*Proceeding Paper* **Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization †**

**Cilina Touabi \*, Abderrahmane Ouadi and Hamid Bentarzi**

**Abstract:** A photovoltaic (PV) cell is generally used as renewable energy source. For an accurate study of various PV applications, modeling this basic device in a PV generator is essential. However, the manufacturers do not usually provide the model parameters and their values vary over time due to PV degradation and the change in weather conditions. Thus, finding an optimal technique for estimating the appropriate parameters is crucial. This problem can be solved by metaheuristic optimization algorithms, namely particle swarm optimization (PSO). However, early convergence is the main defect of PSO. This work presents an enhancement in the optimization method (PSO) for identifying the optimal parameters of a PV generating unit. In this method, the identification of parameters of the single diode model is based on an opposition-based initialization particle swarm optimization technique. The optimization algorithm is implemented in MATLAB which gives good results.

**Keywords:** photovoltaic (PV) cell; opposition-based particle swarm optimization algorithm; one diode model parameters

#### **1. Introduction**

Solar Energy is considered as the most promising alternative to conventional energy sources. Its main application is the photovoltaic (PV) power generation that was predicted to be over 1000 TWh in 2021 [1]. PV systems convert solar energy into electrical energy. They can be installed easily, and they are noise-free. For an accurate study of various PV applications, modeling the basic device in a PV generator is essential. However, the manufacturers do not usually provide the model parameters and their values vary over time due to PV degradation [2] and the change in weather conditions. Thus, finding an optimal method for estimating appropriate parameters is critical.

The single diode model (ODM) is regarded as the most suitable model used to characterize the PV generator [3–7] compared to the two-diode model (DDM) and the three-diode model (TDM) as it has the least number of parameters and a good accuracy. The ODM has five electrical parameters that are: photocurrent (Iph), reverse saturation current (Is), diode ideality factor (n), series resistance (Rs), and parallel resistance (Rsh).

Different techniques have been developed to identify ODM parameters, which can be classified in three categories [5,7]:

Although numerical methods are widely used in the literature due to their speed of calculation, simplicity, and accuracy, they cannot be used to solve PV model complex non-linear equations. This problem can be solved using optimization techniques based on artificial intelligence. Particle swarm optimization (PSO) is a popular metaheuristic

**Citation:** Touabi, C.; Ouadi, A.; Bentarzi, H. Photovoltaic Panel Parameters Estimation Using an Opposition Based Initialization Particle Swarm Optimization. *Eng. Proc.* **2023**, *29*, 16. https://doi.org/ 10.3390/engproc2023029016

Academic Editors: Abdelmadjid Recioui and Fatma Zohra Dekhandji

Published: 3 February 2023

**Copyright:** © 2023 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/).

Laboratory Signals and Systems, IGEE, University M'hamed Bougara, Boumerdes 35000, Algeria

optimization algorithm. However, the main defect of PSO that does not let it provide high-quality solutions in multimodal problems, such as PV panels parameters estimation, is its early convergence. This work presents an enhancement in the optimization method (PSO) for extracting the optimal parameters of a PV generating unit. The purpose of this work is to simulate the I-V and P-V characteristics based on the single diode model (ODM) using an opposition-based initialization particle swarm optimization algorithm that allows finding the optimal values of the needed parameters. The optimization algorithm is implemented in MATLAB for obtaining these model parameters and hence, I-V and P-V characteristics. The analysis is performed on various PV modules under different environmental conditions. The obtained results are compared and discussed to prove the efficiency and accuracy of the suggested optimization technique.

#### **2. Photovoltaic Cell**

A PV cell is an electronic device that permits the conversion of solar energy into electrical energy based on the photovoltaic effect, as shown in Figure 1. Solar cell produces electricity with poor voltage, which is approximately about 0.5 to 0.6 volts for the common single junction silicon PV cell. Thus, PV cells are coordinated in the form of modules or panels to produce electricity with high voltage and to provide adequate voltage and current for life applications.

**Figure 1.** The photovoltaic effect in a PV cell.

#### *2.1. Characteristics of the PV Cell*

The electrical characteristics of a PV generator are mainly provided by the manufacturers under standard test conditions (STC) that are specified by the ambient temperature TSTC = 25 ◦C, irradiation level GSTC = 1000 W/m2, and the air mass value AM = 1.5. However, in the working field, PV panels operate at varying temperatures and at lower insulation levels. In order to define the power output of the solar generator, it is essential to find the expected operating temperature of the PV panel. The nominal operating cell temperature (NOCT) is set as the temperature reached by open circuited cells in a PV panel under the conditions: ambient temperature Tambient = 20 ◦C, solar irradiance G = 800 w/m2, and wind speed = 1 m/s. Hence, the PV cell temperature can be calculated as follows:

$$T\_{\text{cell}} = T\_{\text{ambient}} + \left(\frac{\text{NOCT} - 20}{800}\right) \ast \text{G} \tag{1}$$

The typical I-V and P-V curves characterizing a photovoltaic cell are shown in Figure 2. The three significant points on the photovoltaic characteristics are short circuit current (Isc), open circuit voltage (Voc), and maximum power point (Vmpp,Impp).

The maximum current Isc in the photovoltaic cell is generated when a short circuit occurs between its terminals, while the maximum voltage Voc can be measured when there is an open circuit.

The maximum power achieved from a photovoltaic cell occurs at a point on the bend in the I-V curve known as the maximum power point (MPP). The voltage and current at those points are designated as Vmpp and Impp.

Generally, manufacturers provide these parameters in the datasheet under STC. When the PV panel is connected to an external load, the actual point on the I-V curve at which the photovoltaic cell operates is determined based on the electrical characteristics of the load.

**Figure 2.** Typical I-V and P-V characteristics of PV cell.

#### *2.2. One Diode Model of PV Cell*

In order to analyze solar cells characteristics, this latter is modeled as electrical equivalent circuits using simulation software. Researchers have developed mathematical models to understand and study the effect of different weather conditions on photovoltaic electrical output. One of these models that is widely used is the lumped parameter model. This model is classified based on the number of diodes used and it has proven to be the most successful.

Although the model characteristics accuracy improves as the number of diodes increases, the model mathematical equation becomes more complex. In this work, the single diode model that is shown in Figure 3 is used for the identification of the PV generator parameters due to its simplicity compared to other lumped models and its good accuracy.

**Figure 3.** One diode model of PV cell.

The ODM governing equation is given as:

$$\mathbf{I} = \mathbf{I}\_{\mathrm{ph}} - \mathbf{I}\_{\mathrm{s}} \left[ \mathbf{e}^{(\frac{\mathbf{V} + \mathbf{I} \star \mathbf{R}\_{\mathrm{h}}}{n \star \mathbf{V}\_{\mathrm{h}}})} - 1 \right] - \frac{\mathbf{V} + \mathbf{I} \star \mathbf{R}\_{\mathrm{s}}}{\mathbf{R}\_{\mathrm{sh}}} \tag{2}$$

Such that, Vt is the thermal voltage.

The model's five parameters are:

Iph: Photocurrent (A); Is: Diode saturation current (A); n: Diode ideality factor; Rsh: Parallel resistance (Ω); Rs: Series resistance (Ω).

The single diode model considers various properties of the solar cell such as: the shunt resistance that considers the leakage current to the ground when the diode is in reverse biased, and the series resistance that takes into account the voltage drops and internal losses due to the flow of current. However, this model is still not the most accurate model as it has neglected the recombination effect of the diode.

#### **3. Identification of PV Cell Parameters Using Optimization algorithms**

#### *3.1. Optimization Algorithms*

It can be noticed that the I-V relationship of the one diode model, which is given in Equation (2) and hence the current voltage curve, is complex and highly nonlinear. Thus, it is difficult to solve it using analytical methods. Consequently, metaheuristic optimization methods based on artificial intelligence have been developed by scientists for solving these kinds of equations and determine the needed parameters. These techniques are developed to find a good solution among a large set of feasible solutions with less computational effort than other optimization techniques.

