**Abstract**

Droughts are common in arid areas. These cause important losses in crop production, while the increasing population demands more food and goods. Cultivars able to produce under drought conditions are required to avoid or reduce production losses. Plants have evolved different mechanisms to face drought, and many genes have been already discovered in model and cultivated plants that are involved in this trait. Some of these genes have been successfully transformed into cultivated plants for drought tolerance. Plants native to arid lands may possess variants of drought tolerance mechanisms as compared to mesophytic or model plants. Also, different drought-related genes can be revealed. Studies using high-throughput and bioinformatic tools may allow to discover new genes and give new insights on the mechanisms involved in drought tolerance. However, still scarce studies in plants native to arid lands show that there are many drought-related genes that have not been already characterized and potentially they may be novel genes. These novel genes may be used to improve crops for drought tolerance. Therefore, more physiological, transcriptomic, proteomic, and metabolomic studies are needed on plants native to the deserts.

**Keywords:** abiotic stress, water stress, drought-related genes, genetic diversity, deserts, vegetation, drought-tolerance mechanisms, oxidative stress, osmotic adjustment, differentially expressed genes


*3.2. Single-Robot End-to-End Navigation*

To accomplish the final goal, we start at the single robot navigation case in this section. In the end-to-end mapless navigation tasks, the relationship between the perception inputs and the output control law can be very complex. In our work, we consider a number of modifications to the Deep Deterministic Policy Gradient (DDPG) and use it as the basic framework.

#### 3.2.1. Basic Reinforcement Learning Framework

Classical Deep Deterministic Policy Gradient (DDPG) [41] is a kind of remarkable reinforcement learning algorithm to address the continuous control problem. As we all know, the trade-off between the exploration and the exploitation is one of the most important problems in the reinforcement learning field. To increase the sample efficiency, the DDPG uses the deterministic target policy *μ* : S←A rather than the stochastic policy *π*, and the corresponding exploration decrease is made up of the off-policy technique. The Bellman equation can be rewritten as Equation (4), where the *γ* term represents the discount factor of the equation:

$$Q^{\mu}\left(\mathbf{s}\_{t},\mathbf{a}\_{t}\right) = \mathbb{E}\_{r\_{t}s\_{t+1}\sim E}\left[r\left(\mathbf{s}\_{t},\mathbf{a}\_{t}\right) + \gamma Q^{\mu}\left(\mathbf{s}\_{t+1},\mu\left(\mathbf{s}\_{t+1}\right)\right)\right].\tag{4}$$

At each timestep, the actor networks and the critic networks are updated by sampling a minibatch uniformly from the memory buffer. The algorithm also creates a copy of the actor and critic networks, *Q s*, *a*|*θQ* and *μ s*|*θμ* , respectively; these target networks are then updated softly by the learned networks: *θ* ← *τθ* + (1 − *τ*)*θ* with *τ* 1. This trick can greatly improve the stability of learning.

The loss function for the critic networks can be formulated as Equation (5):

$$L = \frac{1}{N} \sum\_{i} \left( y\_i - Q\left( s\_i, a\_i | \theta^Q \right) \right)^2,\text{ where } y\_i = r\_i + \gamma Q' \left( s\_{i+1}, \mu' \left( s\_{i+1} | \theta^{\mu'} \right) | \theta^{Q'} \right). \tag{5}$$

In addition, the actor networks can be updated by using the sampled policy gradient as shown in Equation (6):

$$
\nabla\_{\theta^{\mu}} I \approx \frac{1}{N} \sum\_{i} \nabla\_{a} \mathcal{Q} \left( s, a | \theta^{\mathcal{Q}} \right) \Bigg|\_{s=s\_{i}, a=\mu(s\_{i})} \nabla\_{\theta^{\mu}} \mu \left( s | \theta^{\mu} \right) \Bigg|\_{s\_{i}} \tag{6}
$$

#### 3.2.2. Network Structure

Owing to the structure of the 2D lidar data, we add one-dimensional, convolutional neural network (CNN) layers to the feature extraction part. To enrich the perception information of the policy module, some relative parameters about the target are added to our framework. It enables the policy module to simultaneously consider the sensor observation and the robot state information. Both of them are essential for efficient navigation.

