**1. Introduction**

In Europe, the existing building stock is more than 50 years old, and about 40% of the existing residential buildings were constructed before the 1960s, when building regulations for energy consumption were limited [1]. Consequently, around 75% of the existing building stock in the European Union (EU) is energy inefficient [2]. In the United States, most existing houses were built before the establishment of the Building Energy Codes Program in 1992 by the U.S. Department of Energy, Washington, DC, United States [3]. These older buildings represent about 68% of the national residential building stock and are typically energy inefficient due to air leakage and inadequate insulation [2]. The National Renewable Energy Laboratory has identified approximately 34.5 million homes with wood studs that have no wall insulation [4]. Overall, in both the United States and Europe, a large portion of residential buildings will need some type of renovation, retrofit, or upgrade in the next five to 10 years.

There have been large investments in energy efficiency-related renovation in the global market. In the period 2012–2016, in the EU, more than EUR 200 billion were invested in energy renovations for residential buildings. In the next decades, energy renovation will become the key determiner for achieving the carbon-neutral goal. The European Climate Foundation has outlined three key areas for the building industry to maintain its trajectory toward zero emissions. One of the areas is reducing energy demand—specifically the operating energy demand—through renovation of the building stock [5]. In the United

**Citation:** Hu, M. Beyond Operational Energy Efficiency: A Balanced Sustainability Index from a Life Cycle Consideration. *Sustainability* **2021**, *13*, 11263. https:// doi.org/10.3390/su132011263

Academic Editors: Carlos Morón Fernández and Daniel Ferrández Vega

Received: 31 August 2021 Accepted: 5 October 2021 Published: 13 October 2021

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

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

Sates, more than USD 279 billion could be invested in building retrofits, resulting in more than USD 1 trillion in energy savings over 10 years, equal to a savings of about 30% of the annual electricity used in the United States (Rockefeller Foundation 2012, New York City, NK, The United States) [6]. The above figures highlight the significant impact building retrofits already have within the EU and American economies. There is space for tremendous potential and growth in both the European Union and the United States.

Currently, the building sector responds to the need for energy retrofits by focusing on an operating energy use reduction. Increasing numbers of companies have announced their commitment to the net zero carbon goal, based on the assumption that operating energy savings leads to an overall carbon emissions reduction and a healthy environment. However, the question remains: Is the current energy-centric renovation approach sustainable? Several studies have looked at the relation between operating energy and embodied energy. Dodoo et al. [7] found that an increase in the thermal mass in the building envelope reduced the cooling load, and hence the operating energy demand; however, such a renovation increases embodied energy considerably. Ellura et al. [8] adopted a life cycle approach for studying net zero energy building, and results showed that when the addition of embodied energy was included in the whole life cycle emission count, the net zero building performance largely shifted away from the nearly zero energy goal. Hu studied energy-efficient renovations in comparison to existing buildings, and showed that the new construction had greater environmental impact potential due to the new building materials added. Such results raised concerns of focusing only on an operating energy reduction while overlooking the added environmental impact [9].

In recent years, there have been studies focusing on the trade-off between embodied and operating carbon. Crawford et al. [10] studied the impact of different building materials of eight residential construction assemblies; a theoretical generic building was used as a base building. Rossello-Batel et al. [11] studied the relation between reduced heating demand and the embodied energy of different building typologies and building envelope options. They found that adding additional insulation in the façade can reduce energy demand to one third of the existing condition while having the highest increase in embodied energy. Stephan et al. [12] also found an increase in insulation in passive houses could reduce the heating demand in the winter, but such a decrease was offset by the higher embodied energy embedded in the insulation materials. With the increase in research on the relation between embodied and operating carbon, most studies were conducted on theoretical conditions using simulated data. Studies using data from actual renovated buildings are limited due to inaccessibility of the data.

The importance of understanding the trade-off between operating and embodied energies and their related carbon emissions has been gradually recognized by practitioners and researchers. Consequently, creating a comprehensive and holistic measurement of sustainability for building energy retrofits has become an emerging research topic. However, there have been very few studies and efforts on this topic, and proposed measurements vary greatly. For example, Bakar et al. [13] proposed using an energy efficiency index as an indicator for measuring building energy performance. Such an index is calculated as the ratio of the energy input to the factor related to the energy-using component. The embodied energy was included as one related factor and measured by the weight of the raw material used. Varusha et al. [14] suggested using the EE factor to quantify the trade-off between the embodied and operating energies of a building. The EE factor is calculated as the ratio of operating energy to embodied energy of a proposed building design against the ratio of a base building based on the ASHRAE 2016 benchmark. Triana et al. [15] proposed a sustainability index in the building life cycle energy use that includes life cycle energy consumption, life cycle carbon emissions, thermal comfort hours, and cost of the building energy life cycle. Those four values are added together and then divided by four to get the sustainability index. However, there is no sustainability index proposed especially for a building retrofit yet.

To respond to such a research gap, the aim of this study is to reveal the importance of considering embodied energy in current energy retrofit practices since the most

energy-efficient building is not necessarily the most sustainable building. Consequently, a comprehensive measure of the sustainability of a renovation project is proposed to measure the effectiveness of a building energy retrofit by integrating the life cycle assessment. This paper uses eight actual energy retrofit projects to demonstrate the trade-off between operating energy and embodied energy. The renovation techniques applied to the eight buildings include a building envelope retrofit, a building heating and ventilation system renovation and upgrades, a lighting system upgrade, and other renovation techniques. This study contributes mainly to the body of knowledge of sustainability by (1) highlighting the importance of embodied energy consideration in energy retrofit projects, (2) presenting a new measure for a sustainability index for renovation projects, and (3) testing the proposed sustainability index and evaluating the sensitivity of the results by applying them to real projects.


**3. Simulation**

The spectral components of the OPC system described in Section 2 was simulated using an experimentally confirmed model [32–40] that takes into account residual Raman gain from the primary pump at 1366 nm to the signal in the C-band as well as pump depletion from both pumps to the lower order pumps and the signal components, double Rayleigh scattering (DRS) and amplified spontaneous emission (ASE) noise for each of the signals. Noise was calculated in a room temperature of 24 ◦C and 0.1 nm bandwidth. Wavelength dependent Raman gain coefficient and attenuation factor were independently chosen for each WDM component as well as primary pump and lasing at the frequency of the FBG using tables for the SMF. Rayleigh backscattering coefficients for Raman pump, lasing and signal were 1.0 × 10−4, 6.5 × 10−5 and 4.5 × 10−5 km−1, respectively. In [28] we showed that higher reflectivity (99%) provides lower nonlinear phase shift compared with 70% and 50% as well as better power efficiency performance without sacrificing output OSNR. To find the best gain profile, the center wavelength of high reflectivity (99%) FBGs was varied from 1456 to 1464 nm for WDM section of transmitted channels and from 1442 to 1454 nm for the conjugated copy with a 2 nm step. The loss and bandwidth of the FBG in the simulations was set to 0.2 dB and 200 GHz, respectively.

Figure 3 illustrates the WDM grid of the mid-link OPC system. Total bandwidth of 6 THz consists of 30 transmitted WDM channels with 100 GHz spacing ranging from 192–194.9 THz and 30 WDM conjugated copies of the original signal in the range 195.1–198 THz (we also present results for 191–197 THz grid). We assumed 200 GHz as the guardband in the middle of the grid centered at 195 THz for the optical phase conjugator. The signal power profile of each WDM component of the original and conjugated signal was simulated independently.