#### *3.2. Opposition Based Initialization Particle Swarm Optimization Technique*

Particle Swarm Optimization is a population-based metaheuristic global optimization method inspired by the motion of schooling fish and bird flocks. The PSO algorithm examines the space of an objective function by adjusting the trajectories of individual agents, named particles. A population of these particles flies through the search space such that each particle i is attracted toward the position of the current global best g\* and its own best location xi in history, simultaneously, it has a tendency to move randomly. Initially, the particles are placed randomly in the search space. The objective function is evaluated for all the particles. When a particle i finds a position that is better than any previously found locations, it updates it as the new current best location xi by updating the velocity, first depending on movement inertia, self-cognition, and social interaction using Equation (3), then updating its position through Equation (4) at each iteration. Thus, all n particles have a current best position at any time t during iterations. The purpose is to find the global best among all the current best solutions until the objective no longer improves or after a specified number of iterations. The motion of particles is schematically represented in Figure 4. Where g\* = {(xi )} for (i = 1, 2, ... n) is the current global best and x∗ i is the current best for particle i [8,12].

$$\mathbf{v\_{n+1}^i} = \mathbf{w} \,\mathbf{v\_n^i} + \mathbf{c\_1 r\_1} \left[\mathbf{p\_n^i} - \mathbf{x\_n^i}\right] + \mathbf{c\_2 r\_2} \left[\mathbf{p\_n^g} - \mathbf{x\_n^i}\right] \tag{3}$$

$$\mathbf{x}\_{\mathbf{n}+1}^{\mathbf{i}} = \mathbf{x}\_{\mathbf{n}}^{\mathbf{i}} + \mathbf{v}\_{\mathbf{n}+1}^{\mathbf{i}} \tag{4}$$

Such that:

xi is the position of the ith particle in the search space;

vi is the velocity of the ith particle;

w is particle inertia;

c1 is the cognitive acceleration constant;

c2 is the social acceleration constant;

pi is the particle's best-known position;

pg is the global best position;

r1, r2 are random numbers that vary between 0 and 1.

The starting points in PSO are randomly given. If these latter are close to the optimal point, convergence speed would be faster. Thus, to get better results, a better and careful initialization based on priori information is needed. The proposed method for an improved PSO algorithm is an opposition-based initialization of the swarm. This approach consists of initializing the PSO population and its opposite population, as shown in Figure 5. The fitness function is evaluated for both populations and only the fitter particles are selected to form a new population for the PSO.

**Figure 5.** PSO with (**a**) Random population initialization and (**b**) Opposition based population initialization.

The concept used is described below.

Particle: a swarm particle pi is defined as:

i ∈ [a, b] such that, i = 1, 2, . . . , D and a, b ∈ R;

D represents dimensions, and R represents real numbers.

Opposite particle: every particle pi has a unique opposite pi op defined as:

$$\mathbf{p}^i \mathbf{p}^i = \mathbf{a} + \mathbf{b} - \mathbf{p}^i \text{ such that, } \mathbf{i} = 1, \text{ 2, ..., D and } \mathbf{a}, \mathbf{b} \in \mathbb{R} \tag{5}$$

#### *3.3. ODM Parameters Extraction Using an IOB-PSO*

The following objective function is used,

$$\begin{cases} F(\mathbf{X}) = I - \mathbf{I}\_{\mathrm{ph}} + \mathbf{I}\_{\mathrm{s}} \left[ \mathbf{e}^{\left( \frac{\mathbf{V} + \mathbf{I} \star \mathbf{R}\_{\mathrm{s}}}{\mathbf{n} \star \mathbf{N}\_{\mathrm{s}} \star \mathbf{V}\_{\mathrm{s}}} \right)} - 1 \right] + \frac{\mathbf{V} + \mathbf{I} \star \mathbf{R}\_{\mathrm{s}}}{\mathbf{R}\_{\mathrm{sh}}} \\ \qquad \qquad X = \left\{ \mathbf{I}\_{\mathrm{ph}}, \mathbf{I}\_{\mathrm{s}}, \mathbf{R}\_{\mathrm{s}}, \mathbf{n}, \mathbf{R}\_{\mathrm{sh}} \right\} \end{cases} \tag{6}$$

The fitness function used to quantify the error between the simulated and measured data is the root mean square error (RMSE),

$$\text{Fitness} = \sqrt{\frac{1}{N} \sum\_{1}^{N} \text{F}(\lambda)^2} \tag{7}$$

The pseudo code of the IBPSO method is given in Algorithm 1 and its corresponding flowchart is shown in Figure 6.

**Figure 6.** The flowchart of the IOBPSO algorithm.

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

The proposed algorithm IOB-PSO is used to identify the parameters of the single diode model. The algorithm is tested for two different PV modules. The obtained results are compared with other methods results to prove its effectiveness. It is developed in MATLAB R2016a and executed under Windows 10 64-bit OS, on a PC with Intel® Core™ i5-2450M CPU processor @ 2.50GHz, 4GB RAM.

Table 1 presents the search ranges used for the optimization of the model parameters. Table 2 presents the IOB-PSO parameters.

**Table 2.** IOB-PSO parameters.

The IOB-PSO algorithm is used to estimate parameters for the various PV modules. The electrical specifications of the utilized modules are described in Table 3.

**Table 3.** Electrical specifications of the PV modules.

The IOB-PSO is tested for the mono-crystalline STM6-40/36 module using I-V experimental data [13] measured at T = 51 ◦C; and for the Photowatt-PWP201 poly-crystalline module with I-V data measured under a temperature of T = 45 ◦C [13]. The obtained results are presented and compared with other previously published methods in Tables 4 and 5, respectively.

\* Proposed method.

**Table 5.** Photowatt-PWP201 extracted parameters achieved by different methods.

\* Proposed method.

Using the identified parameters, the PV characteristics of the two modules were constructed and then compared with the experimental curves in Figures 7 and 8.

**Figure 7.** I-V and P-V experimental data and simulated data for the STM6−40/36 module.

**Figure 8.** I-V and P-V experimental data and simulated data for the Photowatt-PWP201 module.

It can be noted that the RMSE error of the proposed IOB-PSO is the smallest and hence, the obtained results using the proposed algorithm are the best. Therefore, the efficiency of this method for identifying the model parameters is proven. Figures 7 and 8 show the comparison between I-V and P-V experimental and simulated data of the STM6-40/36 and the Photowatt-PWP201 modules. The simulated curves highly match the measured ones, which proves the reliability of the IOB-PSO algorithm.

Figure 9 presents the convergence curve of the IOB-PSO method for these modules; it can be noticed that the convergence is very fast. The algorithm reached the optimal solution within 207 iterations in an execution time of 19.0017 s for the monocrystalline module STM6-40/36, while for the Photowatt-PWP201 module, it needed 613 iterations (27.585 s). This proves that the proposed IOB-PSO method has a very fast convergence speed.

**Figure 9.** Convergence curve of the IOB-PSO for the (**a**) STM6−40/36 module (**b**) Photowatt-PWP201 module.

#### **5. Conclusions**

In this work, an efficient method for obtaining the I-V and P-V characteristics of a PV module based on the one diode model has been proposed, due to the non-availability of all needed parameters in the datasheet. The optimization algorithm was chosen for the identification of the ODM model parameters. The proposed method is an improved opposition-based particle swarm optimization; this algorithm was tested using the experimental I-V data that were acquired at different working conditions for various PV modules. The obtained results were compared with other methods outcomes provided in the references; this algorithm has showed a satisfying estimation of the five parameters extracted with minimum errors. Furthermore, the PV characteristics were plotted using the extracted parameters and compared with the experimental data. The simulated curves are found to be well-suited with the corresponding measured data, thus, the performance of the IOB-PSO algorithm proved to be good. Adding to the fact that this proposed algorithm has provided optimal results with an acceptable accuracy, the time taken for the IOB-PSO execution is less than 30 s.

This approach is found to be useful for designers since it is simple, fast, and provided accurate results.