As shown in Figure 1, the raw lidar sensor data are filtered into 180 dimensions as inputs. As mentioned before, there are two kinds of neural networks in our module. One is the actor network and the other is the critic network. Both of them use three one-dimensional, CNN layers for feature extraction. After the data feature extraction, the networks use residual building blocks with shortcut connections [42] to reduce the training complexity. Then, we pack the target distance, the target angle and the actions at the last timestep as the state parameter set. The state parameter set and the lidar data feature extraction will be fused together and fed to the actor and critic networks at the same time. For the actor network, the fusion data will pass three fully connected layers and output the linear velocity and angular velocity through different activation functions. Besides controlling the robot navigation, the output of the actor network will be transmitted to the critic network. The output action, the state parameter set and the feature extraction of the critic network will be fused into one data stream. After

passing through four fully connected layers, the data stream will finally become a Q value to evaluate the policy and train the networks.

**Figure 1.** The structure of the modified Deep Deterministic Policy Gradient for single robot mapless navigation tasks.

#### 3.2.3. Reward Shaping

The reward function plays an important role in reinforcement learning: it greatly influences the navigation of the robot system and serves as the truth holder of the learning process. For the single robot mapless navigation task, the reward function shown in Equation (7) consists of three different parts: the arrival award, the collision penalty and the approaching award. If the distance value between the robot and the target point is less than the arrival threshold, the robot can get a positive reward *rarrive* if the robot collides with the obstacles during navigation. In other words, one of the lidar distance observations is less than the safety threshold. The robot will get a negative reward *rcollision*. To enable the intuition of approaching the target, we add the approaching award to the robot, where the reward is noted as *k*(*ρt*−1 − *ρt*). The *k* term is a constant to adjust the amplitude of the approaching reward. The *ρ* term represents the distance between the target and the mobile robot at timestep *t*. To unify the range of different parameters, they will be normalized before utilizing:

$$r(s,a) = \begin{cases} r\_{\text{array}} & (\rho < d\_{\text{goal}}) \\ r\_{\text{collision}} & (\min(d\_1, d\_2, \dots, d\_{180}) < d\_{\text{collision}}) \\ k(\rho\_{t-1} - \rho\_t) & (At \text{ each time step } t) . \end{cases} \tag{7}$$


2.2.2. Specifications

The collection of biowaste for both treatment methods is realised by door-to-door collection. The GWP value of 1.25 kg(CO2)eq.twaste−1km−1 was considered for a collection vehicle [25] and value

of 0.088 kg(CO2)eq.twaste−1km−1 for a EURO5 truck [26] used in transport. The distance driven by the collection vehicle was estimated as 10 km, and the transport distance to a treatment facility was set to 50 km for both cases.

In the case of the composting plant, it is assumed that 10% of composting plants are open and 90% of the plants are closed. Any impurities are separated, and the plants produce ready-made compost only. The amount of compost yield is 440 kg/t of biowaste. As for the anaerobic digestion plant, a continuous dry fermentation with composting of solid fermentation residues was supposed. The biogas yield is assumed with 100 mN3/t of biowaste.


3.5.4. Proposed Mechanism

Scheme 2 demonstrates the photocatalytic degradation mechanism of methyl red by N-doped Cu2O/ZnO composite nanoparticles under sunlight irradiation. When the semiconductors are exposed to sunlight, a transition of electrons from the valence band to the conduction band and formation of holes in the valence band (Equations (3) and (4)) can take place. The conduction band of cuprous oxide is at higher position compared to zinc oxide (Jiang et al., 2013), the photo-induced electrons in the Cu2O conduction band can simply transfer to the ZnO conduction band Equation (5), which can successfully prevent the recombination of charge carriers. The electrons at the conduction band of Cu2O and ZnO undergo a reaction with adsorbed oxygen to give a peroxide radical anion Equation (6). It is impossible to oxidize the OH<sup>−</sup> by holes of Cu2O because the valence band edge of Cu2O is higher in a position [45]. However, the holes of ZnO oxidize the hydroxyl ion to yield a hydroxyl radical Equation (8), a strong oxidizing agent that can break down the organic dye. Besides, the peroxide radical anion undergoes a reaction with the hydrogen ion to produce HO2 and H2O2 Equation (9). The hydrogen peroxide is then reacted with the peroxide radical anion to create the powerful hydroxyl radicals Equation (11). Eventually, the hydroxyl radical oxidizes the dye into photocatalytic degradation products Equation (12) [35,43]. The reaction steps are described below.

