Psychological statements—Direct questions.
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\r\n\t
The range of use of heat exchangers is being expanded to extensive applications in various fields. In particular, supercritical fluids and high‐speed air, that is,, compressible fluids, are suitable as working fluids.
Supercritical fluid is a phase of substances, in addition to the solid, liquid, and gas phases. In particular, in the vicinity of the critical point, many physical properties behave in an unusual way. For example, the density, viscosity, and thermal conductivity drastically change at the critical point, the specific heat and thermal expansion ratios diverge at the critical point, and the sound velocity is zero at the critical point. The physical properties of a supercritical fluid must be evaluated by the appropriate equation of state and equation of the transport properties.
On the other hand, a compressible flow can be assumed as an ideal gas, but additional dynamic energy, that is, the Mach‐number effect, must be considered. Therefore, three types of pressures (static, total, and dynamic), four types of temperatures (static, total, dynamic, and recovery), the difference between laminar and turbulent boundary layers, etc., should be distinguished and treated.
Figure 1 shows the P–T diagram of a pure substance (water in this case), which is also called a phase diagram. The sublimation curve divides the solid and gas phases, the melting curve divides the solid and liquid phases, and the vaporization curve divides the liquid and gas phases. Two phases coexist on these three curves. When the pressure and/or temperature change across these three coexistence curves of solid‐gas, solid‐liquid, and liquid‐gas, the density discontinuously changes. These three coexistence curves meet at the triple point, which is the unique point where solid, liquid, and gas coexist in equilibrium.
Phase chart on P‐T diagram (for water).
The vaporization curve ends at the critical point. On the vaporization curve, liquid is called the saturation liquid, and gas is called the saturation gas (vapor). When approaching the critical point along the vaporization curve, the density of the saturation liquid decreases, and the density of the saturation gas (vapor) increases. Finally, they meet at the critical point. Fluid overtaking the critical point in temperature and pressure is called the “supercritical fluid.”
The phase is thermodynamically determined by the Gibbs free energy
Where
That is, the phase is determined by the balance between the diffusivity caused by the thermal mobility of the molecules and the condensability by intermolecular forces. The diffusivity caused by thermal mobility increases with the temperature. The condensability by intermolecular forces increases with the density. In general, the following relationships hold:
Solid | Diffusivity << condensability |
Liquid | Diffusivity < condensability |
Supercritical fluid | Diffusivity ≈ condensability |
Gas (vapor) | Diffusivity > condensability |
Gas (ideal gas) | Diffusivity >> condensability |
In Figure 1, the first‐order differentials of the Gibbs free energy
are discontinuous across the three coexistence curves, but the first‐order differentials of the Gibbs free energy are continuous at the critical point. In addition, the second‐order differentials of the Gibbs free energy are discontinuous at the critical point.
Here,
Figures 2 and 3 show the isobaric and isothermal changes of the density, viscosity, and kinematic viscosity by using the data from references [2, 3]. Both the density (derived from the equation of state) and the viscosity (derived from the equation of the transport properties) drastically change at the critical point, and the derivatives with respect to temperature and pressure diverge at the critical point. The kinematic viscosity (combined with the density and viscosity) has an extremum value at the critical point. The equations of state and the transport properties should consider these types of tricky features in the vicinity of the critical point for transcritical‐ and supercritical‐fluid flows.
Isobaric changes of the density, viscosity, and kinematic viscosity near the critical point where Tcritical = 304.1282 K and Pcritical = 7.3773 MPa (for carbon dioxide).
Isothermal changes of the density, viscosity, and kinematic viscosity near the critical point where Tcritical = 304.1282 K and Pcritical = 7.3773 MPa (for carbon dioxide).
The most important substances in practical applications are carbon dioxide and water, although all substances have a supercritical‐fluid phase. Recently, accurate correlations for the equations of state and the transport properties containing the critical point have been proposed.
For carbon dioxide, Span and Wagner proposed the equation of state from the triple point to 1100 K at pressures up to 800 MPa [2]. Their equation of state is briefly introduced here. They expressed the fundamental equation in the form of the Helmholtz energy A:
with two independent variables—the density
Where
Pressure
Entropy
Internal energy
Isochoric specific heat
Enthalpy
Isobaric specific heat
Saturated specific heat
then,
Speed of sound
etc.
Here,
For carbon dioxide, Vesovic et al. proposed transport properties in the temperature range of 200–1500 K for the viscosity
For water, Wagner and Pruß proposed the equation of state for the temperature range of 251.2–1273 K and pressures up to 1000 MPa [4]. Huber et al. proposed the transport properties from the melting temperature to 1173 K at 1000 MPa [5, 6].