**Author Contributions:** Conceptualization, C.T., A.O. and H.B.; methodology, C.T., A.O. and H.B.; software, C.T.; validation, C.T., A.O. and H.B.; formal analysis, C.T., A.O. and H.B.; investigation, C.T., A.O. and H.B.; resources, C.T.; data curation, C.T.; writing—original draft preparation, H.B.; writing—review and editing, C.T. and H.B.; visualization, H.B.; supervision, A.O. and H.B.; project administration, A.O. and H.B.; funding acquisition, H.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data may involve confidential information of our research group.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

## *Proceeding Paper* **APF Applied on PV Conversion Chain Network Using FLC †**

**Bourourou Fares \*, Tadjer Sid Ahmed and Habi Idir**

LREEI, M'hamed Bougara of Boumerdes University, Boumerdes 35000, Algeria

**Abstract:** This paper focuses on regulation of the parallel active power filter (APF) Dc Voltage bus by judicious choice of rule bases and intervals for each selected fuzzy variable of suitable fuzzy logic controller. In addition, an algorithm describes the main steps for designing an FLC that has any number of rules with direct application to the APF capacitor voltage regulation. Where their simulation, by MATLAB, applied to PV conversion chain network will be represented in the booths cases, constant and variable non-linear loads after modeling, to show the effectiveness of this kind of regulators on electrical power quality and improve the reliability of the APF on PV system. The delivered voltage of PV plant has been regulated and controlled with MPPT using P&O technique and FLC regulator after modeling of each part of the conversion chain. PV plant supplies a nonlinear load from the rectifier installed on the output of the conversion chain via a controlled power inverter. A 3 × 3 rules fuzzy regulator is implanted in the control part of the APF to examine the influence of the FLC on the produced electrical power quality. Simulation results are represented and analyzed.

**Keywords:** APF; PV; FLC; renewable energy; power quality

#### **1. Introduction**

PV plant conversion chain description and the MPPT technique based on P&O under irradiation and temperature variation influence the generated amplitude of output voltage [1–3] and the detailed modeling behavioral Matlab simulation. For obtaining good performance and efficiency energy, it is proposed to host a diverse suitable controls strategy to replace the power electronic interface to achieve the needed performance results for the system [3].

#### **2. Description and Modeling of PV Conversion Chain**

#### *2.1. PV System Description*

The global scheme present in Figure 1 shows the main parts dedicated to converting sun power to electrical energy. The use of PV allows the conversion to be achieved, and the system will be modeled.

**Figure 1.** PV station power conversion basic part.

#### *2.2. PV System Modeling*

Solar cells' modeling is essential to the study of photovoltaic plant generators. Generally, it is represented by an equivalent circuit [4] shown in Figure 2 below.

**Citation:** Fares, B.; Ahmed, T.S.; Idir, H. APF Applied on PV Conversion Chain Network Using FLC. *Eng. Proc.* **2023**, *29*, 17. https://doi.org/ 10.3390/engproc2023029017

Academic Editors: Abdelmadjid Recioui, Hamid Bentarzi and Fatma Zohra Dekhandji

Published: 19 April 2023

**Copyright:** © 2023 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/).

**Figure 2.** One-diode equivalent circuit of the solar cell.

From Figure 2, for equivalent circuit, we can obtain

$$I\_{ph} = I\_D + I\_{Rp} + I.\tag{1}$$

The resistor *Rp* current is obtained by

$$I\_{Rp} = \frac{V + I\_{Rs}}{R\_p} \,\text{}\,\text{}\,\text{}$$

and diode current is obtained by

$$I\_D = I\_s \left[ e^{\frac{(V+l\_{Rs})}{nV\_l}} - 1 \right],\tag{3}$$

where *Is* is the diode saturation current obtained by

$$I\_s = K\_1 T^3 e^{\frac{E\_\xi}{kT}},\tag{4}$$

where

*Vt* = *KT*/*q*: Thermal stress at temperature *T*; *q*: Electron charge - 1 .602 × 10−19 C ;

;

Therefore, the expression of the characteristic *I* (*V*) is

$$I = I\_{\rm plr} - I\_{\rm s} \left[ e^{\frac{(V + I\_{\rm Rs})}{nV\_{\rm f}}} - 1 \right] - \frac{V + I\_{\rm Rs}}{R\_p} \,. \tag{5}$$

The application of the Newton method makes it possible to calculate the value of the current I for each iteration with

$$I\_{n+1} = I\_n - \frac{I\_{cc} - I\_n - I\_s \left[e^{\frac{(V + R\_s, I)}{nV\_T}} - 1\right]}{-1 - I\_s \left(\frac{R\_s}{nV\_T}\right) .[e^{\frac{(V + I\_t, R\_s)}{nV\_T}}]} . \tag{6}$$

Then, the new value of *Icc*, short circuit courant, corresponds to an irradiation *G,* and a given temperature *T* is calculated according to the following equation:

$$I\_{cc}(G,T) = I\_{ccr} \frac{G}{1000} [1 + a\left(T - T\_{ref}\right)].\tag{7}$$

Diode saturation current depends on the temperature. Its value for a given temperature *T* can be calculated by

$$I\_s(T) = I\_{sr} \left( T\_{ref} \right) \left( \frac{T}{T\_{ref}} \right)^{\frac{3}{n}} e^{\left( \frac{-qE\_{\rm g}}{nK} \right) \left( \frac{1}{T} - \frac{1}{T\_{ref}} \right)}.\tag{8}$$

Influence of solar radiation and temperature on PV power is represented by Equation (9):

$$P\_{PV} = \left(I\_{\rm sct} - N\_P I\_{0S} \left(e^{\frac{V\_{PV}}{\lambda\_T} + \frac{R\_s I\_{PV}}{N\_P}} - 1\right) - \frac{V\_{PV}}{R\_p} - \frac{R\_s I\_{PV}}{R\_p}\right).V\_{PV}.\tag{9}$$

A *PV* energy conversion system must have other components ensuring the operation of the system in a more reliable and optimum mode for power and efficiency indices, such as the use of DC-DC converters controlled by different control techniques [3–5], to have the desired output power able to supply industrial equipment.

#### *2.3. PV System MPPT Dependency on T & Ir*

The power delivered by a PV depends on the ambient temperature, the wind speed, the mounting of the module (integrated in the roof or ventilated), and all these parameters change according to the chosen site for module installation. In addition, the coefficients linked to the temperature differ according to the materials used for the manufacture of the module [4].

Temperature is a very important parameter in the behavior of PV cells [4]. Figure 3 describes the behavior of the module under a fixed illumination of 1000 W/m2, and at temperatures between 15 ◦C and 40 ◦C. We notice that the current increases with the temperature; on the other hand, the open circuit voltage decreases. This leads to a decrease in the maximum power available.

**Figure 3.** MPPT function of irradiation and temperature.

#### **3. PV Power Quality Improvement Using APF and Simulation**

The simulation block diagram presented in Figure 4 for an autonomous PV system based on polycrystalline solar panels under standard temperature and irradiation conditions (25 ◦C, 1000 W/m2) which supplies a load via a DC-DC-AC converter is used to supply the industrial plant with integration of an APF on the output of the PV inverter to minimize the harmonic presence and assure the power quality in the acceptable range. A

fuzzy logic controller is used to ensure the DC voltage control of the parallel APF which is based on rules shown in the figure below.

**Figure 4.** PV plant simulation bloc.

Where the member sheep functions of error and the error variation are chow on Figure 5 and the output control cis represented by Figure 6 below

**Figure 6.** Command membership functions.

$$\mu\_{\mathbf{z}}[\mathbf{e}(t)] = \begin{cases} 0 \text{ if } \mathbf{e} < \mathbf{a}\mathbf{e}2 \\ (\mathbf{e} - \mathbf{a}\mathbf{e}2)/(\mathbf{a}\mathbf{e}3 - \mathbf{a}\mathbf{e}2) \text{ if } \mathbf{a}\mathbf{e}2 < \mathbf{e} < \mathbf{a}\mathbf{e}3 \\ (\mathbf{a}\mathbf{e}4 - \mathbf{e})/(\mathbf{a}\mathbf{e}4 - \mathbf{a}\mathbf{e}3) \text{ if } \mathbf{a}\mathbf{e}3 < \mathbf{e} < \mathbf{a}\mathbf{e}4 \\ 0 \text{ if } \mathbf{e} > \mathbf{a}\mathbf{e}4 \end{cases} \tag{10}$$

where the inference table is given by Table 1.

