtained from "Trakya Agricultural Research Institute" were used in the study. Soil of trial field was plowed 30 cm in depth in fall before winter. In spring, it was plowed again for 10–15 cm in depth to make soil ready for sowing. Sowing was performed in the first week of April with spaces of 70 cm inter-row and 25 cm on-row. Three seeds were put in each dibbling to guarantee the emergence. Two weeks after emergence, two of the plants were eliminated and only one plant left in each dibbling. For all defoliation treatments, plots were fertilized with 14 kg/da diammonium phosphate (DAP) before sowing. During growing, weed control was achieved by hand in experimental field.

**Figure 1.** Sowing plan for sunflower cultivars according to the "randomized complete block, split-plots" design with

When plants reached to "star-shaped head stage," which is the beginning of reproductive period, defoliation was carried out and the plants were labeled. Half of the leaves were removed from just below the head, while the other half was removed from the middle part of the plant for each defoliation treatment (**Figure 2**). Four different defoliation treatments were

• Second treatment: A total of two leaves were removed from the plant. One of these leaves was selected from the below of the head, and the other from the middle of the plant.

• First treatment (Control): Defoliation was not carried out in this case.

The study sowing plan is given in **Figure 1**.

10 Physical Methods for Stimulation of Plant and Mushroom Development

performed. They were:

three replications.

**Figure 2.** The places from where leaves were removed in "star-shaped head stage" (from below the head and from the middle of the plant).

two, four, and six leaves removed) was carried out in the middle of the plant and from the below of the head in "star-shaped head stage," which is the beginning of the reproductive period in the plant, and the results of photosynthesis are assimilated and transported to the seeds. When the amount of assimilation produced by photosynthesis is increased, the seed

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In this study, on the effects of different defoliation treatments on seed, crude protein, and crude oil yields per decare in "star-shaped head stage," which is the beginning of the reproductive period in sunflower, it was determined that different defoliation treatments, according to cultivars, significantly increased seed, crude protein, and crude oil yields compared to

For cv. "08-TR-003," the seed yield per decare was 385.4 kg in the control treatment in which no leaf was removed, while it was 431.2 kg in the four-leaf defoliation treatment in the plant. This means that the yield increased by 11.87%. An increase of 1.90% was observed in the crude protein yield obtained from a decare. When the oil yield per decare value was examined, it was 175.0 kg for the control treatment, while it was 207.7 kg — an increase of 18.67% — in the four-leaf defoliation group. The highest values for seed, crude protein, and crude oil yields in the cv. "08-TR-003" were obtained when four leaves per plant were removed (**Table 1**).

In cv. "TR-3080," the seed yield per decare in control treatment was measured as 398.3 kg, whereas there was an 8.64% increase to 432.7 kg when two leaves were removed at the beginning of the reproductive period. The protein yield was 66.2 kg in the control, whereas it increased by 6.40% up to 70.4 kg when two leaves were removed. Examining the crude oil yield per decare values, it was 184.8 kg for the control treatment, whereas it increased by 13.36% to 209.5 kg when two leaves were removed from the plant. In cv. "TR-3080," the highest values for seed, crude protein, and crude oil yields were obtained from the two-leaf

In cv. "TARSAN-1018," 407.3 kg/da seed yield determined for the control treatment was 451.6 kg/da when six leaves were removed from the plant. This indicates that in the six-leaf defoliation treatment, seed yield increased by 10.87% compared to the control. Crude protein yield per decare was found to be 75.6 kg for control treatment, while it was 85.1 kg for the six-leaf defoliation, an increase of 12.61%. The crude oil yield per decare value in the control group was 190.7 kg, while it was 215.3 kg for the six-leaf defoliation treatment, with an increase of 12.92%. In cv. "TARSAN-1018," the highest values for seed, crude protein, and

Leaves forming the surface for active photosynthesis in plants can be damaged due to environmental factors (such as storms and hail) and mechanical factors (tools and machines used in maintenance operations such as drilling and spraying). The extent of this damage is directly proportional to the amount of defoliation. In other words, as the number of defoliations increases in the plant, the agricultural characteristics decrease proportionally, based on the cultivar. This is confirmed by the lowest values for the seed yield per decare values obtained in our study for the six-leaf defoliation treatments for cvs. "08-TR-003" and

crude oil yields were obtained from the six-leaf defoliation treatment (**Table 1**).

yield will also directly increase.

defoliation treatment (**Table 1**).

"TR-3080."

the control group with no defoliation treatment (**Table 1**).

**Figure 3.** Flowering in the head (on the left) and covering the head with paper bags to protect seeds from bird's damage (on the right).

Measurements were performed in totally 30 plants (10 plants per replication) in each defoliation treatments in all cultivars. Seed yield per plant (g/plant), seed yield per decare (kg/decare), protein and oil percentages, crude protein, and crude oil yields (kg/decare) were recorded.

Experiments were arranged at "randomized complete block, split-plot" design with three replications. In the experiment, oil-type sunflower cultivars were main plots and four defoliation applications were subplots. Data were statistically analyzed by Duncan's multiple range test using "IBM SPSS Statistics 22." Data given in percentages were subjected to arcsine (√*X*) transformation before statistical analysis [6].


**1. Introduction**

Hard processes, e.g., exclusive meson electroproduction with *Q*2 1 GeV2, can be only described by taking into account quark-gluon degrees of freedom. The characteristic transverse size of the incoming and outgoing color-neutral quark configurations in a hard process is *rt* ∼ 1/*Q* and, thus, they can be regarded as point-like configurations (PLCs). It can be shown within pQCD [1] that the interaction cross section of the small-*rt* color singlet *qq*¯ pair and a proton behaves geometrically at *rt* → 0, i.e., *σqq*¯ ∝ *r*2*t* . Therefore, the interaction of PLCs with surrounding nucleons in the nuclear target is strongly reduced which is known as the CT phenomenon, see [2] for the most recent review of CT.

A PLC is not an eigenstate of the QCD Hamiltonian and, therefore, it is unstable and expands to the normal hadronic size on the proper time scale <∼ 1 fm/c. However, the expansion time of the PLC can be large due to the Lorentz time dilation. It is thus possible to observe CT if the incoming and/or outgoing PLCs are fast enough in the nuclear target rest frame.

At ultrarelativistic energies, where the PLCs are practically 'frozen', CT has been observed at Fermilab [3] in coherent diffractive dissociation of a 500 GeV/c pion in a pair of high-*kt* jets on nuclear targets following theoretical predictions [4]. The smallness of initial- and final-state interactions (ISI,FSI) has been concluded from the mass number dependence of the cross section *Aα*, *α* = 1.6 at *kt* >∼ 1 GeV (which is far away from expected *α* = 2/3 for soft coherent diffraction but agrees with calculations of refs. [4,5]).