**Figure 3.** WDM grid of the transmitted (**left**) and the conjugated (**right**) WDM channels in a mid-link OPC system. "\*" denotes conjugated channels.

Forward pump power of the Raman fibre laser at 1366 nm was simulated from 0.7 to 1.6 W with 100 mW step. Backward pump power was simulated to give 0 dB net gain for the channel under investigation, then the rest of the WDM channels were simulated with the same forward and backward pump powers: one set of results consists of 30 possible combinations (optimisation towards first channel CH1, second channel CH2 and so on). Conjugated signals were simulated with the same pocedure. To summarise, we simulated every possible combination varying forward pump power (0.7–1.6 W) for each installed FBG (1442–1454 nm for original and 1456–1464 nm for conjugated WDM grid) and finally compared asymmetry between both WDM channel sets to achieve the best overall asymmetry performance in an OPC system with second order Raman amplification.

The asymmetry was calculated using formula:

$$\frac{\int\_{0}^{L} |P\_1(z) - P\_2(L-z)|dz}{\int\_{0}^{L} [P\_1(z) + P\_2(z)]/2dz} \times 100\tag{1}$$

where *L* is the span length and *P*1 and *P*2 represent signal power evolution of the transmitted and conjugated channels, respectively.

#### **4. Results and Discussion**

#### *4.1. 191–197 THz WDM Grid*

For a given WDM grid bandwidth, the key factors that need to be optimized for the best performance overall in a mid-link OPC system are the wavelength of the FBG and the forward and backward pump powers. Both optimizations must be done separately for the original and conjugated WDM grid. Our initial investigation was done for a WDM grid 191–197 THz. Firstly, we had to select the right set of FBGs that will have a performance impact for residual channels located at the beginning and the end of the band.

In Figure 4 we show the impact of the FBG wavelength on asymmetry performance in WDM grid under investigation. Here, the central wavelength of the FBG for original channels was set to 1462 nm (the best configuration) and the one of the FBG for conjugated channels was varied from 1442–1450 nm. The asymmetry of residual side channels can be improved by 12% (CH1: 21% with 1450 nm and 33% with 1442 nm). However, this advantage is lost for CH6 and CH7 where the asymmetry performance is reversed, hence we need average overall performance. In this case the matching FBGs pair was 1462 nm for original and 1446 nm for conjugated WDM grid (we also simulated FBG for original WDM grid from 1456–1464 nm, however, for clarity we only show the best performing configuration with 1462 nm). Next, we optimized forward and backward pump powers for original and conjugated WDM grid.

**Figure 4.** Impact of a FBG choice on asymmetry performance in 191–197 THz grid. Wavelength of a FBG for original WDM grid was set to 1462 nm and FBG for the conjugated copy was varied from 1442–1450 nm.

In Figure 5 we show the impact of pump power on asymmetry performance for original and conjugated WDM grid. Forward pump power for both configurations was varied from 1–1.6 W. Backward pump power from 2.6–2.9 W for original (191–193.9 THz) and 1.6–1.9 W for conjugated (194.1–197 THz) WDM grid. The difference in backward pump power for each WDM grid results from wavelength dependent Raman gain and fiber attenuation.

By analyzing all possible combinations of FBG, forward and backward pump powers for original and conjugated WDM grid, we found the profile that gave the best average asymmetry of 6.4%; however, this figure is biased by first residual channels that are on the edge of Raman gain profile.

**Figure 5.** Impact of pump power choice (original WDM grid) on asymmetry performance. Forward pump power of the conjugated grid fixed to be 1.3 W. FBG for original and conjugated WDM grid was set to 1462 and 1446 nm, respectively.

In Figure 6 we show the best profile with the following settings: FBG 1462 nm with forward pump power 1 W for the original, and FBG 1446 nm with 1.3 W forward pump power for the conjugated WDM grid.

#### *4.2. 192–198 THz WDM Grid*

Based on the results shown in Section 4.1, we decided to repeat the simulations with a grid shifted by 1 THz to avoid the worst performing channels in the beginning of the spectrum. The new grid was set to 192–198 THz. Based on the knowledge from the previous configuration we only tested the FBG sets 1458–1460 nm and 1450–1454 nm for original and conjugated WDM grid, respectively.

In Figures 7 and 8, we show the impact of a FBG choice on asymmetry performance in 192–198 THz grid. The optimum configuration giving the lowest average asymmetry was with FBG set to 1460 nm and 1450 nm for the original (Figure 9) and conjugated (Figure 10) WDM grid, respectively. The right choice of a FBG helps to optimize the gain profile of the WDM band, which will particularly have an impact for an on–off gain of residual front and end channels. This will directly affect asymmetry performance.

**Figure 7.** Impact of a FBG choice on asymmetry performance in 192–198 THz grid. FBG for original WDM grid was set to 1458 nm and conjugated varied from 1450–1454 nm.

**Figure 8.** Impact of a FBG choice on asymmetry performance in 192–198 THz grid. FBG for original WDM grid was set to 1460 nm and conjugated varied from 1450–1454 nm.

**Figure 9.** Impact of pump power choice (original WDM grid) on asymmetry performance. Forward pump power of the conjugated grid was fixed to be 1.3 W. FBG for original and conjugated WDM grid was set to 1460 and 1450 nm, respectively.

**Figure 10.** Impact of pump power choice (conjugated WDM grid) on asymmetry performance. Forward pump power of the original grid was fixed to be 1.1 W. FBG for original and conjugated WDM grid was set to 1460 and 1450 nm, respectively.

After choosing the right FBG set for our WDM band we optimized forward and backward pump powers. This time we extended the range and simulated 0.7–1.4 W forward pump power. Backward pump power was stable for all possible configurations and oscillated within the 50 mW range around 2.3 W for the original and 1.8 W for the conjugated WDM grid.

By shifting our band by 1 THz we managed to improve average asymmetry by 0.5% achieving 5.9% with only three channels (CH1, CH2 and CH30) with figures above 10%. In this regime (asymmetry below 10%) we exceeded 35 nm bandwidth of the C band (1530–1565 nm) from ~4.3 THz to 5.4 THz (27 WDM channels with asymmetry below 10%, giving total 54 channels). As seen in Figure 11, the majority of channels (CH7–CH24) were below 5%, which is a very impressive result covering 3.4 THz.

**Figure 11.** WDM grid with the best average asymmetry of 5.9%. Forward pump power was set to be 1.1 W and 1.3 W and FBG to 1460 nm and 1450 nm for original and conjugated WDM grid, respectively.

In a single channel regime CH21 achieved asymmetry 2.1%, which is the lowest asymmetry in a 60 km span up to date (see Figure 12). This value most likely would be even lower if a single channel only would be simulated with the same configuration due to

the Raman pump depletion. Signal power variation in the original and conjugated channels is very low, below 2 dB, comparing to 12 dB when using EDFA.

**Figure 12.** Signal power profile of original (blue) and conjugated (red) channel with lowest asymmetry of 2.1% in a 60 km standard SMF span.