**Table 1.** FLC inference table.

The aggregation rules are the following:

if (e < 0 & Δe(t) < 0) ==> Ue is GN; if (e > 0 et Δe(t) > 0) ==> U is GP; if (e < 0 & Δe(t) > 0) **OR** (e = 0 & Δe(t) = 0) **OR** (e = 0 & Δe(t) > 0) **OR** (e = 0 & Δe(t) < 0); **OR** (e > 0 & Δe(t) < 0) ====> U is Z ; if (e < 0 & Δe(t) = 0) ==> U is N ; if (e > 0 & Δe(t) = 0) ==> U is P.

The obtained results represented on Figures 7–9 show the effect of the FLC on power quality improvement and the APF Dc voltage regulation with a response time less than 0.01 s.

**Figure 7.** Load, APF and Supply current with and without APF connection.

**Figure 8.** APF VDC voltage regulation with PI then FLC controller.

**Figure 9.** Harmonic specter of supply current before and after APF connecting.

Also static error obtained is near to zero in the two cases PI and FLC controllers results. Where the harmonic specter represented on Figure 9 show the values of each harmonic before and after APF integration on Pv installation.

#### **4. Conclusions**

Nonlinear loads supplied by PV station harmonic have been described by the use of an APF controlled by PI controller and FLC controller. The APF installation on the network connected with the PV conversion chain decreased the THD of the network current from THD > 23% to less than THD < 5% with the use of a fuzzy logic controller.

We hope to apply more intelligent techniques to the studied system to obtain more suitable results for industrial application.

**Author Contributions:** Conceptualization, B.F.; methodology, B.F.; software, B.F.; validation, B.F.; formal analysis, B.F.; investigation, B.F.; resources, B.F.; data curation, B.F.; writing—original draft preparation, B.F.; writing—review and editing, B.F., T.S.A. and H.I.; visualization, B.F.; supervision, B.F., T.S.A. and H.I.; project administration, T.S.A. and H.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding for the university M'hamed Bougara of Boumerdes.

**Institutional Review Board Statement:** Validation by CS of LREE laboratory.

**Informed Consent Statement:** This study don't involving humans.

**Data Availability Statement:** The work has been elaborated in the university of M'hamed Bougara of Boumerdes, faculty of hydrocarbons and chemistry, LREE laboratory directed by Habi Idir.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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**Acknowledgements**

fluorescent probe that is specifically targeted to mitochondria. The resulting confocal images revealed that the green fluorescence of MitoTracker Green colocalize with the red fluorescence of MitoSOX Red indicating that MitoSOX Red is oxidized within mitochondria (**Figure 8**). To determine whether MitoSOX oxidation is a superoxide-dependent process, the cells were pre-

PEG-SOD, MitoSOX Red fluorescence was negligible (**Figure 8**, panels B and E). This result indicates that the majority of the atpenin A5-induced MitoSOX fluorescence is superoxide

is a succinate-dependent process by preincubating the cells with NPA before adding atpenin A5. As shown in **Figure 8** panels C and F, the MitoSOX Red fluorescence intensity in the NPAtreated cells was markedly reduced indicating that the atpenin A5-induced MitoSOX Red oxidation is a succinate-dependent event. Collectively, these data indicate that atpenin A5 induces

and HT-29 cells. In agreement with the results from the isolated mitochondria, addition of atpenin A5 to the MitoSOX Red-loaded dermal fibroblasts did not display any red fluorescence

of dermal fibroblasts. But dermal fibroblasts showed an increment in red fluorescence with antimycin A (**Figure 8** panel H). This observation shows that the mitochondria of the dermal

ROS are produced from the mitochondrial complex II of the cancer cells (DLD-1 and HT-29) but not from the normal cells (dermal fibroblasts) when the entry of electrons to its quinonebinding site is blocked with 10 μM atpenin A5. However, previous studies in our laboratory have revealed that quinone reduction in the mitochondrial complex II can be completely inhibited at the tested concentration of atpenin A5 [8, 10]. Therefore, the difference in ROS production between cancer cells and normal cells may be attributed to a difference in the complex IIs that is upstream of the atpenin A5-binding site. Since the dicarboxylate-binding site inhibitor, NPA, could inhibit the ROS production induced by atpenin A5 in cancer cells, it is concluded that the ROS-producing site must be within the complex II, upstream of the atpenin A5 inhibition site(Q site) and downstream of NPA-binding site (dicarboxylate-binding site). According to previous evidence, the most likely ROS-producing sites of mitochondrial complex II are FAD [35–37] site and the Q site [37–39]. Observations of this study show that the ROS-generating site of complex II in atpenin A5-treated cancer cells is FAD but not Q site.

In order to explain this observation, we analyzed the published data on the crystal structures of flavoproteins, which are available in both the substrate bound and unbound forms [40–42]. They have shown that the capping domain in Fp subunit rotates during the enzyme catalysis. When the active site is not occupied by the substrate, the capping domain is rotated away from the active site creating a solvent channel linking the active site and the surrounding aqueous environment. In the substrate bound state, the capping domain is rotated inward concealing the active site, preventing the access of solvent to the active site [37]. Thus, closed conformation acquired during enzyme catalysis appears to be vital for

dependent. Next, we attempted to determine whether the atpenin A5-induced O2

−

production from the mitochondrial complex II within intact DLD-1

when the Q site of complex III is inhibited by antimycin A.

−

generated within cells. In the presence of

−

production in mitochondria

production

incubated with PEG-SOD, which can scavenge O2

−

−

(**Figure 8** panel G) indicating that atpenin A5 does not induce O2

succinate-dependent O2

102 Mitochondrial Diseases

fibroblasts can produce O2

**4. Discussion**

Ministry of Education, Culture and Sports in Japan is gratefully acknowledged for their financial support.


*3.3. Previous Works and Future Investigations*

Previous studies have investigated the role of the hydrogeological parameters on the potential impacts induced by UPSH in the underground medium [16,30]. The present investigation goes further and shows how the *K* controls the potential environmental impacts on surface water bodies and on the global *η* of the plant. While the results are useful to understand the system behavior and to define screening methodologies, further investigation should be done regarding not only the influence of other hydrogeological parameters, such as the effective porosity, but also considering heterogeneities in the underground medium. In addition, the importance of the shape and volume of the available underground cavity should be assessed.

The results showed that, under certain circumstances, the discharge of large volumes of the pumped groundwater into surface water bodies would be needed. In addition, the effects on the piezometric head of pumping and discharge may alter the interactions between the groundwater and nearby surface water bodies. Consequently, it would be advisable to investigate how UPSH may influence the e-flows of nearby rivers [51] by considering UPSH plants during the environmental flow assessment processes.

This paper also investigated, for the first time, the relevance of the elevation of the piezometric head in terms of *η* and the environmental impacts. The results showed that the relative elevation of the piezometric head was crucial for the operation of a UPSH plant, and for this reason, it should certainly deserve more investigation in the future.

Finally, we assessed the influence of the water exchanges on the global *η* of UPSH plants. To date, only one study had investigated the relation between groundwater exchanges and *η* [32]. However, this previous investigation only considered the influence of groundwater exchanges on the *η* of pumps and turbines. While the findings of this previous study were relevant, the reported variations of *η* were much lower than those obtained in this paper.

The current investigation went further and considered the variations in the global *η* of UPSH by comparing the energy consumed for pumping water from the underground reservoir and that obtained by discharging water from the upper reservoir. Considering that the *η* of the pumps and turbines depends on the head difference between reservoirs, it is advisable to investigate the effect on their *η* of different positions for the piezometric head as in the present paper.