*Particles* **2020**, *3*

At intermediate energies (*E*beam ∼ 10 GeV), CT becomes less pronounced. (The beam energy at which CT will be observable depends, of course, on the concrete process. The minimum requirement for CT is that at least one hadron participating in the hard process should be fast. This can be either an incoming or an outgoing hadron. The momentum transfer from the beam particle to the outgoing hadrons is shared between them so that the largest possible value is given by the beam momentum.) This can be understood from the decomposition of the wave function of a PLC in a hadronic basis of states with fixed momentum *ph* (that is the momentum of the genuine hadron '*h*' to which the PLC is asymptotically converted):

$$\left|\Psi\_{PLC}(t)\right> \coloneqq \sum\_{i=1}^{+\infty} a\_i e^{-iEt} \left|\Psi\_i\right> \:> = e^{-iE\_1t} \sum\_{i=1}^{+\infty} a\_i e^{i(E\_1 - E\_i)t} \left|\Psi\_i\right> \:> , \quad E\_i = \sqrt{p\_h^2 + m\_i^2}.\tag{1}$$

Due to different phase velocities, *Ei*/*ph*, of the plane waves the initially compact in space configuration expands on the length scale of the order of

$$l\_h = \frac{1}{E\_2 - E\_1} \simeq \frac{2p\_h}{\Delta M^{2'}}\tag{2}$$

where Δ*M*2 = *m*22 − *m*21 assuming the relativistic limit, *ph m*1, *m*2. Equation (2) can be thus regarded as an estimate of the hadron 1 (≡ *h*) formation (or coherence) length. The hadronic state 2 is the first radially excited state of the hadron 1. Hence, we can estimate Δ*M*2 *m*2*N*∗(1440) − *m*2*N* 1.2 GeV2 for the nucleon. However, for the pion that has a Goldstone nature the same argument does not apply. Thus, assuming that the quark and antiquark each carry 1/2 of the light cone (LC) momentum of a *qq*¯ system we estimate Δ*M*2 4(*m*2*q* + *k*2*t*) − *m*2*π* 0.93 GeV2 for the pion, where *mq* = 0.340 GeV is the constituent quark mass and *k*2*t*1/2 0.35 GeV/c is the average transverse momentum of a quark in a hadron [6].

These estimates are in a reasonable agreement with the empirical range obtained from the analysis of pionic nuclear transparency at JLab [7], Δ*M*2 0.7 − 1.1 GeV2 corresponding to

$$l\_h = 0.4 - 0.6 \text{ fm} \frac{p\_h}{\text{GeV}/\text{c}'} \tag{3}$$

At *ph* ∼ 10 GeV/c, the empirical pion formation length (3) becomes comparable with the radii of heavy nuclei indicating the onset of CT.

At intermediate energies, clear CT signals have been experimentally observed from the *Q*2-dependence of nuclear transparency in the electroproduction of a pion *A*(*e*,*eπ*+) for *Q*2 = 1 − 5 GeV2 [8] and of a *ρ*-meson *A*(*e*,*eρ*0) for *Q*2 = 0.8 − 2.4 GeV2 [9] at JLab. However, CT has not been observed for the quasielastic proton electroproduction *A*(*e*,*e p*) studied at SLAC and JLab. (Squeezing proton probably needs larger *Q*2 values than for pion.)

CT has been predicted for the hadron-induced semi-exclusive processes with large momentum transfer *h* + *A* → *h* + *p* + (*A* − 1)∗ [10,11]. So far, only C(p,2p) process at Θ*c*.*m*. = 90◦ has been studied experimentally at BNL [12]. The nuclear transparency for this process increases with beam momentum until *p*lab ∼ 9 GeV/c in agreement with CT, but then it starts to decrease. In [13], such a complex behavior has been explained by the intermediate (very broad, Γ ∼ 1 GeV ) 6*qcc*¯ resonance formation with mass ∼ 5 GeV. Alternatively in [14], the same behavior has been explained by stronger absorption of the large-size quark configurations produced by the Landshoff mechanism (three-gluon exchange).

In the inclusive processes at high energies, e.g., in DIS, the formation of PLCs is less clear, since even at high *Q*2 the momentum transfer is shared between many particles. Nevertheless, most theoretical studies of DIS off nuclei include CT effects for the interaction of fast pre-hadrons with nuclear medium using dynamical hadron formation models [15–17]. Hadron formation effects are included in microscopic transport models for high-energy heavy ion collisions, such as UrQMD [18], HSD [19], and the GiBUU model [20].

The purpose of this paper is to elucidate the effect of PLC expansion on CT. We start from the most clean exclusive processes which can be described on the basis of the Glauber model supplemented by the quantum diffusion effect [6]. Then we continue discussing the effects of hadron formation on slow neutron production in photon–nucleus interactions. Finally, we address proton, pion and hyperon rapidity and *pt* spectra in pA- and central AA collisions.

The structure of the paper is as follows. In Section 2 we present the results of the Glauber model and the quantum diffusion model (QDM) calculations of the nuclear transparency in pion electroproduction *A*(*e*,*eπ*+) at large *Q*2, in pionic Drell-Yan process *A*(*π*−, *l*+*l*−) at large invariant mass of the dilepton pair, and in large-angle pion photoproduction *A*(*γ*, *π*− *p*). In Section 3 the GiBUU model supplemented by the statistical multifragmentation model (SMM) for the decay of excited nuclear residue is applied to describe slow (*E* < 10 MeV) neutron production in high-energy virtual-photon–nucleus interactions. We study the sensitivity of the slow neutron production to various treatments of hadron formation. Section 4 contains the discussion of the results of the GiBUU calculations of the pA- and central AA collisions. Finally, in Section 5 we summarize our results and draw conclusions.

## **2. Exclusive Processes**

There is a delicate connection between CT and pQCD-factorization, namely, if the latter is applicable for the description of some hard exclusive process then CT necessarily appears for that process in the nuclear target. This is because without CT the multiple gluon exchanges before and after the hard process would not be suppressed. Therefore, CT is important for testing the applicability of factorization in exclusive hard processes.

## *2.1. Pion Electroproduction*

The process *A*(*e*,*eπ*+) at large space-like photon virtuality can be used to better understand the mechanism of the elementary *γ*∗ *p* → *π*+*n* transition. It is argued in ref. [21] that for the longitudinal photon the pion pole dominates, while for the transverse photon the quark-gluon degrees of freedom are important (PYTHIA/JETSET simulation). On the other hand, the factorization theorem [22] renders descriptions in terms of hadronic degrees of freedom to be questionable for the longitudinal photon. Having this uncertainty in mind, it is reasonable to assume that PLCs are formed both for longitudinal and transverse photon, i.e., in the non-polarized channel.