In Figure 13 we show OSNR and NPS results for an optimized 6 THz WDM grid (192–198 THz) for both original (CH1–CH30) and conjugated (CH31–CH60) channels in a 60 km span. We can notice small OSNR variation within 1 dB range between the best and the worst performing channels for both simulated grids, thanks to the combined gain from the first order pump at 1366 nm and lasing at the wavelength of the FBG. NPS variation was also very low across all but the first two channels; however, at these values of NPS it is insignificant.

**Figure 13.** OSNR (blue) and NPS (red) simulations for original (CH1–CH30) and conjugated (CH31–CH60) channels.


**5. Minimization of devices' energy consumption in UAV-assisted IoT network**

The devices at the cell edge consume high energy to achieve the required data rate when transmitting data to the nearest BS because of the large LoS distance between

*Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent… DOI: http://dx.doi.org/10.5772/intechopen.110312*

**Figure 8.** *Illustration of UAV-assisted IoT network.*

BSs and those devices. Alternatively, a quad-rotor UAV-assisted IoT network could provide reliable communication compared to fixed terrestrial BSs. Therefore, in this section, we aim to find the optimal trajectory of UAV and the association of IoT devices that simultaneously support energy-efficient data collection.

#### **5.1 System model**

**Figure 8** illustrates the UAV-assisted IoT network, in which *M* terrestrial BSs with fixed height of *HB* and a single UAV collect data from *K* stationary uniformly distributed IoT devices. The UAV flies at a fixed altitude *Hu* with the constant speed of *ϑu* where its start and end locations are represented by *US* ¼ *xs*, *ys* , *Hu* � � and *UE* ¼ *xe*, *ye*, *Hu* � � respectively. To track the UAV's location at each instance, we discretize its flight period into *N* equally spaced time slots, each of duration *Ts*, and assume that UAV's location at *n*th time slot *U n*½ �¼ ð Þ *x n*½ �, *y n*½ �, *Hu* , ∀*n*∈ N ¼ f g 1, 2, … , *N* is constant. All devices transmit atleast D*Min* bits data to the core network to maintain reliable QoS.

#### *5.1.1 Data collection of core network*

The transmission environment is categorized into two scenarios, i.e., ground to ground (G2G) and ground to air (G2A) channels. G2G channel establishes the links between BS and IoT devices, whereas G2A channel connects the IoT devices with the UAV platform. We generalize the wireless channel gain between each device and its destination (either UAV or BS) at each time slot as the combination of large-scale path loss and small-scale fading. The channel gain between each device and its destination can be modeled as [39]

$$h\_k^i[n] = \mathbf{g}\_k^i[n] \sqrt{L\_k^i[n]}, \forall k \in \mathcal{K} = \{1, 2, \dots, K\} \tag{51}$$

where *i* ∈f g *B* or *U* is the destination indicator in which *B* and *U* represent nearest BS and UAV, respectively, *Li k*½ � *n* is the large scale path loss, *gi k*½ � *n* is the small scale fading coefficient. The achievable instantaneous transmission rate of the *k*th IoT device can be formulated as [40]

$$R\_k^i[n] = C\_B \log\_2 \left[ 1 + \frac{\left| h\_k^i[n] \right|^2 P\_k^i[n]}{\eta\_0} \right] \tag{52}$$

where *CB* is channel bandwidth, *Pi k*½ � *n* is transmitting power of the *k*th device, and *η*0 is noise power. Instantaneous data transmitted by the *k*th device over G2G and G2A channel is measured as D*B k* ½ �¼ *n RB k* ½ � *n Ts* and D*U k* ½ �¼ *n RU k* ½ � *n Ts* respectively. The energy consumption of device *k* at *n*th time slot can be calculated as

$$E\_k[n] = \left(I\_k^U[n]P\_k^U[n] + I\_k^B[n]P\_k^B[n]\right)T\_s, \forall k \in \mathcal{K} \tag{53}$$

where *PU k* ½ � *n* and *PB k* ½ � *n* are the instantaneous transmit powers of *k*th device when connecting with UAV and BS, respectively and *I U k* ½ � *n* ,*I B k* ½ � *n* ∈f g 0, 1 are the binary device association indicators with UAV and BS respectively. The *k* th device transmits data to the core network during each time slot is measured as

$$\mathcal{D}\_{k}[n] = I\_{k}^{U}[n]\mathcal{D}\_{k}^{U}[n] + I\_{k}^{B}[n]\mathcal{D}\_{k}^{B}[n], \forall k \in \mathcal{K}, n \in \mathcal{N} \tag{54}$$

## *5.1.2 Problem formulation*

We aim for energy-efficient data collection that jointly exploit reliable data transmission, optimal instantaneous position of UAV and transmit power control. The fluctuation of channel gain causes unstable network performance, leading to quickly drain out devices' on-board battery energy. Thus, to minimize total energy consumption of all devices we jointly optimize UAV' trajectory, device association indicators and their transmit power allocation, while ensuring that each device should transmit a minimum data to the destination and UAV chooses a constant speed during its trajectory between the initial and final locations. Therefore the optimization problem is formulated as

$$\begin{array}{c} \mathbf{P1}: \begin{array}{c} \text{Minimize} \\ \{ (\mathbf{x}[n], \mathbf{y}[n]) \}, I\_{k}^{U}[n], I\_{k}^{B}[n], P\_{k}^{U}[n], \text{and } P\_{k}^{B}[n] \end{array} \end{array} \sum\_{n=1}^{N} \sum\_{k=1}^{K} \left[ \left( I\_{k}^{u}[n]P\_{k}^{u}[n] + I\_{k}^{B}[n]P\_{k}^{B}[n] \right) T\_{s} \right] \end{array} \tag{55}$$

Subject to the constraints

$$\mathbf{C1}: I\_k^U[n] \mathcal{D}\_k^U[n] + I\_k^B[n] \mathcal{D}\_k^B[n] \ge \mathcal{D}\_{\text{Min}}, \forall k \in \mathcal{K}, n \in \mathcal{N} \tag{56}$$

$$\mathbb{C}\mathbb{C}: I\_k^U[n] \in \{0, 1\}, I\_k^B[n] \in \{0, 1\}, \forall k \in \mathbb{K}, n \in \mathcal{N} \tag{57}$$

$$\mathsf{CSC}: I\_k^U[n] + I\_k^B[n] \le \mathbf{1}, \forall k \in \mathcal{K}, n \in \mathcal{N} \tag{58}$$

$$\mathsf{C4}: \sum\_{k=1}^{K} I\_k^U[n] \le K, \forall n \in \mathcal{N} \tag{59}$$

$$\mathbf{C}\mathbf{S}: U[\mathbf{1}] = U\_{\mathbf{S}}, U[\mathbf{N}] = U\_{E} \tag{60}$$

Here, C1 ensures that each device transmits atleast D*Min* bits data to either UAV or nearest BS at a time slot. C2 defines the device association indicators. C3 verifies that each device associates with either UAV or the nearest BS at each time slot. C4 implies that UAV can associate with maximum *K* number of devices instantaneously; and C5 guarantees that UAV starts its trajectory from an initial given position and ends to the final predefined location. The optimization problem contains multiple interactive and coupled variables, and they have a complex relationship by which changing one's value may impact to others. Furthermore, these discrete optimizing variables make the problem highly non-convex to find a limited time trajectory between the start and end points. *Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent… DOI: http://dx.doi.org/10.5772/intechopen.110312*

Hence, standard optimization methods face difficulties in obtaining exact solutions. In order to tackle this situation, we propose RL framework and adaptive decision-making policy to find UAV's successive locations, and device association along with their transmit power allocation. We adopt the SARSA algorithm to control the UAV, which acts as an RL-agent for taking the optimal action at each step to maximize its reward.