**Contents**

#### **About the Special Issue Editors** ..................................... **vii**

#### **Francesco Aletta and Jian Kang**

Promoting Healthy and Supportive Acoustic Environments: Going beyond the Quietness Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 4988, doi:10.3390/ijerph16244988 . . . **1**

#### **Angel M. Dzhambov, Iana Markevych, Boris Tilov, Zlatoslav Arabadzhiev, Drozdstoj Stoyanov, Penka Gatseva and Donka D. Dimitrova**

Lower Noise Annoyance Associated with GIS-Derived Greenspace: Pathways through Perceived Greenspace and Residential Noise

Reprinted from: *Int. J. Environ. Res. Public Health* **2018**, *15*, 1533, doi:10.3390/ijerph15071533 . . . **5**

#### **Marcus Hedblom, Bengt Gunnarsson, Martin Schaefer, Igor Knez, Pontus Thorsson and Johan N Lundstr ¨om**

Sounds of Nature in the City: No Evidence of Bird Song Improving Stress Recovery Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 1390, doi:10.3390/ijerph16081390 . . . **21**

#### **Sarah R. Payne and Neil Bruce**

Exploring the Relationship between Urban Quiet Areas and Perceived Restorative Benefits Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 1611, doi:10.3390/ijerph16091611 . . . **33**

#### **Shilun Zhang, Xiaolong Zhao, Zixi Zeng and Xuan Qiu**

The Influence of Audio-Visual Interactions on Psychological Responses of Young People in Urban Green Areas: A Case Study in Two Parks in China Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 1845, doi:10.3390/ijerph16101845 . . . **59**

#### **Gunnar Cerw´en**

Listening to Japanese Gardens: An Autoethnographic Study on the Soundscape Action Design Tool Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 4648, doi:10.3390/ijerph16234648 . . . **73**

#### **Gunnar Cerw´en and Frans Mossberg**

Implementation of Quiet Areas in Sweden Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 134, doi:10.3390/ijerph16010134 . . . **103**

#### **Karmele Herranz-Pascual, Itziar Aspuru, Ioseba Iraurgi, Alvaro Santander, Jose Luis ´ Eguiguren and Igone Garc´ıa**

Going beyond Quietness: Determining the Emotionally Restorative Effect of Acoustic Environments in Urban Open Public Spaces

Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 1284, doi:10.3390/ijerph16071284 . . . **121**

#### **Francesco Aletta and Jian Kang**

Towards an Urban Vibrancy Model: A Soundscape Approach Reprinted from: *Int. J. Environ. Res. Public Health* **2018**, *15*, 1712, doi:10.3390/ijerph15081712 . . . **141**

#### **Daniel Steele, Edda Bild, Cynthia Tarlao and Catherine Guastavino**

Soundtracking the Public Space: Outcomes of the Musikiosk Soundscape Intervention Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 1865, doi:10.3390/ijerph16101865 . . . **159**

#### **Jieling Xiao and Andrew Hilton**

An Investigation of Soundscape Factors Influencing Perceptions of Square Dancing in Urban Streets: A Case Study in a County Level City in China Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 840, doi:10.3390/ijerph16050840 . . . **197**

#### **Luis Hermida, Ignacio Pav ´on, Antonio Carlos Lobo Soares and J. Luis Bento-Coelho**

On the Person-Place Interaction and Its Relationship with the Responses/Outcomes of Listeners of Urban Soundscape (Compared Cases of Lisbon and Bogota): Contextual and ´ Semiotic Aspects

Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 551, doi:10.3390/ijerph16040551 . . . **213**

#### **Francesco Aletta, Tin Oberman and Jian Kang**

Associations between Positive Health-Related Effects and Soundscapes Perceptual Constructs: A Systematic Review Reprinted from: *Int. J. Environ. Res. Public Health* **2018**, *15*, 2392, doi:10.3390/ijerph15112392 . . . **235**

#### **Mercede Erfanian, Andrew J. Mitchell, Jian Kang and Francesco Aletta**

The Psychophysiological Implications of Soundscape: A Systematic Review of Empirical Literature and a Research Agenda Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 3533, doi:10.3390/ijerph16193533 . . . **251**

#### **Armin Taghipour, Tessa Sievers and Kurt Eggenschwiler**

Acoustic Comfort in Virtual Inner Yards with Various Building Facades Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 249, doi:10.3390/ijerph16020249 . . . **271**

#### **Sonja Di Blasio, Louena Shtrepi, Giuseppina Emma Puglisi and Arianna Astolfi**

A Cross-Sectional Survey on the Impact of Irrelevant Speech Noise on Annoyance, Mental Health and Well-being, Performance and Occupants' Behavior in Shared and Open-Plan Offices Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 280, doi:10.3390/ijerph16020280 . . . **291**

#### **Shan Shu and Hui Ma**

Restorative Effects of Classroom Soundscapes on Children's Cognitive Performance Reprinted from: *Int. J. Environ. Res. Public Health* **2019**, *16*, 293, doi:10.3390/ijerph16020293 . . . **309**


**Abstract**

The synthesis and applications of the peptides are gaining increasing popularity as a result of the developments in biotechnology and bioengineering areas and for a number of research purposes including cancer diagnosis and treatment, antibiotic drug development, epitope mapping, production of antibodies, and vaccine design. The use of synthetic peptides approved by the health authorities for vaccine, for cancer, and in drug delivery systems is increasing with these developments. The aim of this book chapter is to review the recent developments in the use of peptides in the diagnosis of drug and vaccine systems and to present them to the reader with commercially available illustrations.

**Keywords:** synthetic peptide, solid-phase peptide synthesis (SSPS), peptide therapeutics, peptide vaccines, cell-penetrating peptides


*Article* **Relationship between the Use of Social Networks and Mistrust of Mass Media among Mexican Youth: A Mixed-Methods and NLP Study**

**Daniel Javier de la Garza Montemayor 1,\* and Xunaxhi Monserrat Pineda Rasgado 2**

**Abstract:** The relationship between trust and media consumption has been a subject continually studied in communication sciences. There are various studies that indicate that the greater the confidence in a certain medium, the greater the consumption. However, due to the consolidation of digital media and specifically social networks as the main sources of information among the young, the question of whether trust in these media affects their consumption arises again. To examine this relationship, a study was carried out with a mixed methodology. On the one hand, a survey was carried out in which young Mexican university students were asked about the media that they trusted the most and those that they used the most. On the other hand, interviews were conducted with university professors regarding their observations of the behavior of students in relation to usage and trust in these media. In general, the results allow us to conclude that trust in the media is not a variable that impacts the consultation of a specific media outlet given that, despite mistrust, there may be a high rate of usage.

**Keywords:** trust; social media; internet; youth; information

#### **1. Introduction**

The COVID-19 pandemic caused a transformation in practically all sectors of human life, and particularly made digital media essential for carrying out work, social, economic, and cultural activities. This makes the study of social networks increasingly relevant in the social sciences, and it is necessary to approach the subject matter from different perspectives (De la Garza and Peña-Ramos 2022). At first, these media seemed to bring with them a positive element towards democratization due to the use they were given in certain movements and social outbreaks such as the Arab Spring, 15M in Spain, or the #YoSoy132 movement (Candón 2013). Because of this, several investigations focused on the relationship between the use of electronic media and political participation.

However, today we have seen events in the world that have questioned whether the influence of digital media continues to positively strengthen democracy. Therefore, new questions have arisen along with different angles of research that need to be explored (Hall et al. 2018). The main objective of this investigation is to determine if pre-existing trust in media is a variable that determines the consumption of political content, whether traditional or digital. This approach is necessary as currently informational biases, as well as misinformation, have had unexpected impacts on social life.

#### **2. Literature Review**

#### *2.1. Confidence and Consumption of Conventional and Unconventional Media*

On the issue of trust, conclusions have been reached that trust in a medium is essential for an individual's decision to continue its usage (Gainous et al. 2019). If people trust a

**Citation:** de la Garza Montemayor, Daniel Javier, and Xunaxhi Monserrat Pineda Rasgado. 2023. Relationship between the Use of Social Networks and Mistrust of Mass Media among Mexican Youth: A Mixed-Methods and NLP Study. *Social Sciences* 12: 179. https://doi.org/10.3390/ socsci12030179

Academic Editors: Concha Pérez Curiel and Rubén Rivas-de-Roca

Received: 13 January 2023 Revised: 3 March 2023 Accepted: 6 March 2023 Published: 15 March 2023

**Copyright:** © 2023 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/).

medium, they tend to consume more information from it. This can have an implication in the vision that citizens have about democracy, and potentially in their decision to act on matters of a public nature.

This process, ironically, is stronger in places where democracy has limitations. In times of growing political polarization and the emergence of populist alternatives on both the left and right, research has shown that those citizens who support anti-establishment alternatives distrust traditional media more than they do virtual media (Liu et al. 2020). But among those citizens who maintain more moderate positions, they usually value journalistic aspects (in all types of media) such as political analysis and the questioning of the excesses of public power. But the information from previous studies can only apply to a specific place. Arguably, during the presidential election in Mexico in 2018, social media did not damage trust in institutions in a greater proportion than traditional media, as was expected (Echeverría and Mani 2020).

