**3. Methods**

*3.1. CNN*

CNN is a typical feedforward neural network. It virtually constructs various filters that can extract the characteristics of input data. Through these filters, the input data is convoluted and pooled, and the topology features hidden in the data are extracted step by step. With the deep entry of the network layer, the extracted features are abstracted. Therefore, the extracted features have translation, scaling, and rotation invariance. The sparse connection in CNN reduces the number of training parameters and speeds up the convergence; weight sharing effectively avoids algorithm overfitting; and downsampling makes full use of the features of the data and reduces the data dimension, optimizing the network structure [29,30]. CNN can deal with one-dimensional (1-D) signals and sequences, two-dimensional (2-D) images, and three-dimensional (3-D) videos. We apply CNN to extract features from 1-D sequences in this paper.

The essential components of CNN are convolutional operation and pooling operation. Through convolution operation, high-level local region feature representations are extracted with different filter kernels. The convolution process is described as follows:

$$\mathbf{x}\_{j}^{l} = f(\sum\_{i \in M\_{j}} \mathbf{x}\_{i}^{l-1} \times \mathbf{k}\_{ij}^{l} + \mathbf{B}\_{j}^{l}) \tag{4}$$

where *xlj* are the j feature maps of *lth* layer through convolution operation between *l* − 1*th*'s output *xl*−1 *i* and *j* filters *klij*, *Blj* is *j* bias of each feature map; *i* is in the range of *j* input values *Mj*. After convolution operation, *xlj* is processed with an activation function. The comprehensive result *alj* is the input of the next layer. Rectified Linear Unit (ReLU) was widely applied to accelerate and converge the CNN, which enabled a nonlinear expression of input signals to enhance the representation ability. Which is formalized as follows:

$$a\_j^l = \max\{0, x\_j^{l-1}\} \tag{5}$$

Another key component of CNN is the pooling operation, which is employed to reduce the dimension of input data and ensure scale invariance. Thus, obtained features are more stable, especially when data is acquired from a noisy environment. There are three types of pooling operations: maximum, minimum, and average pooling operation. We give an example of utilizing maximum pooling, which is expressed as follows:

$$p\_j^l = \max\{q\_j^{l-1}(t)\}, \ t \in [(j-1)w, jw] \tag{6}$$

where *plj* is the output of maximum value among *l* − 1*th* layer obtained feature maps *ql*−1 *j* (*t*), *t* is *tth* output neurons at *jth* layer in the network, *w* is the width of pooling size. Further details of CNNs can be found in LeCun's paper [11].


*5.4. Hydropower Systems*

In Poland, compared to other countries, the use of the water energy potential is incomparably lower, which is primarily due to the climatic conditions, average rainfall, and topography. Traditional hydroelectric power plants could be built in places with large natural slopes or where water could be artificially dammed. Hydropower systems require high initial investment costs (mainly due to construction costs). It was estimated that the share of hydropower in primary energy was about 1.5%. Poland's hydropower resources alone are estimated at 13.7 TWh per year. If it were possible to fully use Poland's hydropower systems potential, it would be possible to achieve even ca. 11 GW of capacity in big, and ca. 1.2 GW in small and medium, hydropower plants. In 2020, there were only 18 hydroelectric plants in the country with more than 5 MW.

The conclusion from the weighted SWOT matrix for the hydropower systems in Poland (Table 6) was also quite optimistic. The IFR was above 1–1.45, meaning that the strengths were more critical than weaknesses. The opportunities to threats ratio was 0.98, which means that the hydropower systems' development perspectives are not favorable because of extreme dependence on political decisions. The balance between the IFR and EFR was 1.45:0.98. This means a favorable situation because strengths and opportunities are more critical than weaknesses and threats. The things that Polish policymakers should do are to manage the potential resistance of society and to promote the building of microto medium hydropower power stations primarily in the places where watermills operated over 100 years ago.


**6. Enzymes involved in the detoxification of reactive oxygen and nitric species**

Human tissues contain specific enzyme systems to detoxify ROS and RNS. Superoxide dismutases (SODs) and catalase (CAT) together with glutathione peroxidases represent an important line of the primary antioxidant enzyme defence system against ROS. Superoxide dismutases catalyse the dismutation of O2 − to H2O2 and oxygen (O2), whereas CAT subsequently catalyses the conversion of H2O2 to water (H2O) and O2 [48, 75–82]. Three distinct SOD isoenzymes have been identified in mammals: a cytosolic copper-zinc SOD (CuZnSOD or SOD1) localised in cytoplasmic compartment with cooper (Cu) and zinc (Zn) in the catalytic centre, manganese SOD (MnSOD or SOD2) that is localised in mitochondria and uses manganese (Mn) as a cofactor and extracellular SOD (ECSOD or SOD3) that also contains Cu and Zn in the catalytic centre and is located in the extracellular space [74, 82, 83].

Another important family of enzymes involved in the detoxification of xenobiotics and electrophiles produced by ROS and RNS is glutathione S-transferases (GSTs) [84–87]. They catalyse the conjugation of reduced glutathione to different electrophiles [88]. These conjugation reactions mostly result in less reactive products [89]; however, in some cases, the products are more reactive and consequently more harmful than the parent compound [90, 91]. Seven classes of cytosolic GST isoenzymes have been recognised in mammals (Alpha, Mu, Pi, Sigma, Theta, Omega, Zeta) [84–86, 91, 92]. The major GST enzyme in the human lung is GSTP1, which belongs to the Pi class [90, 91, 93], while GSTM1 (Mu class) and GSTT1 (Theta class) were most frequently investigated [90, 91].