#### **5.2 Reinforcement learning based on SARSA algorithm**

As discussed earlier in Section 4.3, the RL framework follows MDP, where the current state only depends on the immediate past state, and the UAV acting as RL agent chooses an action according to the *ϵ*-greedy policy. Here, the generated reward depends on UAV's current state and taken action at each time slot. The expected trajectory is obtained more precisely when the reward generated by the UAV at the current time slot is beneficial for the long term. To reflect this property, we model the instantaneous reward for every time slot as UAV's instantaneous objective value, which is expressed as

$$\mathcal{R}(\mathfrak{s}[n], \mathfrak{a}[n]) = \left[ \sum\_{k=1}^{K} \left( I\_k^U[n] P\_k^U[n] + I\_k^B[n] P\_k^B[n] \right) T\_s \right]^{-1} \tag{61}$$

Algorithm 2 summarizes the optimal trajectory learning procedure using the improved SARSA technique. In this framework, we first calculate UAV's current state, channel gain, and distances from all devices to UAV and the nearest BS at every time slot. Then, all devices select the destination (either UAV or nearest BS) by estimating the instantaneous device association indicator and the required transmit power while satisfying the data rate constraint value. This process is repeated at each step, and UAV obtains optimal policy at the final episode. Since the number of episodes is *T* and each episode goes through *N* time slots, the computation complexity depends on total steps *TN*, including state space and action space in RL. In our scenario, there are *L*1*L*2 possible state locations and eight possible actions for each time slot. Therefore, the computational complexity of algorithm 1 is Oð Þ 8*TNL*1*L*2 , including the complexity of the action selection scheme in each step.

12: **else if** *n* ¼ *N* � 1 and ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð Þ *xe* � *xu*½ � *n* 2 þ *ye* � *yu*½ � *n* � �2 q ≤*ϑuTs* **then** 13: Obtain the next state as *s*½ �¼ *n* þ 1 *xe*, *ye* � � 14: Calculate reward Rð Þ *s*½ � *n* ,*a*½ � *n* by (61) 15: Choose the next action *a*½ �¼ *n* þ 1 *δu x*½ � *n* þ 1 , *δu y* ½ � *n* þ 1 n o by (50) and obtain *Q*ð Þ *s*½ � *n* þ 1 ,*a*½ � *n* þ 1 value 16: Update *Q*ð Þ *s*½ � *n* ,*a*½ � *n* value according to (49) 17: Update the respective state and action as *s*½ � *n s*½ � *n* þ 1 and *a*½ � *n a*½ � *n* þ 1 18: **else** 19: **Break** 20: Find an optimal policy as *π* ∗ *h* ¼ *arg* max *a*½ �¼*n δu x* ½ � *n* , *δu y* f g ½ � *n Q*ð Þ *s*½ � *n* ,*a*½ � *n* , ∀*s*½ � *n* ∈S,*a*½ � *n* ∈ A, *n*∈ N

#### **5.3 Simulation results**

This sub-section presents the training outcomes corresponding to the proposed SARSA algorithm for optimal trajectory and subsequently evaluates the energyefficient data collection. Here, we compare the effectiveness and superiority of the proposed design with the benchmark PSO technique [41], where 100 IoT devices are uniformly distributed within a square field of size 2000 � 2000 m. Moreover, we adopt the required simulation parameters from [40] and [24] to implement the proposed algorithm.

#### *5.3.1 Convergence analysis*

The agents' training evaluations using RL-based SARSA algorithm are illustrated in **Figure 9a**, when all IoT devices maintain the data rate constraint of 10 Mbps. In this figure, we have found that the convergence rate varies for flying time because UAV explores the target area more efficiently with the available time slots. As a result more devices associate with UAV and the convergence occurs before 10,000 episodes.

**Figure 9b** shows the episode-wise objective value evaluation using PSO algorithm. From this figure, it is visible that PSO takes more time to converge, and its final convergence value is less than the SARSA algorithm. This is because PSO updates particles' position and velocity according to the random inertial weight which causes less exact regulation of particles' moving directions and speed. Hence, its

#### **Figure 9.**

*Training results corresponding to the proposed and benchmark algorithms. (a) Cumulative reward generated by proposed SARSA. (b) Fitness value generated by benchmark PSO.*

*Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent… DOI: http://dx.doi.org/10.5772/intechopen.110312*

#### **Figure 10.**

*Optimal trajectories corresponding to the proposed and benchmark algorithms. (a) Optimal UAV trajectory using SARSA. (b) Optimal UAV trajectory using PSO.*

computational complexity increase due to the high dimensions of decision variables. Therefore, the proposed SARSA algorithm improves the cumulative reward by 10.26% with respect to the PSO.

#### *5.3.2 Optimal trajectory*

Using the same parameters mentioned in **Figure 9**, UAV finds its optimal trajectories with the help of SARSA and PSO algorithms, depicted in **Figure 10**. These figures indicate that UAV moves toward the devices, far away from the BS, and within the flight period, it reaches the final destination point. Since devices consume more energy while transmitting data to BS, UAV fly toward those devices to improve their channel conditions. as we mentioned earlier, device association with UAV increases with the flying time, more devices transmit their data to the UAV instead of BS, reducing their energy consumption.

#### *5.3.3 Performance comparison of proposed SARSA with benchmark PSO*

The variation of devices' average transmit power to achieve 10 Mbps data rate with the index value is demonstrated in **Figure 11a** where a device's index indicates its

#### **Figure 11.**

*Performance comparison of the proposed and benchmark algorithms. (a) Devices' transmit power corresponding to their index value. (b) Devices' energy consumption versus data rate constraint.*

distance from the nearest BS. It is observed that, when there is no UAV support, average transmit power increases with the index value because, according to (52) devices far away from BS utilize more power to obtain the given data rate. But when UAV is employed, its optimal trajectory focuses the devices which are consuming more power and associates with them for data collection. Furthermore, since UAV's straight trajectory cannot improve all devices' channel conditions, the corresponding energy-efficient data collection would not be possible.

The total energy consumption of all devices for various data rate constraint values is illustrated in **Figure 11b**. It is clear that devices' energy consumption increases with data rate constraint because, according to (49), devices allocate more power to achieve the given rate constraint. Furthermore, from **Figure 11a**, UAV's optimal trajectory corresponding to the proposed SARSA algorithm reduces devices' transmit power with its available flying time as compared to PSO algorithm, because PSO achieves low convergence rate in an iterative process and could not identify the local optimal in high-dimension space. Hence, the proposed SARSA methodology significantly reduces the total energy consumption of all devices by 8.15%, 7.72%, and 5.67% for UAV's flying time of 80, 100, and 120 timeslots, respectively as compared to PSO.