However, another study agrees that the credibility that people grant to a certain medium corresponds to the journalistic credibility it has (such as the presence of sources) (Llamero et al. 2019). In a context of wide polarization, users may be motivated to participate (or comment) in media that have a different ideological position than their own, because in this way, they consider that they can balance the information.

There are similarities with research carried out during the confinement in Spain during COVID-19, where it was found that the information that citizens consume more, and also consider more reliable, come from those media that express similar ideologies to their own (López et al. 2020). There may come a situation in which citizens' distrust of the information they receive becomes unanimous. This is a scenario in which people declare that they distrust all sources of information that are presented, representing a deep crisis of institutional trust, which ends up transcending ideologies and political parties across the spectrum (Gualda and Rúas 2019; Marques et al. 2022).

The trust that users have in the same social media is a factor that influences the loyalty that they may have to a particular site. This factor is so important that some argue that trust plays an important mediating role between user loyalty and the satisfaction they get from the site (Sadiq et al. 2020). Without the presence of trust, there is no relationship between the other two factors.

#### *2.2. Media and Perceptions*

Media generate perceptions that influence how people think. Even when we perceive a transition in which mass media are progressively losing their monopoly on influencing public opinion, a study shows us that they still maintain an important influence (Jacu ´nski et al. 2019). On the other hand, social media have had an enormous responsibility building the perceptions that users have of mass media. An investigation concluded that, during the presidential elections in the United States in 2016, social networks contributed to the questioning of the credibility of traditional media (Weeks et al. 2019).

But these conclusions differ from another study that argues that political polarization is the consequence of audiences that consider that the information they receive from the media with which they maintain an ideological identification is the most reliable. To the extent that a medium demonstrates a different narrative, audiences tend to distrust the source (Kelly 2019; Cetina and Martínez 2019).

This situation is valid for both traditional and digital media. While in the past it was an editorial stance that can reinforce the convictions of viewers, today, platforms such as YouTube work in a similar way. The algorithm that suggests content to users usually recommends videos that are in the same argumentative line as those that have been previously selected. The suggested alternatives strengthen the preconceived perceptions of citizens who use this social network (Lin et al. 2016; Vihalemm et al. 2019).

There is a study that reinforces this position. It maintains that there are some audiences that interpret the information in the following way: If what a medium describes is aligned with their convictions, they choose to consider that it behaves with professionalism and objectivity. Otherwise, they might choose to have a critical attitude towards the information presented (Javaid and Elahi 2014).

It is also possible to argue that the transition from communication media to digital media entails a significant risk because the younger generations, who largely consume digital media, are not used to checking the news they receive (Catalina-García et al. 2019). This could represent a serious problem, since the perceptions obtained about what is happening around them come from sources that, in many cases, are weak (Sup and Kaye 2019).

#### *2.3. Social Networks as an Informative Medium*

As part of the transition of mass media that has occurred in recent years, it has been noted that social networks are a means of communication that inform citizens about relevant information. According to a study, there is a positive relationship between the usage of social networks and knowledge about political issues (David et al. 2019). Following the same logic, the conclusions of another study are similar, as it shows that users who consult social media for news have a greater knowledge of issues of public interest (Kim and Dennis 2019; Mohamed et al. 2020).

The researchers found a clear differentiation between those who use traditional media for public affairs and those who consult social networks, in which the second group was more politicized than the first. It is also important to recognize that social media can help some users persuade others about a specific agenda. This influence is more noticeable when the users receive information from friends and family (Dewi and Satyawan 2022).

One of the main differences between digital and traditional media has to do with the plurality of voices that are recognizable in the dissemination and positioning of specific information. In the view of some, the traditional media were more efficient in setting the public agenda, while in the time of social networks, more actors are involved in the interpretation and spreading of information (Yerlikaya 2020; Arianto et al. 2019).

As social media have become a relevant and, in many cases, even a primary source, awareness of how they can contribute to misinformation has increased. Acknowledging a new reality also implies noticing some of the main risks presented by digital media (Tucker et al. 2017). These include practices related to information verification methods (Fink and Gilich 2020; Domínguez 2015). Therefore, in current times, the change in the consumption of information on politics is not questioned, but there are doubts about the challenges that arise with this new reality. Fake news and misinformation constitute a serious challenge for democracy (Moreno and Ziritt 2019).

#### *2.4. Young People and Consumption of Social Networks*

The importance of social networks has been progressively recognized as a tool used by young people to organize themselves and discuss public affairs (Rubio-Romero and Espinosa 2015; Domínguez 2015). These media have enabled interaction that can be both collective and individual. These media have also changed civic involvement, giving rise to movements that have a different dynamic from those of the past (Alonso López and Bolinches 2020).

Due to its popular use among youth, WhatsApp has been found to be one of the applications most valued by young people because it allows them to make contact in a timely manner (Huang et al. 2021). There are also studies that indicate that use has been given to Instagram as a platform capable of contributing to the expansion of knowledge of students by allowing them to continue to learn about subjects that interest them in a didactic manner (Fuster-Gullén et al. 2020). This is mainly because young people value interacting in a very visual way (Bustamante Pavez 2017).

There are similarities with an experiment in which it was confirmed that social networks can contribute to improving student learning by connecting them with hypertext links (Gavilán et al. 2017). In this way, students can encounter different sources of information, which is something that favors the fact that they can delve into the topics they investigate. From a commercial point of view, brands that seek to connect with young

people through digital media have had to employ a different language from the one used in a traditional marketing strategy. A study pointed out how young people tend to pay more attention to those brands that behave like another user than to a company that only seeks to promote its products (Cano et al. 2017).

Digital media have also functioned as a platform for activism and to defend social causes. However, the results of one study show that these cases are generally a minority (Hoffmann and Lutz 2021). Most of the Spanish students that were surveyed remain skeptical of actively participating in social media or expressing themselves on these issues. These results are largely consistent with other research in which young people remain skeptical of participating in politics, even when they have extensive digital resources at their disposal (Serrano and Serrano 2014; Valerio and Serna 2018).

However, it is also important to note that social media can also have a negative effect on the young. In another survey carried out, it was reported that a significant proportion of young people surveyed had suffered situations of harassment through social networks (De la Garza et al. 2019). Social media can also end up generating a sense of disconnection among youth (Díaz et al. 2013).

#### **3. Materials and Methods**

The main objective of this paper is to analyze the relationship between the variables of consumption and trust in media based on a data analysis of a polling instrument applied to young university students between the ages of 18 and 25 during the 2018 electoral period in Mexico. Likewise, it seeks to describe the patterns of consumption and trust, specifically observing the relationship between the level of consumption of digital media and the degree of trust or distrust in them. For this, a hypothesis test was performed using the Mann–Whitney U statistic.

In the same way, we applied text mining to analyze a group of interviews carried out with professors where they were asked for their observations regarding the same phenomenon, consumption, and trust in the media of their students. The analysis was carried out by applying computational artificial intelligence (AI) techniques, known as Natural Language Processing (NLP), with the aim of obtaining new empirical conclusions through mathematical models of Machine Learning, and thus assess and propose the use of these new tools for the analysis of qualitative data in social research in Spanish. For both approaches, the Jupyter Notebook computing platform was used, and the programming language used was Python in its version 3.9.7. Based on previous research, it is advisable to have more than a single point of view regarding perceptions on the use of technology. Therefore, it can be argued that it is useful to have both students and professors' testimony, because students are the actors of this transition, but professors have been a witness of this process.

On the one hand, students provide valuable information about their own behaviors and perceptions of the media, which allows for a deeper understanding of the degree of trust that influences their consumption of political information. On the other hand, teachers can provide a more objective and observational perspective of their students. Since teachers interact with students in an educational setting, they can provide insight into how students seek and consume political information in a more formal context. In addition, teachers can gain a broader view of trends in media consumption among young people, which can help contextualize the results of the student survey.

#### *3.1. Quantitative Approach*

The data used for this first part were obtained through a survey of university students in which they were presented with a list of media, both traditional and digital, and using a Lickert-type scale, they were asked to indicate the respective level of usage, as well as in which they place greater trust or distrust. The design of the questionnaire took into consideration previous studies on the subject, and it had already been validated in other contexts (Krishnan and Rogers 2014). The sample was non-probabilistic, for convenience, in which 804 young university students residing in the state of Nuevo León, the Valley of Mexico and Oaxaca participated.