The experimental data [8] were taken in the collinear kinematics, **p***π* **q** = **p***e* − **p***e* . This leads to the following expression for the nuclear transparency (*z*-axis is parallel to the pion momentum **p***π*):

$$T = \frac{d^5 \sigma\_{eA \to \epsilon'\pi^+} / d^3 p\_{\epsilon'} d\Omega\_{\pi^+}}{Z d^5 \sigma\_{ep \to \epsilon'\pi^+ n} / d^3 p\_{\epsilon'} d\Omega\_{\pi^+}} = \frac{1}{Z} \int d^3 r \rho\_p(\mathbf{r}) \, \mathbf{e}^{-\int\_{\frac{\pi}{2}} d\omega' \sigma\_{nN}^{\text{eff}}(p\_{\pi\pi} z' - z) \rho(\mathbf{b}, z')}\,,\tag{4}$$

where *ρp*(**r**) and *ρ*(**r**) are the proton and nucleon densities, respectively. In Equation (4), the expansion of the pionic PLC is accounted for within the QDM [6] in terms of the effective pion-nucleon cross section:

$$
\sigma\_{\pi N}^{\text{eff}}(p\_{\pi \prime} z) = \sigma\_{\pi N}(p\_{\pi}) \left( \left[ \frac{z}{l\_{\pi}} + \frac{n^2 \langle k\_{\ell}^2 \rangle}{M\_{\text{CT}}^2} \left( 1 - \frac{z}{l\_{\pi}} \right) \right] \Theta(l\_{\pi} - z) + \Theta(z - l\_{\pi}) \right), \tag{5}
$$

where *σπN*(*pπ*) is the empirical total pion-nucleon cross section, *n* = 2 is the number of valence quarks and antiquarks, *M*2CT is the CT scale, and *lπ* is the pion formation length. In the hard interaction point, *z* = 0, the effective cross section (5) is reduced by a factor ∝ *M*−2 CT as compared to the empirical total cross section *σπN*(*pπ*). With increasing propagation distance *z* from the interaction point the effective cross section grows linearly with *z* and becomes equal to *σπN*(*pπ*) for *z* ≥ *lπ*. In the kinematics of the pion electroproduction the CT scale *M*2CT is given by *Q*2 = −(*pe* − *pe*)2.

Figure 1 displays the nuclear transparency as a function of *Q*2. The Glauber model results are obtained by replacing *σ*eff *πN*(*pπ*, *z*) → *σπN*(*pπ*) in Equation (4). We see that the Glauber model significantly underpredicts the transparency. The QDM with the formation length of Equation (2) with Δ*M*2 = 0.7 GeV2 is in a good agreement with data for all considered targets, except gold where Δ*M*2 = 1.4 GeV2 is closer to the data. In the considered kinematics the pion formation length varies in the interval *lπ* = 1.6 − 2.5 fm, i.e., it is comparable to the r.m.s. radii of light nuclei, 12C and 27Al. Due to larger average nucleon density, the relative effect of CT is, however, stronger for heavier targets.

**Figure 1.** Transparency vs *Q*2 for the (*e*,*eπ*+) reaction on the carbon (**a**), aluminum (**b**), copper (**c**), and gold (**d**) targets in the collinear kinematics. Dashed (magenta) line—Glauber model; thick (black) and thin (red) solid line—quantum diffusion model (QDM) with Δ*M*2 = 0.7 and 1.4 GeV2, respectively. The pion momentum is *pπ* = 2.793, 3.187, 3.418, 4.077, and 4.412 GeV/c for *Q*2 = 1.10, 2.15, 3.00, 3.91 and 4.69 GeV2, respectively, according to the kinematics of JLab experiment [8].

#### *2.2. Pionic Drell-Yan Process*

The process *π*− *p* → *l*+*l*−*n* at *p*lab = 15 − 20 GeV/c at small |*t*| and large invariant mass of the dilepton pair, *Ml*+*l*− , has been proposed to study the generalized parton distributions of the nucleon at J-PARC [23,24] (see also the feasibility study of W.C. Chang reported in [25]). Thus, the study of the nuclear transparency in the semiexclusive *A*(*π*−, *l*+*l*−) process is complementary to the studies of factorization. The expression for the transparency has a similar form to Equation (4) except that the integration is done along the trajectory of the incoming pion (along *z*-axis):

$$T = \frac{d^4 \sigma\_{\pi^- A \to l^- l^+} / d^4 q}{Z d^4 \sigma\_{\pi^- p \to l^- l^+ n} / d^4 q} = \frac{1}{Z} \int d^3 r e^{-\int\_{-\infty}^z dz' \sigma\_{\pi N}^{\text{eff}}(p\_\pi z - z') \rho(\mathbf{b}, z')} \rho\_p(\mathbf{r}) \tag{6}$$

where *q* = *pl*− + *pl*+ − *pπ* is the four momentum transfer from the nucleus to the dilepton pair. The effective pion-nucleon cross section, *σ*eff *πN*(*pπ*, *z*), is given by Equation (5) with *M*2CT = *M*2 *l*+*l*− . The selection of the exclusive transition *π*− *p* → *l* +*l* −*n* in the nucleus can be done either by restricting the longitudinal momentum transfer *qz* for fixed *qt* and *M*2 *l*+*l*− [26] or directly applying the missing mass method [25].

Figure 2 shows the transparency for the pionic Drell-Yan process as a function of *p*lab. The relative effect of CT grows with beam momentum due to increasing pion formation length and reaches ∼50–100% at *p*lab = 20 GeV/c. The effect is stronger for heavier targets. It is, however, interesting that in the calculation with CT the nuclear transparency reaches saturation at *p*lab = 15–20 GeV/c for light targets, 12C and 27Al, while it continues to increase with *p*lab for the heavier ones. This behavior is explained by the approximate relation *lπ* ∼ 2*R* which is fulfilled at the saturation. Thus, by measuring the beam momentum dependence of *T* on light nuclei it is possible to pin down the beam momentum dependence of the pion formation length (see, e.g., Figure 2a for 12C target where the shapes of the *p*lab dependence for Δ *M*2 = 0.7 GeV2 and 1.4 GeV2 significantly differ).

**Figure 2.** Transparency vs pion beam momentum for the (*π*−, *l* +*l* −) reaction at fixed *M*2 *l*+*l*− = 4 GeV2 on the carbon (**a**), aluminum (**b**), copper (**c**), and gold (**d**) targets. Dashed (magenta) line—Glauber model, thick (black) and thin (red) solid line—QDM with Δ*M*2 = 0.7 and 1.4 GeV2, respectively.