**3.6 Cerium oxide nanoparticles (CeO2 NPs)**

Cerium oxide NPs could help *Sorghum bicolor* to cope with drought stress by inducing oxidative stress by catalytically neutralizing ROS and maintaining the photosynthetic activity and grain yield [80]. The foliar spray of nanoceria dramatically decreased free radicals (H2O2 and superoxide) under drought conditions, for instance, by 36 and 41%, respectively. ROS-scavenging enzymes were shown to be more active in plants exposed to drought: POD (54%), SOD (94%), and CAT (117%). The rate of pollen germination, carbon absorption, and seed output per plant was improved by 38, 31, and 31%, respectively. Different reports summarizing the effects of a number of nanoparticles on different plant species and their physiological responses under drought stress are discussed in **Table 2**.


*2.2.4 Other high-entropy ceramics—silicide, borocarbide, sulfide, etc.*

To date, the work of bulk high-entropy ceramics is mostly focused on oxide, boride, and carbide, and several other classes of high-entropy materials like silicide, sulfide, and borocarbide have been reported.

**163**

*High-Entropy Ceramics*

at 773 K was obtained.

ultrahigh hardness of 35 GPa.

**3.1 Preparation methods**

*3.1.1 Sputtering*

**3. High-entropy ceramic coatings**

(target) is bombarded with accelerated charged ions (Ar<sup>+</sup>

*DOI: http://dx.doi.org/10.5772/intechopen.89527*

The synthesis of high-entropy silicide was reported by Gild et al. [26] and Qin

By adding B4C into the four-component HEC (HfMoTaTi)C, Zhang et al. [29] studied the capability of a high-entropy carbide system on accommodating one more nonmetal element, boron. The quaternary (HfMoTaTi)C contains FCC structures, while the addition of B4C induced the formation of a small fraction of hexagonal phase. Similar with the diffusion process reported by Castle et al. [19], TaC is suggested to act as the host lattice in the borocarbide system. The effect of employing different particle sizes for host carbide TaC and other constituent carbides on the phase formation and mechanical properties of the HEC composites was discussed. A high-entropy B4(HfMo2TaTi)C ceramic exhibiting hexagonal structure, with alternating metal and nonmetal layers in the lattice, was found when SiC whiskers are introduced to the borocarbide system [30]. The hexagonal HEC solid solution shows great improvement of the mechanical property with an

Sputtering is a physical vapor deposition (PVD) technique where a solid surface

scattering of surface atoms of solid. Sputtering technique is used to deposit thin coatings. There are several sputtering systems employed for deposition of thin coatings, such as direct current (DC) magnetron sputtering and radio frequency (rf) sputtering being the widely used techniques. DC-magnetron sputtering is a low pressure sputtering system for metal deposition and electrically conductive target coating materials. The magnetic field from magnetron lowers the sputtering gas pressure and increases the deposition rate of sputtered coatings. On the other hand, when metallic target is replaced by an insulator target, the plasma discharge cannot be maintained due to buildup of surface charge of positive ions on the target. Thus, an rf power supply is used instead of dc voltage power supply to sustain the glow discharge on an insulator target [31]. Another advantage of using sputtering for thin coating deposition is the use of reactive gases, substrate bias, and substrate temperature to vary the composition and properties of deposited coatings. In reactive sputtering, different reactive gases, such as oxygen, nitrogen, and CH4 can be used to deposit ceramic coatings of oxides, nitrides, and carbides, respectively [32–34]. Furthermore, deposition of carbonitride and oxynitride coatings can be

) resulting into backward

et al. [27]. Gild utilized SPS and sintered high-entropy silicide (Mo0.2Nb0.2 Ta0.2Ti0.2W0.2)Si2 at 1923 K, resulting in a single hexagonal C40 crystal structure (space group P6222). Hardness enhancement over the constituent silicide and low thermal conductivity similar to high-entropy carbide was reported. On the other hand, Qin produced (Ti0.2Zr0.2Nb0.2Mo0.2W0.2) Si2 from compositional elemental powders, leading to the formation of a hexagonal structure highentropy silicide, with the same space group P6222. Zhang et al. [28] developed a data-driven model to design high-entropy sulfides for thermoelectric applications. Compositions Cu5SnMgGeZnS9 and Cu3SnMgInZnS7 were picked to be synthesized via high energy ball milling and SPS. Single-phase high-entropy sulfide with homogeneous distribution of the elements is reported. By increasing the Sn content in the Cu5SnMgGeZnS9 system, a figure of merit zT value of 0.58

#### *High-Entropy Ceramics DOI: http://dx.doi.org/10.5772/intechopen.89527*

*Engineering Steels and High Entropy-Alloys*

**162**

solid solution via SPS.

**Figure 3.**

*(a) The energy distribution spectra of different configurations and the corresponding EFA value of the nine five-metal carbides. (b) The X-ray patterns of the sintered carbides with the same compositions, by Sarker*

crystal structures was commonly understood to impede the formation of the highentropy phase because of the mismatch of lattice structure. It was observed that the addition of group VI elements (Cr, Mo, and W) is likely to reduce the chance of forming a single phase, as group VI metal monocarbides are generally demonstrated as non-cubic structure at room temperature. However, the MoNbTaVWC5 composition that simultaneously contains tungsten carbide (W2C) and molybdenum carbide (Mo2C), which exhibit orthorhombic and hexagonal structure, respectively, shows the highest EFA value among the 56 analyzed compositions and demonstrates single-phase FCC structure after SPS. The mechanical properties measured by experiments showed that the Vickers hardness and elastic modulus have significant enhancement compared to the predicted value from the rule of mixture, contributed by mass disorder in the structure and solid solution hardening. Following the work of the pioneers, a systematic study focused on the phase stability and mechanical properties of high-entropy carbides synthesized from group IV, V, and VI metal carbides [25] was carried out. It was verified that carbide system that was stated to have EFA value lower than 45 cannot form single-phase

*et al. [23] (licensed under CC BY 4.0, https://doi.org/10.1038/s41467-018-07160-7).*

*2.2.4 Other high-entropy ceramics—silicide, borocarbide, sulfide, etc.*

sulfide, and borocarbide have been reported.

To date, the work of bulk high-entropy ceramics is mostly focused on oxide, boride, and carbide, and several other classes of high-entropy materials like silicide,

The synthesis of high-entropy silicide was reported by Gild et al. [26] and Qin et al. [27]. Gild utilized SPS and sintered high-entropy silicide (Mo0.2Nb0.2 Ta0.2Ti0.2W0.2)Si2 at 1923 K, resulting in a single hexagonal C40 crystal structure (space group P6222). Hardness enhancement over the constituent silicide and low thermal conductivity similar to high-entropy carbide was reported. On the other hand, Qin produced (Ti0.2Zr0.2Nb0.2Mo0.2W0.2) Si2 from compositional elemental powders, leading to the formation of a hexagonal structure highentropy silicide, with the same space group P6222. Zhang et al. [28] developed a data-driven model to design high-entropy sulfides for thermoelectric applications. Compositions Cu5SnMgGeZnS9 and Cu3SnMgInZnS7 were picked to be synthesized via high energy ball milling and SPS. Single-phase high-entropy sulfide with homogeneous distribution of the elements is reported. By increasing the Sn content in the Cu5SnMgGeZnS9 system, a figure of merit zT value of 0.58 at 773 K was obtained.