The survey was applied through the Google Forms platform, so, the responses were obtained electronically. Likewise, these were applied between 30 March and 27 June of the year 2018, since, according to Mexican electoral legislation, this is the period in which political parties are allowed to carry out electoral campaign acts. For the pre-processing and analysis of the data, the Pandas, Seaborn and Matplotlib libraries were used. For the hypothesis test, mainly the stats module of the Scipy library was used. Media consumption variables were treated as ordinal variables since a Likert-type scale was used for their measurement. On the other hand, the trust variables were treated as nominal, since the possible answers to these measures were: (1) I trust it, (2) I do not trust it and (3) I don't know.

#### Hypothesis

To carry out the hypothesis test concerning the relationship between the variables of consumption and trust in the media, it was first necessary to confirm the type of distribution of the data. For this, the Kolmogórov–Smirnov test as shown in Table 1 was carried out, which establishes the following:

#### **H0.** *The sample follows a normal distribution;*

**H1.** *The sample follows its own distribution.*

**Table 1.** Results of the Kolmogorov–Smirnov test.

According to the results of the normality test, the result of the statistic is equal to 0.916 and the corresponding *p*-value is 0.0. Since the *p*-value is less than 0.05, it is possible to reject the null hypothesis; that is, we have enough evidence to say that the sample data do not come from a normal distribution.

Once confirmed that our data follow a non-normal distribution, it was possible to formulate the hypothesis that allowed us to understand the type of relationship between the variables of consumption and trust in the media. The statistic used was non-parametric, in this case, the Mann–Whitney U test. The approach included the following:

**H0.** *The degree of confidence is not related to the level of media consumption; that is, the variables are independent;*

**H1.** *The degree of confidence is related to the level of media consumption; that is, the variables have a certain measure of dependency.*

#### *3.2. Qualitative Approach*

The data used for this second part of the analysis are textual and come from a series of interviews with 16 research professors: 5 from the state of Nuevo León; 6 from the Valley of Mexico and 5 from the state of Oaxaca, all with more than five years of experience in the classroom and with a field of study in the social sciences. The professors were selected based on the following criteria: they must be Social Sciences professors with at least five years of experience. We wanted to make a match on the perceptions of both actors. The interviews were of a structured type so that they had the same common thread according to the analysis categories established in the survey (TensorFlow n.d.).

The questions were aimed at gathering information that teachers could have observed and perceived in the behavior of their students regarding the two dimensions considered in this study: (1) The level of consumption of traditional and digital media by university students and (2) The degree of confidence they appear to have regarding both categories of media.

Data analysis and preprocessing were performed with the help of Pandas, Re, NLTK and Gensim libraries. The implementation of these tools is part of the study approach called Natural Language Processing (NLP), a field that combines linguistics and artificial intelligence (AI) so that computers can understand human or natural language (Elekes et al. 2020).

The NLP techniques applied to this study comprised 4 main phases: The first consisted of a stage of cleaning and purifying the text of the transcribed interviews. At this point, capital letters, tildes and special characters were removed. In this way, the algorithm could label each of the terms more precisely. In the second phase, a tokenization process was carried out, which consisted of classifying and separating strings of characters into entities called tokens.

For this work, two tokenization processes were considered: the first in sentences and the second in words. At this point, stop words were also eliminated. These are words that do not add value to data labeling, such as prepositions or articles, and therefore do not capture the essence of the words and phrases that help natural language processing. In the third stage, the Wordcloud library was used to create a graph that could proportionally visualize the frequencies of the most recurring words in the text, also known as a word cloud.

Finally, the fourth phase consisted of the embedding of text or Embedding. This is a deep learning technique or Deep Learning that consists of a dense and continuous representation of words in a low-dimensional vector space. The advantage of this vector representation is the possible encoding of general semantic and syntactic relationships between words, assigning similar words to nearby points in the representation space (Elekes et al. 2020).

For this, the Gensim library was imported, and the Word2vec pre-trained neural network was used. This tool converts the words into vectors, and once the algorithm has been trained with our data, it could represent the words according to their context. The export of our model and resulting files were loaded in the Jupyter Notebook and code web application. We use the open embedding projector at http://projector.tensorflow.org (accessed on 26 August 2022). With this tool, it was possible to show the visualization of the embedding work easily and automatically and thus observe the closeness of words according to the cosine similarity.

#### **4. Results**

#### *4.1. Quantitative Results*

According to the results of the survey (Table 2), students state that they have greater distrust in the media that are presented digitally or electronically. The data showed that almost 60% of students distrust the information they receive through emails, followed by social networks (57.6%) and blogs (57.1%). Likewise, it was observed that the media that enjoy greater trust are those considered traditional media. The results show that the media that are most trusted among university students are: the printed magazine (61.1%), the written press (55.5%) and the radio (49.3%). However, this generalization does not extend to television. In this sense, it was observed that, on the contrary, television is the only traditional means of communication to which young people show greater distrust.

**Table 2.** Confidence in the media of university students from Nuevo León, Valle de México and Oaxaca.

N = 804. (1 means "He trusts", 2 means "He does not trust, and 3 means "He does not know").

Regarding consumption, young university students state that the media they consume use the most are social networks, as shown in Table 3, (M = 3.23, STD = 0.93), with 51.37% responding to consult them "Quite a lot" (the highest value they could choose), followed by the digital press (M = 2.44, STD = 1.07) with 32% of young people who say they consult these media "A lot". Regarding the least consumed media, the results were: television (M = 1.32, STD = 0.96), with 43.91% of young people stating that they consume it "Little", followed by the written press (M = 1.34, STD = 0.9), with 41% indicating the same response, in addition to radio (M = 1.31, STD = 1.0), with 39.4% of young people also stating that they consume it "Little". In general, young people consume traditional media to a lesser extent, having a greater preference for digital and electronic sources.

**Table 3.** Media consumption of university students from Nuevo León, Valle de México and Oaxaca.

N = 852. The table shows the percentage of each degree of consumption according to the medium. The minimum value is 0 and the maximum value is 4. There are 5 values: Never (0), Little (1), Somewhat (2), A lot (3) and Quite a lot (4).

#### Hypothesis Test

Table 4 shows the results concerning the previously stated hypothesis test, which seeks to understand the relationship between the consumption and trust variables. Because the p value is greater than 0.05, it is possible to accept the null hypothesis, which states that the degree of confidence is not related to the level of media consumption; that is, the variables are independent.

*4.2. Results of Natural Language Processing*

#### 4.2.1. Word Frequencies

As can be seen in Figures 1 and 2, teachers generally observed that the trust and consumption of university students reside mainly in digital media and specifically in social networks. Throughout the interviews, there was a clear consensus among those interviewed that the main sources of information for young university students are social networks. Furthermore, contrary to what the young people say, their teachers perceive that their trust also lies in said media, since some of them mentioned certain episodes in which their students were victims of misinformation.

**Figure 1.** Frequency of words about trust in the media.

**Figure 2.** Frequency of words about media consumption.

In order to better analyze the results obtained with the embedding process, it was decided to visualize a couple of specific vectors. In other words, from the selection of two specific words, "consumption" and "trust", it was possible to visualize the words that our algorithm was able to relate. Figure 3 shows the vector space and the words close to the word "consumption".

**Figure 3.** Visualization of the "consumption" vector.

#### 4.2.2. Embedding Visualization

In order to better analyze the results obtained with the embedding process, it was decided to visualize a couple of specific vectors. In other words, from the selection of two specific words, "consumption" and "trust", it was possible to visualize the words that our algorithm was able to relate. Figure 3 shows the vector space, and the words close to the word "consumption". Specifically, the closest words are the following: "access", "media", "facebook", "generation", "portals", "news", "students", "time", "prints" and "information".

Likewise, Figure 4 shows the vector space where the word "trust" is highlighted. Specifically, the closest words were: "people", "themes", "media", "portals", "generation", "digital", "headline", "social", "print" and "consumption".