#### *2.3. Large-Angle Pion Photoproduction*

The mechanism of the *γn* → *π*− *p* process significantly depends on the invariants *t* = (*pn* − *pp*)2 and *u* = (*pn* − *pπ*)2. At |*t*| *s*/2 (|*u*| *s*/2) the photon converts to the *ρ*-meson long before the struck neutron and the process is dominated by the reggeized pion (nucleon) exchange. This regime is called the "resolved photon" (RP) regime which is based on the vector-dominance model [27]. With increasing min(|*t*|, |*u*|) the photon gradually looses its complex hadronic structure and interacts more and more like a bare electromagnetic state, i.e., the transition to the unresolved photon (UP) regime

takes place. (In literature, unresolved photon is also often called the "direct" or "point-like" photon.) There is presently no theory that describes the both regimes simultaneously. However, one can use phenomenology to estimate |*t*| at the transition. As follows from the asymptotic scaling law [28], in the UP regime the differential cross section *dσ*/*dt* of the *γN* → *πN* process should scale as *s*−7 at *s* → ∞, *t*/*s* = const. The scaling *s*−7 is observed at SLAC for *γp* → *π*+*n* at Θ*c*.*m*. = 90◦ for *s* >∼ 4 GeV2 [29]. Thus, the value of |*t*| at which the transition between the RP and UP regimes occurs can be estimated as ∼ *s*/2 ∼ 2 GeV2. On the other hand, the onset of CT is also expected at about the same values of |*t*| (see Figure 1 above). We expect then a complex interplay between the photon transparency (i.e., UP regime) and CT. How can one disentangle these two effects?

To this end we have calculated the nuclear transparency for the *A*(*γ*, *π*− *p*) process [30]:

$$\begin{split} T &= -N^{-1} \int \mathrm{d}^2 b \, \mathrm{d}z \, \rho\_{\mathrm{\boldsymbol{\mu}}}(b, z) \exp \Big( -\sigma\_{\gamma \mathrm{N}}^{\mathrm{eff}} \int \mathrm{d}z' \, \rho(b, z') \\ &- \int\_{l\_r}^{\infty} \mathrm{d}l \, \rho(b\_r, l) \sigma\_{\mathrm{\boldsymbol{\pi}} \mathrm{N}}^{\mathrm{eff}}(p\_{\mathrm{\boldsymbol{\pi}} \boldsymbol{\nu}}, l - l\_r) - \int\_{l\_r'}^{\infty} \mathrm{d}l' \, \rho(b\_{r'}, l') \sigma\_{\mathrm{pN}}^{\mathrm{eff}}(p\_{\mathrm{\boldsymbol{\mu}} \boldsymbol{\nu}}, l' - l\_r') \Big), \end{split} \tag{7}$$

where *z* is along photon beam, *ρn*(*b*, *z*) is the neutron density, *N* is the total number of neutrons. *l* and *l* denote the coordinates along the linear trajectories of the outgoing pion and proton, respectively. The initial values and impact parameters are calculated using the spherical symmetry of the target nucleus: *lr* = **rp***π*/*pπ*, *br* = -*r*2 − *l*2*r* , *lr* = **rp***p*/*pp*, *br* = -*r*2 − (*lr*)2, where **r** ≡ (**b**, *z*). The effective photon–nucleon cross section, *σ*eff *γ*N, accounts for the absorption of the intermediate *ρ*-meson in nuclear medium. In the RP regime, the distance traveled by the *ρ*-meson is approximately given by the photon coherence length

$$l\_{\gamma} = \frac{2p\_{\text{lab}}}{m\_{\rho}^2},$$

and we set *σ*eff *γ*N equal to the inelastic *πN* cross section. In the UP regime, the absorption of the photon is totally neglected, i.e., *σ*eff *γ*N = 0. The effective pion-nucleon cross section *σ*eff *π*N is given by the QDM expression, Equation (5), with *M*2CT = min(−*t*, −*u*). For simplicity, we apply Equation (5) with *n* = 3 for the effective proton-nucleon cross section *σ*eff *p*N with replacement *σπN* → *σp*N and assuming *lp* = *lπ* for equal momenta of the proton and pion.

Figure 3 displays the nuclear transparency calculated assuming the UP and RP regimes, but disregarding CT. In the RP regime, the nuclear absorption is stronger due to the large *ρN* cross-section. However, in both regimes the nuclear transparency shows up a rather flat behavior as a function of beam momentum.

In contrast, as shown in Figure 4, the effect of CT is the increase of the nuclear transparency with *p*lab. This is expected since the formation length grows with *p*lab. This qualitative difference may help to disentangle the transition to the photon transparency from the onset of CT.

**Figure 3.** Transparency for the 12C(*γ*, *π*− *p*) semiexclusive process at *t* = −2 GeV2 vs photon beam momentum. Calculations for the unresolved photon (UP) and resolved photon (RP) regimes are shown by the solid (black) and dashed (magenta) line, respectively.

**Figure 4.** Transparency for the 12C(*γ*, *π*− *p*) semiexclusive process at Θ*c*.*m*. = 90 ◦ vs. photon beam momentum. The band and solid line correspond to the QDM and Glauber model calculations, respectively. The upper (lower) boundary of the band is given by Δ*M*2 = 0.7 (1.1) GeV2.

#### **3. High-Energy Virtual-Photon–Nucleus Reactions**

The space-time scale of hadronization in high-energy *γ*∗*A* DIS reactions should also be dominated by the hadron formation length that has a similar dependence on the hadron momentum as in exclusive processes, see Equation (3). Due to CT, during the formation stage pre-hadrons interact with nucleons with reduced strength. This picture is supported, in particular, by the GiBUU calculations of hadron attenuation at HERMES and EMC [17]. Hadron formation can also be tested by studying the production of low-energy neutrons from the decay of excited nuclear remnant. This has been initiated by the E665 experiment at Fermilab [31], where the neutrons with energy below 10 MeV produced in *μ*− DIS at 470 GeV off H, D, C, Ca, and Pb targets have been detected. The main motivation was that the nucleus may serve as a "microcalorimeter" for high-energy hadrons: the excitation energy of the residual nucleus grows with the number of holes (wounded nucleons) and can be measured by the number of emitted low-energy neutrons. The first theoretical analysis of the E665 data performed in ref. [32] has led to the surprising conclusion that the CT effects are much stronger than those expected based on formation length (3) and are rather consistent with the scenario when only particles with momenta below ∼ 1 GeV/c interact with the nuclear remnant.