By adding B4C into the four-component HEC (HfMoTaTi)C, Zhang et al. [29] studied the capability of a high-entropy carbide system on accommodating one more nonmetal element, boron. The quaternary (HfMoTaTi)C contains FCC structures, while the addition of B4C induced the formation of a small fraction of hexagonal phase. Similar with the diffusion process reported by Castle et al. [19], TaC is suggested to act as the host lattice in the borocarbide system. The effect of employing different particle sizes for host carbide TaC and other constituent carbides on the phase formation and mechanical properties of the HEC composites was discussed. A high-entropy B4(HfMo2TaTi)C ceramic exhibiting hexagonal structure, with alternating metal and nonmetal layers in the lattice, was found when SiC whiskers are introduced to the borocarbide system [30]. The hexagonal HEC solid solution shows great improvement of the mechanical property with an ultrahigh hardness of 35 GPa.


**7. Clinical implications of dual-process framework of social cognition and consciousness**

Given the proposed framework of dual-process social cognition [6] and access in the global workspace [33, 34, 38], we attempt to explain abnormal social cognition in clinical disorders. We demonstrate how our framework can bring new light into understanding selected examples of clinical disorders in which there are deficits in mindreading and metacognition. Here, we show examples of clinical disorders such as borderline personality and schizophrenia.

A vivid of example showing abnormality in conscious access may be presented by patients with borderline personality disorder. Individuals diagnosed with BDP characterize instability of emotional and behavioral reactions as well as unstable relationships with others [49]. It has been argued that BDP symptoms arise from deficits in perceiving and interpreting social signals [50–52]. Several researchers believe that patients with BPD have difficulties in their ability to correctly ascribing mental states to oneself and recognizing others' mental states; therefore, BPD is considered to be a metarepresentation disorder [53]. It is believed that a lack of longterm bonds with others may be due to the difficulty in maintaining a stable representation of others' mind and one's own mind [54]. Moreover, Semerari and colleagues [53] point out that impairments in BPD in reflecting one's own thoughts and emotions are of selective nature, since they are mainly associated with integrating representations of self and others in consciousness. In particular, these researchers have video-taped four patients suffering from BDP and then evaluated clinical outcomes of therapeutic intervention over the first year of their therapy with the Metacognition Assessment Scale [55]. The MAS focuses on measuring basic metacognitive functions such as monitoring, integration, and differentiation [55]. Here, we focus on monitoring dimension associated with the ability to identify one's inner states, and other functions of metacognition linked with integration defined as the "ability reflect in different mental states and or contents giving them an order and hierarchical relevance" [55]. The study clearly has demonstrated that four patients had the ability to identify their own internal states, although their integration functions aimed at organizing metacognitive representations of self and others were impaired. Following our theoretical framework of dual-process cognition and consciousness, this abnormal pattern of the metarepresentative functions in patients with BPD may indicate that the low-level structures of control and evaluation are preserved; however, there may be difficulties in consciously accessing higher-level structures containing available psychological knowledge. Since conscious access should endorse integrating nature of the metacognition-mindreading relation, its disturbances can lead to abnormal information flow between both subsystems [3]. Here, one can interpret this situation as results of abnormal regulation in accessing the information content that goes in the "from-low-to-highlevel" direction. Therefore, low-level processes of evaluation and monitoring work properly, but the further attribution of psychological contents fails. Thus, our theoretical proposal is that patients with BPD may have disturbances in conscious access that affects activation of interpretative data from metacognition and mindreading to establish proper social behavior.

mindreading module [46]. Interestingly, functional neuroimaging studies of clinical population have demonstrated that mindreading deficits are associated with decreasing activation within the medial prefrontal cortex [47]. Some other fMRI study on mindreading deficits in patients with schizophrenia also shows abnormal activation within the left medial prefrontal cortex [48]. Obviously, the actual organization of brain circuity resulting from the proposed framework of linking the dual-process social cognition and the global workspace is considerably more complicated. However, such simplified cognitive architecture of conscious social cognition can allow us to understand how normal and abnormal behavioral and neuronal

Given the proposed framework of dual-process social cognition [6] and access in the global workspace [33, 34, 38], we attempt to explain abnormal social cognition in clinical disorders. We demonstrate how our framework can bring new light into understanding selected examples of clinical disorders in which there are deficits in mindreading and metacognition. Here, we show examples of clinical disorders such as borderline personality and schizophrenia.

A vivid of example showing abnormality in conscious access may be presented by patients with borderline personality disorder. Individuals diagnosed with BDP characterize instability of emotional and behavioral reactions as well as unstable relationships with others [49]. It has been argued that BDP symptoms arise from deficits in perceiving and interpreting social signals [50–52]. Several researchers believe that patients with BPD have difficulties in their ability to correctly ascribing mental states to oneself and recognizing others' mental states; therefore, BPD is considered to be a metarepresentation disorder [53]. It is believed that a lack of longterm bonds with others may be due to the difficulty in maintaining a stable representation of others' mind and one's own mind [54]. Moreover, Semerari and colleagues [53] point out that impairments in BPD in reflecting one's own thoughts and emotions are of selective nature, since they are mainly associated with integrating representations of self and others in consciousness. In particular, these researchers have video-taped four patients suffering from BDP and then evaluated clinical outcomes of therapeutic intervention over the first year of their therapy with the Metacognition Assessment Scale [55]. The MAS focuses on measuring basic metacognitive functions such as monitoring, integration, and differentiation [55]. Here, we focus on monitoring dimension associated with the ability to identify one's inner states, and other functions of metacognition linked with integration defined as the "ability reflect in different mental states and or contents giving them an order and hierarchical relevance" [55]. The study clearly has demonstrated that four patients had the ability to identify their own internal states, although their integration functions aimed at organizing metacognitive representations of self and others were impaired. Following our theoretical framework of dual-process cognition and consciousness, this abnormal pattern of the metarepresentative functions in patients with BPD may indicate that the low-level structures of control and evaluation are preserved; however, there may be difficulties in consciously accessing higher-level structures containing

patterns that accompany conscious processes in social cognition can be developed.

**7. Clinical implications of dual-process framework of social**

**cognition and consciousness**

112 Prefrontal Cortex

Now, turning to the domain of schizophrenia, we attempt to present how development of clinical symptoms in schizophrenic patients suffering from persecutory delusions can be understood within our proposed framework. With reference to delusional beliefs in schizophrenia, cognitive theories suggest that persecutory delusions often emerge as misinterpretation of social interactions [56]. Therefore, individuals with persecutory delusions are preoccupied with intensions to others [57]. Thus, psychotic patients fail to make accurate judgments in relation to their experiences attributed to others. It has been also suggested that delusional impairments in inferences on the social data may arise from the mindreading deficits. Apparently, deficits in mindreading are demonstrable in schizophrenia as indicated by the meta-analysis by Sprong et al. [21]. Frith [58] hypothesizes that mindreading skills in people with persecutory delusions develop normally; however, those theories of mind capacities are "lost" during psychotic episodes. There is also substantial evidence for mentalizing deficits in patients with first-order episode schizophrenia in the early course of schizophrenia [59]. In fact, mentalizing skills have been shown empirically to be impaired in psychotic patients with persecutory delusions. Patients that follow a paranoid subtype of schizophrenia perform poorly on a wide range of the "theory of mind" tasks including those exercising the attribution of intentions [60]. Moore et al. [61] have explored cognitive etiology of persecutory delusions in patients with late onset of schizophrenia and found that patients performed poorly in a deception task by making more mentalizing errors as compared to healthy participants.