**Figure 4.** Visualization of the "trust" vector.

#### **5. Discussion and Conclusions**

The results of the survey allowed us to anticipate the possible relationship that exists between trust and media consumption. It was observed that, in the case of television, usage was minimal and at the same time a degree of distrust was evident. It was also observed that the radio or the press had a higher degree of trust; however, usage was not higher but, on the contrary, was lower. Likewise, the phenomenon also presented changes in reference to social networks, since despite the clear distrust, these are the media most consumed by Mexican youth.

The results of the U test allowed us to provide more statistical evidence to support the hypothesis that in these times of the digital boom, young natives of the digital world do not consider trust as a factor that determines their consumption decisions. With this evidence regarding the independence between these two variables, the results of the qualitative approach seem to offer a possible assumption that would be worth exploring in future research, which is that the consumption of these digital media, and especially social networks, is not precisely determined by trust, but rather by an aspect of easy access to information that identifies this generation.

In this sense, obtaining the information that young people obtain through digital means seems to be due to a question of optimizing the time and effort involved in searching for and comparing information. Therefore, it is interesting that the embedding model that was trained for this research has related words such as "access", "time" or "header". The teachers stated that they observed that the students had much information at hand on social networks; however, beyond inquiring about the events related in headings, the headings of the notes seemed to be enough to satisfy students' information needs: a fact that may represent a problem affecting young people's ability to distinguish false information and consequently their ability to make informed decisions in the political arena. Finally, it is hoped that the research presented here will serve to motivate the use of these types of computational tools within social research and thus enrich the methods of qualitative data analysis.

The findings confirm the two hypotheses raised for the development of this research. H1 confirms a trend that was visible in past research, among which are those that affirm that there is a change in the consumption of information on politics (Moreno and Ziritt 2019). At the same time, this research coincides with the research that found the positive relationship that exists between consumption of social networks and the politicization of young people (David et al. 2019). As the qualitative results indicate, young people learn about political and social events, even if they initially access platforms for entertainment purposes.

In addition, it is also possible to affirm that the results prove H2, since even though the young people surveyed expressed greater distrust towards digital media, it is precisely these media that are consumed more frequently. Based on the data obtained, it was not possible to confirm other trust findings, since the highest levels of trust that young people expressed, in traditional media, did not correspond to their consumption (Gainous et al. 2019). However, these findings could partially confirm that, despite the progressive decrease in the presence of the mass media, they still maintain an important influence on public opinion due to their migration to social networks (Jacu ´nski et al. 2019). Of course, based on the data collected, the latter does not apply to television, since this was the only medium in which there is a negative correlation in both trust and consumption.

Another important conclusion of this study is that when young people say that they have distrust in social networks, they possibly refer to the electronic medium as such and not to the network of acquaintances with whom they interact in that space and from whom they obtain and relay information. Social networks have already been confirmed as a means of information and communication; however, the dynamics of their operation are very different from the traditional model of the mass media.

As some point out, in the logic of the traditional media, there was only one channel that issued the message that would be received by a wide audience. In this sense, the establishment of a specific editorial position was much easier to identify (Yerlikaya 2020). However, in the dynamics of social networks, there is no clear editorial line, since within the same medium, multiple broadcast channels of content can be presented; therefore, the determining factor turns out to be the interpretation of the information by each user.

Consequently, this phenomenon causes the parameters of trust to be transformed; that is, it causes users to go from trusting an established medium, to trusting a network of contacts and followers who, in turn, also play a role as a channel for generating and distributing information.

Having said this, it would be possible to understand why young people argue that they do not trust social networks, even though qualitative data shows the opposite. In other words, it is possible that when young people say they have distrust towards social networks, they do so by understanding them from a generalized conception of a digital tool. But in the use and consumption of these, the trust they show is not towards the medium, but towards their own network of contacts.

According to this view, users may consider members of their network as reliable sources. However, there is also a risk that a member of your network could distribute false information. As some research reveals, users of social networks are increasingly aware of this risk, so it is possible that the knowledge of the existence of this phenomenon is what makes them mistrust social networks in general. Obviously, this argument gives rise to new studies that would allow us to confirm this approach with greater certainty. In general, it seeks to contribute to and motivate the study of the need to better understand how young people consume political information, since this has a direct impact on democratic systems that function as an important foundation for the making of informed political decisions (Tucker et al. 2017).

**Author Contributions:** Conceptualization, D.J.d.l.G.M.; methodology, D.J.d.l.G.M.; software, X.M.P.R.; validation, D.J.d.l.G.M. and X.M.P.R.; formal analysis, X.M.P.R.; investigation, D.J.d.l.G.M. and X.M.P.R.; resources, D.J.d.l.G.M. and X.M.P.R.; data curation, X.M.P.R.; writing—original draft preparation, D.J.d.l.G.M. and X.M.P.R.; writing—review and editing, D.J.d.l.G.M. and X.M.P.R.; visualization, D.J.d.l.G.M. and X.M.P.R.; supervision, D.J.d.l.G.M. and X.M.P.R.; project administration, D.J.d.l.G.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This study used informed consent for all participants (students and teachers).

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

**Data Availability Statement:** Data on mixed methods is available upon request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

Candón, J. 2013. Movimientos por la democratización de la comunicación: Los casos del 15- M y #Yosoy132. *Razón y Palabra* 82: 21.

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*Rationale*

Figure 1 demonstrates some reported and FDA-approved VEGFR-2 inhibitors such as sorafenib **I** [34], sunitinib **II** [35], vorolanib **III** [36], nintedanib **IV** [37], and toceranib **V** [38]. These drugs have four key pharmacophoric features that must exist in any inhibitor to fit with the VEGFR-2 active site. The four key features are aromatic ring, spacer moiety, pharmacophore moiety (comprising hydrogen bond donor and hydrogen bond acceptor atoms), and hydrophobic group [20,39,40].

Regarding sunitinib **II**, vorolanib **III**, nintedanib **IV**, and toceranib **V,** these drugs possess 1*H*-indole derivatives as hetero aromatic structures that can occupy the hinge region of the VEGFR-2 active site. In all compounds, it was noticed that the 1*H*-indole derivatives share three characteristics. (i) The NH group at the 1-position was kept free without any substitution. (ii) Except nintedanib **IV**, the 5-position was substituted with the hydrophobic (fluoro) group. (iii) The 1*H*-indole moiety was attached to the rest of the structures at the 3-position. Moreover, sunitinib **II**, vorolanib **III**, nintedanib **IV**, and toceranib **V** have an amide moiety as a pharmacophore that binds the DFG motif region and forms essential hydrogen bonds with Asp1044 and Glu833.

Taking these characteristics into consideration, we designed a modified analog that kept the main characteristics of the lead compounds (**II**–**V**). As appeared in Figure 2, the designed molecule has a 1*H*-indole moiety as a hetero aromatic structure that can occupy the hinge region of the VEGFR-2 active site. The 1*H*-indole moiety has a free NH group with a substitution at the 5-position with a hydrophobic methoxy group. In addition, the 1*H*-indole moiety was attached to the rest of the structures at the 3-position as in the lead compounds. Furthermore, the designed molecule has an amide moiety as a pharmacophore that binds the DFG motif region at the VEGFR-2 active site. The linker structure of the designed compound consisted of an *N* -methylene benzohydrazide moiety that can form hydrophobic and hydrophilic interactions at the gatekeeper region. Finally, a plain phenyl ring was utilized as a hydrophobic tail in the designed molecule to form a hydrophobic interaction at the allosteric binding pocket.

**Figure 1.** Sorafenib and some FDA-approved 1*H*-indole derivatives working as VEGFR-2 inhibitors show the essential pharmacophoric features.

**Figure 2.** The design rationale of the targeted compound.


**Acknowledgements**

The authors thank the Scintilla Group, Bangalore, India; Anderson Cancer Institute and Mercer University Medical Center, Savannah, GA, USA; Vanderbilt-Ingram Cancer Center, Nashville, TN, USA; Cedars-Sinai Medical Center, Los Angeles, CA, USA; American Cancer Society, USA; Georgia Cancer Coalition, Atlanta, GA, USA; and NIH for providing opportunity and research grant support. We also thank Dr. Nagendra of MVIT, Bangalore, for assisting us with the STRING software for the analysis and Michigan State University Research Center, Grand Rapids, MI, USA, for generating KKEG pathway using Affymetrix analysis data.