*Particles* **2020**, *3*

We applied the GiBUU model (see detailed description in ref. [20]) to study slow neutron production induced by the passage of the DIS products through the nucleus [33]. This model solves the coupled system of kinetic equations for the baryons (*N*, *N*∗, Δ, Λ, Σ ...), corresponding antibaryons (*N*¯ , *N*¯ ∗, Δ¯ , Λ¯ , Σ¯ ...), and mesons (*π*, *K*, ...) explicitly in time and six-dimensional phase space of particle position and momentum by using the method of test particles. The collision term includes two- and three-body particle collisions and resonance decays. High-energy elementary binary collisions (√*s* > 2.2, 3.4 and 2.38 GeV for meson-baryon, baryon-baryon, and antibaryon-baryon collisions, respectively) are simulated by the PYTHIA and (for antibaryon-baryon collisions only) FRITIOF models, while the low-energy ones are simulated my the Monte–Carlo method using empirical cross sections. Between collisions, the particles propagate along curved trajectories described by the Hamiltonian equations of motion in the non-relativistic Skyrme-like- and, optionally, relativistic (non-linear Walecka model) mean fields. (In the present calculations of DIS we apply the relativistic mean field NL3 of ref. [34]. We checked that using the medium (incompressibility *K* = 290 MeV) momentum-dependent Skyrme-like interaction (see Table 1 in ref. [20]) leads to practically indistinguishable results for neutron spectra. The pA and AA collisions were calculated in the cascade mode disregarding mean-field potentials.) In calculations, we applied the following alternative prescriptions for the pre-hadron-nucleon effective interaction cross section: (i) Time-dependent, based on the production (*t*prod) and formation (*t*form) times (see ref. [16], used as default in GiBUU) favored by the analysis of hadron attenuation at HERMES and EMC [17]:

$$
\sigma\_{\rm eff}(t)/\sigma\_0 = X\_0 + (1 - X\_0)\frac{t - t\_{\rm prod}}{t\_{\rm form} - t\_{\rm prod}},\tag{9}
$$

where *X*0 = *r*lead*a*/*Q*2, *a* = 1 GeV2, *r*lead – the ratio of the number of leading quarks to the total number of quarks in the pre-hadron. (ii) Time-dependent, based on the QDM [6]:

$$
\sigma\_{\rm eff}(t) / \sigma\_0 = X\_0 + (1 - X\_0) \frac{c(t - t\_{\rm hard})}{l\_h} \tag{10}
$$

where *t*hard is the time of hard interaction (collision time instant), and the formation length is given by Equation (2) with Δ*M*2 = 0.7 GeV2. Note that the arguments leading to the initial size ∼1/*Q* may not be applicable as we are dealing with inclusive process here. Thus, for simplicity we set *X*0 = 0. (iii) Momentum cutoff:

$$
\sigma\_{\rm eff} / \sigma\_0 = \Theta(p\_{\rm cut} - p\_h), \quad p\_{\rm cut} \sim 1 - 2 \,\text{GeV}/\text{c}.\tag{11}
$$

Cascade of the interactions of DIS products in the nucleus leads to the direct emission of fast particles, including neutrons, and to the hole excitations of the nuclear residue. In order to describe the evaporation of slow neutrons from the excited nuclear residue, we applied the SMM [35,36]. The mass number *A*res, charge number *Z*res, excitation energy *E*∗res, and momentum **p**res of the nuclear residue were determined from GiBUU at the end of the time evolution (*t*max = 100 fm/c) and used as input for the SMM.

Figure 5 displays the calculated energy spectrum of neutrons in comparison with E665 data. The spectra are obtained under conditions *ν* > 20 GeV, *Q*2 > 0.8 GeV2 that select DIS events (*ν* is the energy of virtual photon in the target nucleus frame). One can see that almost all neutrons below 1 MeV are statistically evaporated. The sensitivity to the model of hadron formation presents for *En* > 5 MeV. More restrictive conditions for the FSI of hadrons lead to smaller multiplicity of neutrons, mainly due to smaller excitation energy of the nuclear residue. The data can be only described with very strong restriction on the FSI (*p*cut = 1 GeV/c), in agreement with earlier calculations [32].

**Figure 5.** Energy spectrum of emitted neutrons in *μ*−+208Pb deep inelastic scattering (DIS) at 470 GeV. Different lines correspond to different prescriptions for the hadron formation: dash-dotted (red) line—QDM calculation; dotted (brown) line—Giessen Boltzmann–Uehling–Uhlenbeck (GiBUU) default; dashed (blue) line—cutoff momentum 2 GeV/c; solid (black) line—cutoff momentum 1 GeV/c. Upper (lower) lines are calculated with (without) adding evaporated neutrons from the nuclear residue. Experimental data are from ref. [31].

Various scenarios for hadron formation can be tested in ultraperipheral collisions (UPCs) of heavy ions. In such processes, the quasireal photons are emitted coherently by the entire nucleus [37] and get absorbed by another nucleus. The maximal longitudinal momentum of the photon in the c.m. frame of colliding nuclei (collider lab. frame) is determined by the inverse radius of the Lorentz-contracted nucleus:

$$k\_L^{\text{max}} \simeq \frac{\gamma\_L}{R\_A} \tag{12}$$

where *γL* is the Lorentz factor. For symmetric colliding system in the rest frame of the target nucleus the maximum photon momentum is expressed as follows:

$$k^{\text{max}} = \gamma\_L 2k\_L^{\text{max}} \simeq \frac{2\gamma\_L^2}{R\_A} \,. \tag{13}$$

Table 1 summarizes the parameters of the symmetric UPCs at RHIC and the LHC. (*W* is the *γN* c.m. energy.) It is clear that using UPCs at these colliders one can study photon–nucleus interactions in the energy region never reachable so far and address the physics of hadronization in nuclear medium.

**Table 1.** Parameters of ultraperipheral collisions (UPCs) Au + Au at RHIC and Pb + Pb at the LHC.

We will focus on the photon–gluon interaction producing two jets: *γ*∗*g* → *qq*¯ . The LC momentum fraction of the gluon is

$$\mathbf{x}\_{\mathcal{S}} = \frac{Q^2 + M\_{q\eta}^2}{2Pq} \simeq \mathbf{x} + \frac{M\_{q\eta}^2}{W^2} \,. \tag{14}$$

where *P* and *q* are the four-momenta of the struck nucleon and virtual photon, respectively, *W*2 = ( *P* + *q*)2, and *Mqq*¯ is the invariant mass of the dijet. In the last step of (14) we assumed small *Q*2. For the typical setting at the LHC [38]:

$$M\_{\bar{q}q} \simeq |p\_t(\text{jet}\_1)| + |p\_t(\text{jet}\_2)| \gtrsim 40\,\text{GeV}.\tag{15}$$

This condition eliminates *xg* in the gluon shadowing region.

In the GiBUU program package, the initial hard interaction is simulated via the PYTHIA model that can only describe a virtual photon emitted by the scattered lepton, *l* → *l γ*∗. Moreover, the events with two high-*pt* jets are very rare. Thus, we rather rely on the inclusive set of PYTHIA events with fixed Bjorken *x* = (40 GeV)2/*W*2. It is clear from Equation (14) that this will produce the same lower limit on *xg* and, therefore, the same fragmentation pattern of the nucleon as in the case of the dijet production by the direct photon. This is important since the nucleon debris largely determine the production of slow particles.