Interestingly, theoretical developments in the conceptualization of relation between metacognition and mindreading skills underlie an interesting casual formation of persecutory delusions. As we mentioned above, the Carruthers' account [4] views metacognition as beliefs of our own attitudes that arise from turning mindreading capacities on ourselves. This implicates that mindreading deficits are prior leading in consequences to dysfunctional metacognition capacities. On the other hand, another explanation is possible as at least deficits in both capacities are paired and may be explained by the interactionist view on social cognition [6]. Indeed, an empirical study by Köther and collaborators [62] on schizophrenic patients could support such interactionist view on persecutory delusions as results of mindreading deficits accompanied with relevant dysfunctional metacognition capacity. In particular, the researchers by employing Reading the Mind in the Eyes test (Eyes Test; [63]) with an additional confidence measure showed in schizophrenic patients not only impaired social cognition in terms of perceiving emotional and social cues but had also commitments to make more high-confidence errors and at the same time made fewer high-confidence correct responses. Obviously, this raises questions about specificity of delusions that may be due to the failures of mentalizing skills and subsequently failures in lower-level metacognition that mirrors mindreading deficits in delusional-prone individuals. Following our framework on social cognition and consciousness, it is likely that unconscious interpretative contents from the higher-level structures are not properly broadcasted to other systems via global workspace in order to get proper monitoring and evaluations from the low-level structures. Moreover, because the information contents remain unconscious, correction of faulty beliefs and interpretations is not possible.

and propositional nature are broadcasted globally within a cognitive system. According to Baars' theory [33, 38], we predict that consciousness mobilizes two levels of processing in "from-low-to-high" or "from-high-to-low" directions depending on a psychological contexts (related to self or other people) and integrates metacognitive and mindreading functions in such a way that interpretative contents are globally available for other specialized areas of the brain. Thus, the combined framework of dual-process social cognition and global workspace explains the basic processes that may potentially underlie normal and abnormal regulation of social behavior. This account suggests that global broadcasting enables corrective interpretation in case of detecting erroneous information about themselves and false assessments of others' behaviors, intentions, etc. We believe that this framework offers a useful cognitive perspective for better understanding of clinical disorders characterized by abnormal relations between mindreading and metacognition and elaborating their therapeutic interventions.

Consciousness and Social Cognition from an Interactionist Perspective: A New Approach…

http://dx.doi.org/10.5772/intechopen.79584

115

In fact, psychological and psychiatric research clearly shows (see [12]) that people in terms of social interactions need in a continuous way to adjust their behavior and regulate distress, implicating in this fashion a great need for establishing psychological interventions aimed at improving their metacognitive and mentalizing capabilities. Our paper clearly addresses this need by providing more comprehensive cognitive explanations of complex mental health problem as well as offering a new path toward understanding symptom expression of metacognitive and mindreading disturbances. The vital point in this paper is to demonstrate that phenomenology of social interactions as well as symptom expression in severe psychological and mental disorders can be described by adapting an interactionist approach that combines access consciousness mechanisms with mental capacities of mindreading and metacognition in a complex unit. According to this view, social cognition can be driven by a set of independent mental functions in the brain that interact with each other [12, 24, 25], and as a matter of fact each element underlying this interaction may be a subject of selective impairment. On the other hand, the interactionist model undoubtedly maximizes a role of individual within the process of psychotherapeutic intervention by emphasizing his/her conscious efforts. For instance, given the interactionist view on global access and both forms of mentalization, we can also efficiently explain the way the therapy based on metacognitive training provides promising results in treating severe psychiatric disorders [27]. Indeed, the interactionist approach suggests that the individual can consciously correct his/her theories and interpretations about oneself and others, since access consciousness mediates the effective interaction between the sets of independent mental capacities. Moreover, interactionist perspective assumes that consciousness mechanisms by its mediating and integrating functions improve social cognition including metacognitive and mentalizing skills. Therefore, future studies should seek for effects of consciousness on improving both metacognition and mindreading

This research has been supported by the National Science Centre (Poland) and funded under

skills either in clinical or in nonclinical population samples.

the award number 2014/15/B/HS6/03834 to RS.

**Acknowledgements**

Interestingly, disturbances in conscious access presented in schizophrenia seem to be confirmed by studies on metacognitive and mindreading malfunctioning by Lysaker and colleagues [64]. Researchers have investigated the impairments within self-generated personal narratives in terms of perceiving one's own state and mental states of others in adults with schizophrenia spectrum disorders. The researchers by using the MAS measure (MAS measure; [55]) have identified three groups of patients: the first one with impaired metacognition ("minimal reflectivity") and poor mindreading, the second group with intact basic self-reflectivity and poor mindreading, and the third group characterized with intact self-reflection and mindreading ability in terms of attributing thoughts and emotions to other people. It turned out that individuals with impaired metacognition and mindreading facilities performed worst in recognizing negative affective cues in others' faces and voices in the video-typed material. According to Dimaggio and colleagues [8], these findings could fit into the concept of the simulation theory (see above) proposed by Gallese and Goldman [65]. Thus, these findings can support the idea of priority of having direct access to one's own internal states to further involve simulation and inferences to interpret and understand others' mental states. Since the study by Lysaker and colleagues [64] was of the correlation nature, alternative interpretations cannot be ruled out. In particular, the study showed that performance results in emotion recognition task were positively correlated with general metacognitive functions (*r* = 0.44) assessed with the "Understanding one's own mind" subscale and to some extent with mindreading capacity (*r* = 0.26) as indicated by scores on the "Decentration" dimension (the ability to perceive others as having their own emotions, thoughts, and perspectives) [64]. These results may have also implication to the idea of dual-process model of social cognition and global workspace. Clearly, better performance in recognizing affective cues in other's faces and voices was linked with activation of both higher- and lower-level structures. Subsequently, the MAS measure "Understanding one's own mind" subscale including also metacognitive function of integration could indicate that low-level metacognitive functions in emotion recognition were supported by conscious access to some extent.

### **8. Conclusions**

In our opinion, it becomes clear that efficient interactions between metacognition and mindreading capacities should be supported by access consciousness. The GWT framework by Baars [33, 38] indicates that consciousness can mediate the interaction between metacognition and mindreading subsystems by managing the access to interpretative contents from both mental faculties. Since the GWT assumes the interaction of unconscious and conscious processes, it becomes crucial how and under what conditions interpretative data of a mentalizing and propositional nature are broadcasted globally within a cognitive system. According to Baars' theory [33, 38], we predict that consciousness mobilizes two levels of processing in "from-low-to-high" or "from-high-to-low" directions depending on a psychological contexts (related to self or other people) and integrates metacognitive and mindreading functions in such a way that interpretative contents are globally available for other specialized areas of the brain. Thus, the combined framework of dual-process social cognition and global workspace explains the basic processes that may potentially underlie normal and abnormal regulation of social behavior. This account suggests that global broadcasting enables corrective interpretation in case of detecting erroneous information about themselves and false assessments of others' behaviors, intentions, etc. We believe that this framework offers a useful cognitive perspective for better understanding of clinical disorders characterized by abnormal relations between mindreading and metacognition and elaborating their therapeutic interventions.