$$\text{Cu}\_2\text{O} + \text{hv} \rightarrow \text{Cu}\_2\text{O} \left(\text{h}^+\right) + \text{Cu}\_2\text{O} \left(\text{e}^-\right) \tag{3}$$

$$\text{ZnO} + \text{hv} \rightarrow \text{ZnO} \left(\text{h}^+\right) + \text{ZnO} \left(\text{e}^-\right) \tag{4}$$

$$\text{CuO}\_2\text{O}\left(\text{e}^-\right) + \text{ZnO} \rightarrow \text{Cu}\_2\text{O} + \text{ZnO}\left(\text{e}^-\right) \tag{5}$$

$$\text{CuO}\_2\text{O}\left(\text{e}^-\right)/\text{ZnO}\left(\text{e}^-\right) + \text{O}\_{2^\circ} \tag{6}$$

$$\text{H}\_2\text{O} \rightarrow \text{H}^+ + \text{OH}^-\tag{7}$$

$$\text{ZnO} \left(\text{h}^+\right) + \text{OH}^- \rightarrow \text{OH} \tag{8}$$

$$\text{O}\_{2\*} + \text{H}^+ \rightarrow \text{HO}\_2^\cdot \tag{9}$$

$$\text{HO2} + \text{HO2} \rightarrow \text{O2} + \text{H2O2} \tag{10}$$

$$\text{H}\_2\text{O}\_2 + \text{O}\_{2^\circ} \rightarrow \text{OH}^- + \text{OH}^\cdot + \text{O}\_2\tag{11}$$

$$\text{OH} + \text{Dye} \rightarrow \text{H}\_2\text{o} + \text{CO}\_2\tag{12}$$

The schematic illustration for the photocatalytic degradation mechanism of methyl red under sunlight is summarized below.

**Scheme 2.** Schematic illustration of the photocatalytic degradation of methyl red using N-Cu2O/ZnO nanocomposite under sunlight illumination.


**Abstract**

The chapter is meant to expose how a sound methodology can be instrumented to both, remediate acidic metal polluted mine wastes, taking advantage of the neutralizing power and high metal sorption affinity of biochar, and to utilize pyrolyzed material derived from high-rate growth plants (water hyacinth, *Eichhornia crassipes* Mart, and Eucalyptus, *Eucalyptus globulus* Labill), which have become of ecological relevance due to their unwanted proliferation over specific terrestrial, lacustrine or riverine environments. In addition, the proposal considers not only neutralizing the mine tailings and abating the toxic levels of specific heavy metals like Pb, Cd, Cu, Zn, etc., to fulfill the international and national standards and norms, but to conveniently combine biochar with widely used soil amendments to pass widely recognized biological tests of growth using heavy metal-sensitive plants. The approach addresses firstly: a) characterizing physiochemically mine tailings and biochar, in terms of their properties (metal speciation and contents, potential acidity and neutralization potential, chemical oxygen demand, heavy metal-biochar sorption-complexing affinities, among others), and secondly; b) creating a" fertile environment" by reconditioning, agriculturally, the heavy metal-polluted acidic mine waste to allow native vegetation, or other reforesting species, to regrow on the reclaimed site, based on the bioassay tests performances.

**Keywords:** biochar, mine tailings remediation, soil amendments, heavy metal phytotoxicity, heavy metal availability and speciation