Figure 6 shows the transverse momentum spectra of neutrons emitted in the hard virtual photon collisions with lead target in the fixed kinematics (a), and with lead and gold targets in different kinematics (b). The spectra are calculated with condition *xF* > 0.1 [39] which guaranties that the neutrons longitudinal momenta are directed along the target nucleus momentum in the collider laboratory frame. The Feynman variable *xF* is expressed as

$$\text{tr}\_F = \frac{E - p^z}{(E\_A - p\_A^z)/A'} \tag{16}$$

where *E* (*EA*) and *pz* (*pz A*) are the particle (target nucleus) energy and the longitudinal component of momentum, respectively. The neutron spectra at *pt* =100–200 MeV/c show up a strong sensitivity to the hadron formation model. However, the photon kinematics has practically no influence. Thus, folding with actual photon flux is not expected to change significantly the neutron *pt*-spectrum. Note that also the choice of the nuclear target (lead or gold) practically does not change the results.

**Figure 6.** Neutron transverse momentum spectra for *γ*∗+nucleus deep inelastic collisions. (**a**) Spectra for fixed photon kinematics *W* = 100 GeV, *x* = 0.16 on the 208Pb target with different prescriptions for hadron formation (line notations are the same as in Figure 5). Upper (lower) lines show calculations with (without) statistical evaporation. (**b**) Spectra for the different photon kinematics and nuclear targets as indicated calculated with *p*cut = 1 GeV/c.

NICA allows to study the UPCs too. In Table 2 we provide the estimates of the parameters of the maximum photon momentum and *γ N* c.m. energy reachable in Au + Au and p + Au collisions. For the latter, the photon can be emitted either by the gold nucleus or by the proton (we assume proton radius of 0.6 fm). Correspondingly, either *γp* or *γ*Au collisions are considered. In the Au + Au UPCs one can study the baryon resonance excitation in nuclear medium and perform studies complementary to the JLab program. In the p + Au UPCs with photon emitted by the proton we enter in the regime where the study of hard pQCD processes such as *J*/*ψ* production and large-angle scattering become possible.

**Table 2.** Parameters of UPCs Au + Au and p + Au at NICA.

#### **4. Proton–Nucleus and Nucleus–Nucleus Collisions**

Hadron formation reduces the FSI of pre-hadrons and thus we expect that the rapidity and transverse momentum distributions of produced particles in pA- and AA collisions will be affected. In this exploratory study we do not separate particles in the nuclear interior from those emitted in free space. In the case of pA collisions the calculation is performed in the rest frame of the target nucleus, while heavy ion collisions are calculated in the c.m. frame of the colliding nuclei.

Figure 7 displays the rapidity distributions of p, *π* and Λ + Σ0 in p + Au and central Au + Au collisions at √*sNN* = 11 GeV. Neglecting formation length (i.e., assuming that hadrons are instantly formed) results in the largest yields at the intermediate rapidities (*y* 1 for p + Au, *y* 0 for Au + Au). Restricting the FSI of produced particles by the introduction of either finite formation length or momentum cutoff depletes the intermediate rapidity region. The default GiBUU formation method and the QDM give almost indistinguishable results for p + Au collisions while for Au + Au collisions the QDM gives somewhat less stopping and less pion production than the GiBUU-default. Applying the momentum cutoff leads to the strongest constraints on the FSI. For the p + Au system, the resulting rapidity distributions become depleted at *y* 1 and enhanced at *y* 4. The two bumps at these two rapidities are populated by the products of the target and projectile fragmentation, respectively. For the Au + Au system at *b* = 1 fm, the momentum cutoff leads to the transparency pattern, especially pronounced for protons and hyperons in calculation with *p*cut = 1 GeV/c.

Figure 8 shows the *pt* spectra of p, *π* and Λ + Σ0. The bump in the proton spectrum at low *pt*'s is due to the bound protons in the target nucleus. Elastic rescattering increases the transverse momenta of outgoing hadrons. (This effect has been also observed in calculations of large-angle *d*(*p*, *pp*)*n* [40] and *d*(*p*¯, *π*−*π*0)*p* [41] exclusive processes.) Thus, reducing FSI due to hadron formation makes the spectra steeper at large *pt* (closer to the direct production in first-chance NN collisions.

**Figure 7.** *Cont*.

**Figure 7.** Rapidity spectra of protons, pions and neutral hyperons produced in minimum bias p+Au collisions and central (*b* = 1 fm) Au + Au collisions at √*sNN* = 11 GeV. Different lines show calculations with different prescriptions for hadron formation (line notations are the same as in Figure 5). Arrows show rapidities of the projectile and target in the laboratory frame.

**Figure 8.** Transverse momentum spectra of protons, pions and neutral hyperons produced in minimum bias p + Au collisions and central (*b* = 1 fm) Au + Au collisions at √*sNN* = 11 GeV. Different lines show calculations with different prescriptions for hadron formation (line notations are the same as in Figure 5). For the case of Au + Au collisions, the rapidity cut |*y*| < 0.5 has being applied for the spectra.


*2.3. Remote Sensing Data*

For the subsequent spatial-scale research in Changchun, the Landsat 8 images (30 m) and MOD11A1 data (1 km) in 2017 were downloaded from the website of USGS (https:// earthexplorer.usgs.gov/ accessed on 21 June 2021) with the aim of acquiring the LST data. The available remote sensing images can be seen in Table S1. Daily LST data were derived from the MOD11A1 product (1 km). Due to the cloudiness, there were only four clear Landsat 8 images in 2017 relevant to maize growth that would be used for mapping maize in the research area and verified by the field monitoring data, cropland map from the GlobeLand30 platform, and statistics data.

To obtain the maize distribution in Changchun, some calculations and preprocessing needed to be finished. Firstly, the normalized difference vegetation index (NDVI) and land surface water index (LSWI) were calculated by Equations (1) and (2), which were also followed for four Landsat 8 images (Operational Land Imager, OLI) [41,42]. The Landsat 8 image on 25 September, which is the best one just in the maize growth stage, was chosen as a sample to classify the region of interest (ROI). The classification thresholds could be determined by combining the features of vegetation in different stages and the changes in NDVI and LSWI of ROI samples. The process contained the following four steps (Figure S1): (1) to distinguish the vegetation and the others according to the NDVI values on 25 September (Julian day 268), which belonged to the optimum discrimination period of vegetation in four images; (2) to obtain the thresholds by analyzing the NDVI values of forest and crop samples on 5 June (Julian day 156), when the crops were in the early growth stage, and the NDVI of the forest should be obviously higher; (3) to employ the LSWI of rice samples in ROI on 5 June (Julian day 156) to determine the corresponding thresholds considering the rice was irrigated during this stage, and the LSWI values of rice should be higher; and (4) to determine the relative thresholds between the maize and the other vegetations by analyzing the LSWI ranges of them in ROI on 5 June (Julian day 156) and 25 September (Julian day 268). Based on these thresholds, the distribution of maize + rice could be obtained according to the classification rules of decision tree classification.

$$\text{NDVI} = (\mathfrak{p}\_{NIR} - \mathfrak{p}\_{RED}) / (\mathfrak{p}\_{NIR} + \mathfrak{p}\_{RED}) \tag{1}$$

$$\text{LSWI} = (\mathfrak{p}\_{NIR} - \mathfrak{p}\_{SWIR}) / (\mathfrak{p}\_{NIR} + \mathfrak{p}\_{SWIR}) \tag{2}$$

where ᐩ*NIR* (band5), ᐩ*RED* (band4), and ᐩ*SW IR* (band6) are the reflectivity of near-infrared, red, and shortwave infrared band, respectively.