In fact, psychological and psychiatric research clearly shows (see [12]) that people in terms of social interactions need in a continuous way to adjust their behavior and regulate distress, implicating in this fashion a great need for establishing psychological interventions aimed at improving their metacognitive and mentalizing capabilities. Our paper clearly addresses this need by providing more comprehensive cognitive explanations of complex mental health problem as well as offering a new path toward understanding symptom expression of metacognitive and mindreading disturbances. The vital point in this paper is to demonstrate that phenomenology of social interactions as well as symptom expression in severe psychological and mental disorders can be described by adapting an interactionist approach that combines access consciousness mechanisms with mental capacities of mindreading and metacognition in a complex unit. According to this view, social cognition can be driven by a set of independent mental functions in the brain that interact with each other [12, 24, 25], and as a matter of fact each element underlying this interaction may be a subject of selective impairment. On the other hand, the interactionist model undoubtedly maximizes a role of individual within the process of psychotherapeutic intervention by emphasizing his/her conscious efforts. For instance, given the interactionist view on global access and both forms of mentalization, we can also efficiently explain the way the therapy based on metacognitive training provides promising results in treating severe psychiatric disorders [27]. Indeed, the interactionist approach suggests that the individual can consciously correct his/her theories and interpretations about oneself and others, since access consciousness mediates the effective interaction between the sets of independent mental capacities. Moreover, interactionist perspective assumes that consciousness mechanisms by its mediating and integrating functions improve social cognition including metacognitive and mentalizing skills. Therefore, future studies should seek for effects of consciousness on improving both metacognition and mindreading skills either in clinical or in nonclinical population samples.


**1. Introduction**

The daily light integral (DLI; light intensity x photoperiod duration) plays a vital role in plant biomass accumulation and yield. While the natural solar DLI is dictated by time of year, global location, and local weather, the DLI can be augmented by the introduction of supplemental lighting. Supplemental lighting can aid in the achievement of a desired/target DLI to increase plant growth and yield, specifically during low-light months [1]. The use of an extended photoperiod with supplemental light at a lower light intensity can have economic benefits by reducing the overall fixture need (i.e., capital cost) and by using electricity during the night, when electrical costs are low [2]. Furthermore, most of the input electricity in light fixtures is eventually converted into heat because plants only convert a small percentage of light into biomass. By utilizing LEDs during the subjective night period, the heat released from light fixtures can help to meet nighttime

**Citation:** Lanoue, J.; Thibodeau, A.; Little, C.; Zheng, J.; Grodzinski, B.; Hao, X. Light Spectra and Root Stocks Affect Response of Greenhouse Tomatoes to Long Photoperiod of Supplemental Lighting. *Plants* **2021**, *10*, 1674. https://doi.org/10.3390/ plants10081674

Academic Editors: Valeria Cavallaro and Rosario Muleo

Received: 15 July 2021 Accepted: 12 August 2021 Published: 14 August 2021

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

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

heating requirements. However, exceeding the tolerable limits of photoperiods, which are species-specific, can lead to diminished yield, photoperiod-related leaf injury, and an economic disadvantage for growers [3]. For tomatoes, photoperiods up to 17 h are associated with normal growth patterns [4]. Photoperiods beyond 17 h, which do not employ a drastic temperature dip, spectral change, or decrease in light intensity, have been shown to cause photoperiod-related injury characterized by leaf chlorosis, photosynthetic inhibition, and yield decrease [5–7]. However, prolonged photoperiods (>18 h) can theoretically lead to increased plant biomass and yield due to the added light available for photosynthesis, if photoperiod-related injury is not induced [8].

The underlying mechanism involved in photoperiod-related injury has yet to be determined. The *type III light harvesting chlorophyll a/b binding protein 13* (*CAB-13*) gene has been demonstrated to play an important role in photoperiod-related injury [9]. As the name suggests, *CAB-13* plays an important role in the light harvesting/photosystem II (PSII) super complex [10]. When plants were grown under continuous light (CL, 24 h), *CAB-13* expression was downregulated, leading to photoinhibition and photoperiod-related injury characterized by a decrease in the maximum quantum efficiency of PSII (Fv/Fm) [9]. Velez-Ramirez et al. [9] also suggest that photoperiod-related injury may be due to the unbalanced excitation between photosystem I (PSI) and PSII. Furthermore, Haque et al. [6] hypothesized that continuous lighting could cause damage to PSII, which ultimately reduces photosynthesis and yield. It has also been hypothesized that the restoration of proper leaf photochemistry, potentially through improved expression of genes such as *CAB-13*, as well as balancing source/sink strength may alleviate injury under extended photoperiods [6,9,11,12]. From this, we have deduced that the utilization of different spectral compositions during an extended photoperiod may play a key role in avoiding or lessening photoperiod-related leaf injury.

The ability to regulate gene expression can largely be traced back to the role of photoreceptors such as phytochrome and cryptochrome [13]. With the advancements in light-emitting diode (LED) technology, the interaction between photoperiod length and spectral composition has become of interest in optimizing growth conditions for high-value crops. With red and blue LEDs being the most efficient and these wavelengths being primarily absorbed by phytochrome and cryptochrome, respectively, much research relating to photoperiods and spectra has focused around these wavelengths [7,9,14]. Matsuda et al. [14] indicated that photoperiod-related injury was less severe when tomato seedlings were exposed to red or orange LEDs compared to blue or white during the subjective night period when grown under CL. Moreover, Velez-Ramirez et al. [15] determined that phytochrome A (PHY A) plays an important role in photoperiod-related injury. Together, these studies indicate that light spectral compositions/quality may play an important role in reducing tomato injury under extended photoperiods. However, both Matsuda et al. [14] and Velez-Ramirez et al. [15] performed experiments using controlled environment growth chambers, which would exclude any effect that the natural solar radiation would have. Furthermore, using such chambers would not facilitate adequate growth space for plants to reach maturity (both studies only used young plants up to the first flower stage) and thus did not allow for the assessment of yield during prolonged photoperiods.

Exposing tomatoes to extended photoperiods tends to lead to smaller leaf area [9,16]. It has been stated that even if tomatoes were genetically altered to be CL-tolerant, the overall leaf area would be low, resulting in reduced light capture and plant growth [9]. However, leaf expansion can be controlled by spectral compositions. Red supplemental light is generally thought of as a vegetative light, able to improve leaf expansion, whereas blue light is known to reduce leaf size and increase leaf thickness [17,18]. Therefore, utilizing red light during extended photoperiods may overcome the reduction in leaf size invoked by CL. It should also be noted that the rootstocks can affect plant growth. While grafting is traditionally done to invoke disease resistance, many studies have shown that proper selection of rootstock materials can increase leaf area as well as plant vigor, leading to improved yield [19–21]. Therefore, the use of wavelength-specific lighting such as red light

and proper rootstock selection may alleviate photoperiod-related injury. For these reasons, we set out to test the response of large vining tomatoes to an extended photoperiod of lighting with different spectral compositions/quality and rootstocks to see if the negative impact of extended photoperiod lighting on tomato fruit production can be eliminated by proper selection of light spectral compositions and rootstocks.