As mentioned in Section 2, the kinematic viscosity of a supercritical fluid is less than those of a liquid and gas; therefore, the Reynolds number, Re, of a supercritical fluid flow is higher than those of a liquid and gas flow with the same velocity, and a turbulent flow is easily formed. For heat transfer in a turbulent flow, Dittus and Boelter proposed a correlation of the Nusselt number using the
Heat transfer between a supercritical fluid flow and a circular solid tube wall.
Here, the superscript
Here,
Ito et al. proposed an airfoil heat exchanger, which is applied between a compressible airflow and a liquid or a supercritical fluid flow [9]. It has an outer airfoil shape suitable for high‐speed airflow and contains several tubes for a high‐pressure liquid or a supercritical fluid flow. The researchers installed a cascade of airfoil heat exchangers into a subsonic wind tunnel at a temperature of
These correlations are very simple and similar to the Dittus‐Boelter correlation in Eq. (20) but have sufficient accuracy. Ito et al. used accurate equations of state and the transport properties, as mentioned in Section 2. They said in reference [9] that ordinary correlations (of course, containing the Dittus‐Boelter correlation) for liquid and gas can be used when sufficiently accurate equations of state and the transport properties are used. However, the physical properties at a temperature and pressure in the vicinity of the critical point continuously change throughout the tube because of the heat input and/or pressure loss; therefore, changes in these physical properties throughout the tube should be sufficiently considered. For example, the present author recommends the numerical integration of local heat transfer correlations using local accurate physical properties for the entire tube.
A stationary fluid pressure of
The “pressure” (often called “static pressure”)
in addition to
Here,
Next, we consider thermal energy. A stationary fluid at an isochoric specific heat of
Here, the internal energy is an index of the thermal energy level contained in a stationary fluid. In the case of a constant
The “temperature” (often called “static temperature”)
In cases where a fluid is assumed as an ideal gas,
where
A stationary fluid at an isochoric specific heat of
In the case of a constant
where
Here,
The specific heat ratio
This equation is substituted into Eqs. (40) and (44). Then,
The change in the entropy
When isentropic change
We totally differentiate Eq. (41), obtaining the following:
We substitute the final equation of Eq. (59) and Eq. (45) into the rightmost part of Eq. (55):
We integrate Eqs. (57) and (62):
The sound speed
Eqs. (63) and (41) are substituted into Eq. (65), yielding the following:
The one‐dimensional energy equation of an isentropic flow at an arbitrary cross section is derived by using Eq. (46) as:
When the enthalpy and velocity are
This relationship is true even if cross section 1 corresponds to the stagnant cross section 0 (
Eqs. (44) and (50) are substituted into Eq. (69), yielding the following:
Eq. (66) is substituted into Eq. (70):
We multiply by
At the stagnant cross section 0, the static temperature
where
Figure 5 shows the pressure distribution on a plane and an airfoil. On both the plane and the airfoil, boundary layers are formed. The pressure
Pressure distributions of flows on a plane and an airfoil.
The pressure distribution on a solid wall is usually expressed by pressure coefficient
but is sometimes expressed by another pressure coefficient
The two expressions are related as follows:
On a plane,
Figure 6 shows the temperature distribution on an adiabatic plane and an airfoil. In flows on an adiabatic wall, the total temperature
Temperature distributions of flows on an adiabatic plane and an airfoil.
Here, the static temperature
On an adiabatic plane,
Eckert surveyed and organized the heat transfer in a boundary layer in a compressible flow on a wall [10]. In a boundary layer on an adiabatic plane, the adiabatic‐wall temperature reaches
Total‐, static‐, and recovery‐temperature profiles in the vicinity of cooling, adiabatic, and heating solid surfaces with a boundary layer in a compressible flow.
In cases where a thermal boundary layer is completely inside a momentum boundary layer, that is, Pr ≥ 1 the heat generated by the braking effect uses the rise of the static temperature
Here,
where
Here, Eckert\'s theory is extended to the recovery temperature
where
As described in Section 4 4, the local Mach number
the distribution of the local pressure coefficient
Flow field through a cascade of airfoils, where θ is the turning angle.
Ito et al. obtained distributions of
Local Mach‐number distributions assumed from pressure‐coefficient distribution.
Nishiyama described in his book [11] that a developing boundary layer transforms from a laminar boundary layer to a turbulent boundary layer at
Recovery‐temperature distribution assumed according to the pressure coefficient and local Mach number distributions in Figure 9.