Based on the decision tree classification mentioned above, the combination pattern for maize + rice in Changchun was obtained (Figure 3a). As the dominant field crops are maize and rice here, it was assumed that these results are mostly cropland. In order to evaluate the classification precision, the land-cover data with a spatial resolution of 30 m in 2020 (Figure 3b) were downloaded from the platform of GlobeLand30 (http://www.globallandcover.com/ home.html accessed on 3 October 2021), published by the Ministry of Natural Resources of China. Meanwhile, the statistical data of crop areas were obtained from the Statistic Bureau of Jilin Province (http://tjj.jl.gov.cn/tjsj/ accessed on 3 October 2021) for further evaluating the classification precision.

**Figure 3.** Crop patterns and cropland in Changchun area: (**a**) maize and rice obtained by decision tree classification; (**b**) cropland data in 2020 from the GlobeLand30 platform; (**c**) maize mapping by decision tree classification.

Using the confusion matrix, the value of the producer's accuracy (ratio of the number of estimated correct pixels to reference pixels) about cropland was 82.18%, determined through comparing Figure 3a,b. This value proved that the classification method established above was available and appropriate. It should be emphasized that the validation method used here was restricted by the accuracy of the GlobeLand30 product. Meanwhile, this product is not offered for every year, which will affect the accuracy evaluation of crop mapping. The maize mapping could be obtained by the same method (Figure 3c), which took up 87% of cropland in Figure 3b. The ratio was consistent with the value from statistics data (84%).

For the Jiefangzha sub-irrigation district, the spatial distribution of maize was derived from the report from Bai et al. [39], as shown in Figure 1c. Fortunately, the images from Landsat 8 (30 m) and MOD11A1 (1 km) can support the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) algorithm [43] to improve the spatial accuracy of LST. Therefore, the fused LST was used in this region. Details of the extraction process can be found in research by Huang et al. [40].


*3.2. Statistics*

We use both accounting measures like Return on Assets (ROA) and Return on Equity (ROE) and market measures that is Tobin's Q (TQ) of profitability. We classify the firms producing a single product as specialized with a value of 0 and firms producing more than one product as diversified firms with a value of 1. Afza et al. (2008) apply the same criteria to measure product diversification. The proxy for geographic diversification is the proportion of foreign sales to total sales. Schmid and Walter (2012) also use this proxy in their study. We measure capital structure as a ratio of total debts to total assets of the firm. Bhaduri (2002) uses the same measure of capital structure. We calculate dividend per share as the proxy of dividend policy. Oloidi and Adeyeye (2014) use the same proxy in their study. We use the proxy for investment policy as a change in investment in fixed assets. Aivazian et al. (2005) apply this measure of investment in their study.

We measure board size as a number of board members. Bhagat and Bolton (2008) use this proxy in their research. For CEO duality, we use value 1 if the CEO is also a director of the firm, otherwise, it is 0 (Bhagat and Bolton 2008). Audit quality characteristics include audit quality, audit committee size and audit committee activity. We take value 1 if the firm is audited by big four audit firms, otherwise, 0 followed the approach of Francis and Yu (2009). We measure audit committee size as a total number of members in audit committee (Azim 2012). We calculate the audit committee activity as the frequency of audit committee meetings in a financial year (Xie et al. 2003). We calculate the firms' size as the natural log of total assets. In our study, we measure growth as the percentage change in sales. Further, we measure the firms' age as the difference between the year in which a firm starts and the year in which it exists in the sample. Hunjra et al. (2014); Muritala (2012) apply similar calculations of size, growth and age for analysis. The description of variables is given in Table 2.

We use the following equations to analyze the results:

$$\begin{aligned} \text{ROA}\_{\text{i,t}} &= \alpha\_{\text{i,t}} + \beta\_{\text{1}} \text{PD}\_{\text{i,t}} + \beta\_{\text{2}} \text{GD}\_{\text{i,t}} + \beta\_{\text{3}} \text{CS}\_{\text{i,t}} + \beta\_{\text{4}} \text{DP}\_{\text{i,t}} + \beta\_{\text{5}} \text{IP}\_{\text{i,t}} + \beta\_{\text{6}} \text{BSIZE}\_{\text{i,t}} + \beta\_{\text{7}} \text{CEOD}\_{\text{i,t}} + \varepsilon \\ &\quad \beta\_{\text{8}} \text{AQ}\_{\text{i,t}} + \beta\_{\text{9}} \text{ACSIZE}\_{\text{i,t}} + \beta\_{\text{10}} \text{ACA}\_{\text{i,t}} + \beta\_{\text{11}} \text{AGE}\_{\text{i,t}} + \beta\_{\text{12}} \text{CRTH}\_{\text{i,t}} + \beta\_{\text{13}} \text{SIZE}\_{\text{i,t}} + \varepsilon\_{\text{i,t}} \end{aligned} \tag{1}$$

$$\begin{array}{l} \text{ROE}\_{\text{i,t}} = \alpha\_{\text{i,t}} + \beta\_{\text{1}} \text{PD}\_{\text{i,t}} + \beta\_{\text{2}} \text{GD}\_{\text{i,t}} + \beta\_{\text{3}} \text{CS}\_{\text{i,t}} + \beta\_{\text{4}} \text{DP}\_{\text{i,t}} + \beta\_{\text{5}} \text{IP}\_{\text{i,t}} + \beta\_{\text{6}} \text{BSIZE}\_{\text{i,t}} + \beta\_{\text{7}} \text{CEOD}\_{\text{i,t}} + \varepsilon\\ \beta\_{\text{8}} \text{AQ}\_{\text{i,t}} + \beta\_{\text{9}} \text{ACSIZE}\_{\text{i,t}} + \beta\_{\text{10}} \text{ACA}\_{\text{i,t}} + \beta\_{\text{11}} \text{AGE}\_{\text{i,t}} + \beta\_{\text{12}} \text{GRTH}\_{\text{i,t}} + \beta\_{\text{13}} \text{SIZE}\_{\text{i,t}} + \varepsilon\_{\text{i,t}} \end{array} \tag{2}$$

$$\begin{aligned} \text{TQ}\_{\text{i},\text{t}} &= \alpha\_{\text{i},\text{t}} + \beta\_{\text{1}} \text{PD}\_{\text{i},\text{t}} + \beta\_{\text{2}} \text{GD}\_{\text{i},\text{t}} + \beta\_{\text{3}} \text{CS}\_{\text{i},\text{t}} + \beta\_{\text{4}} \text{DP}\_{\text{i},\text{t}} + \beta\_{\text{5}} \text{IP}\_{\text{i},\text{t}} + \beta\_{\text{6}} \text{OSIZE}\_{\text{i},\text{t}} + \beta\_{\text{7}} \text{CEOD}\_{\text{i},\text{t}} + \varepsilon \\ &\quad \beta\_{\text{8}} \text{AQ}\_{\text{i},\text{t}} + \beta\_{\text{9}} \text{ACSIZ}\_{\text{i},\text{t}} + \beta\_{\text{10}} \text{ACA}\_{\text{i},\text{t}} + \beta\_{\text{11}} \text{AGE}\_{\text{i},\text{t}} + \beta\_{\text{12}} \text{GRTI}\_{\text{i},\text{t}} + \beta\_{\text{13}} \text{SIZE}\_{\text{i},\text{t}} + \varepsilon\_{\text{i},\text{t}}. \end{aligned} \tag{3}$$

We use descriptive statistics to check the normality of data and correlation is used to check the multicollinearity. We apply the Generalized Method of Moments (GMM), as this method performs consistent parameter estimation for the small time period and for a large cross-section. The GMM estimators enable asymptotically efficient inferences employing a relatively minimal set of assumptions (Arellano and Bond 1991; Blundell and Bond 1998). We deal with the unobserved heterogeneity by applying a fixed effect or by taking the first or second difference. The ability of first or second difference to remove the unobserved heterogeneity is developed for two-step dynamic panel data models. Furthermore, these models contain one or more lagged dependent variables and allow modeling of a partial adjustment mechanism.


**7. Conclusion**

Outer membrane targeting is a revolutionary strategy for antibiotic discovery. Gram-negative pathogens will become sensitive to the range of clinically approved Gram-positive active antibiotics when their OM is perturbed. In this way, chemical space compatible with novel antimicrobial peptides would be expanded. However, there are many obstacles before performing this approach in the clinic successfully. The success or failure of this approach depends on the correct selection or development of the outer membrane perturbant and antibiotic adjutant combination. In comparison to monotherapy approaches as other combination therapies, dosage optimizing for adequate overlap in bioavailability approves difficulty and needs

more complicated clinical trials [77]. Spontaneous resistance development, horizontally acquired resistance genes, and biofilm formations are all significant barriers to successful antibiotic treatment. The capacity for OM disruption to overcome many of these challenges, uniquely positioning this approach among discovery efforts in the Gram-negative resistance crisis [19].


**4. Conclusions**

Pesticide formulations would have a significant impact on the biological effect for the studied pesticides. Efficacy, knockdown effect and period of effectiveness protection can be affected by formulation of an insecticide. All these parameters are determinant in the design of an application program of insecticides, but in most cases are unknown by the user, the farmer, generating consequently that a greater number of applications are realized. This increases the impact on the environment and likely the costs associated with pest management.

On the other hand, when insecticide residue can be affected by formulation (both in the initial deposit and in its persistence), other parameters inherent to the active ingredient and the capacity of detection and quantification of the measurement technique may be mediating the real importance that the formulation may have on the behavior of the residue. In this chapter, the formulation was determinant in the residual pattern of acetamiprid, imidacloprid, and diazinon, but not for l-cyhalothrin. Future complementary works may focus on considering other interaction between variables such as relevance of the type and concentration of coformulants; fruit growth stage; climatic conditions and spraying method; and fruit or vegetable species, since all of them dynamically seem to affect in some degree the behavior of the insecticide residue generated. This in turn suggests that in some cases the estimation of preharvest intervals calculated for certain active ingredients may be affected by the formulation used in the baseline studies, generating the risk of an underestimation of that interval.

Therefore, when formulations of the same active ingredient are widely available, it is desirable to have independent declination curves of insecticide residue for at least those which differ drastically in type of coformulants or adjuvants and its concentration of active ingredient, considering that there is a risk of significant variations in the behavior of the residue.


Meet the editors

Professor Magdy Elnashar is employed at the School of Medicine, Curtin University, Australia as well as the Centre of Excellence for Advanced Sciences, Egypt. He received his Ph.D. in Biochemistry from Leeds University, United Kingdom. Dr. Elnashar is the recipient of many awards in teaching, research, and commercialization from Australia, the United Kingdom, the United States, and Egypt. He also received the 2019 Austra-

lian Award for University Teaching. He has six patents and several research articles, books, and book chapters to his credit. His research expertise is in preparing/grafting hydrogels, immobilized enzymes, drug delivery systems, and nanoparticles/ nanocomposites.

Associate Professor Selcan Karakuş is currently working in the Department of Chemistry, Istanbul University-Cerrahpasa (IUC), Turkey. She received her MSc in Physical Chemistry from Istanbul University (IU) in 2006. She received her Ph.D. in Physical Chemistry from IU in 2011. She worked as a visiting researcher in the Department of Polymer Science and Engineering, University of Massachusetts, USA. Dr. Karakuş has research

experience in drug carrier systems, nanoparticles, nanocomposites, nanoemulsion self-assembled polymeric nanostructures, biosensors, and copolymer blends. She has worked on different projects funded by IUC and has published several research articles and book chapters.

## Contents

Preface

This book provides a comprehensive overview of biocomposites. It discusses current research, applications, and cutting-edge technological advancements in the basic

Chapter 1: "Introductory Chapter: Introduction to Biocomposites – New Insights"

Chapter 3: "Reactive Extrusion as an Environmentally Friendly Technology for the Production of Bio(Nano)Composites: Implementation and Characterization"

We truly appreciate our authors for their contributions. We would also like to express our gratitude to the editorial staff at IntechOpen for their support throughout the

> **Magdy M.M. Elnashar** School of Medicine, Curtin University, Perth, Australia

**Selcan Karakuş** Faculty of Engineering, Department of Chemistry, Istanbul University-Cerrahpasa,

Istanbul, Turkey

Chapter 5: "Antibacterial, Antifungal and Antiviral Nanocomposites: Recent

Chapter 6: "Perspective Chapter: Tissue-Electronics Interfaces"

Chapter 2: "Physicochemical Characterization of Nanobiocomposites"

characteristics of biocomposites. It includes six chapters:

Chapter 4: "The Porosity of Nanofiber Layers"

Advances and Mechanisms of Action"

publication process.