Ito et al. evaluated the rate of heat transfer from a hot compressible airflow to a cold supercritical‐fluid flow through an airfoil heat exchanger, as shown in Figure 8 [10]. Heat is transferred from the hot compressible airflow to the outer surface of the airfoil heat exchanger and is conducted from the outer surface to the five inner surfaces in the airfoil heat exchanger. Then, heat is transferred from the five inner surfaces of the airfoil heat exchanger to the cold supercritical‐fluid flow inside the five tubes.
First, Ito et al. conducted wind‐tunnel experiments. They installed
Second, they assumed
Third, they performed an inverse heat‐conduction analysis. The boundary conditions were set according to the experimental results for the distribution of the recovery temperature using the methods described in Sections 4.6, as well as the inlet supercritical‐fluid temperature and pressure. Using these boundary conditions, heat‐conduction calculations for the airfoil heat exchanger were conducted, and the temperatures at the n points in the airfoil heat exchanger and the outlet supercritical temperature were numerically obtained.
Finally, the
Using these procedures, Ito et al. obtained an air Nusselt number correlation
They also obtained a supercritical‐fluid Nusselt number correlation
Moreover, the heat‐transfer rate
where
Here,
Here,
Here,
Here,
Φ is the ratio of the logarithmic mean temperature difference to the temperature difference between the inlet air temperature and the supercritical‐fluid temperature.
The actual heat‐exchange rate is estimated as
For example, Ito et al. performed cycle calculations for an intercooled and recuperated jet engine employing several pairs of airfoil heat exchangers whose heat‐transfer performance is evaluated by Eqs. (91)–(99) [13].
These examples can be used for a cascade of airfoil heat exchangers; therefore, the air Nusselt number correlation in Eq. (91) or thermal resistance in Eq. (93) might be further modified in the near future according to the progress of research, as knowledge in this field is still developing.
The Nusselt number between supercritical fluid flows and solid walls can be estimated by appropriate conventional correlations using the Reynolds and Prandtl numbers if sufficiently accurate physical properties are used for each local point through the region of supercritical fluid flows. Thus, a numerical integration of local heat flow rate is required when you calculate the entire heat flow rate in a heat exchanger between supercritical fluid flows and solid walls.
The recovery temperature should be considered for the estimation of heat transfer between compressible flows and solid walls. For compressible flows on adiabatic airfoil surfaces, the local recovery temperature varies by each point on the airfoil surface, owing to the accelerating and decelerating effects of the main flow outside of the boundary layer on the airfoil surface. In addition, for compressible flows on cooling and heating airfoil surfaces, the local total temperature on airfoil surfaces in the boundary layer also varies at each point because of cooling and heating effects. The accelerating and decelerating effects can be estimated from the local Mach number distribution on the airfoil shape. The cooling and heating effects can also be estimated when a numerical integration of elapsed variation of the local total temperature along the boundary layer from the leading edge if the detailed solid temperature distribution on the airfoil surface is known. To obtain the detailed solid temperature distribution on the airfoil surface, detailed experimental measurements or an accurate CFD analysis may be required.
To estimate conjugate heat transfer through a practical heat exchanger with a complex shape, one solution is a combination of experimental results in wind tunnel tests and an inverse heat conduction method. The other solution is CFD analysis validated by experimental results in wind tunnel tests. Empirical correlations are very limited for conjugate heat transfer through a practical heat exchanger with complex shape because knowledge in this field is still developing.
The diffusion and market penetration of new technologies are becoming a crucial point for transport system analysts and decision-makers. The main issues are regarding a correct understanding of the phenomena and the simulation of different possible operational scenarios.
Among the several new technologies aiming to let the transportation system be more efficient and sustainable, two main issues continue to be challenging tasks: (a) interpreting and modeling users’ behaviour towards these new technologies and (b) assessing the potential environmental impacts.
Both issues are highly correlated as, without an effective interpretation of users’ behaviour, no reliable estimation of the market penetration, and/or the corresponding impacts could ever be obtained.
Within the cited context, the consumer choice theory based on the random utility theory (RUT) may be considered the more effective and practical approach to model and forecast user’s behaviour, but it is a common opinion that consolidated random utility model (RUM) formulations may lead to neglect the numerous nonquantitative factors that may affect users’ perceptions and behaviors. As a matter of fact, psychological factors, such as attitudes, concerns and perceptions, may play a significant role which should be explicitly modeled. On the other hand, collecting psychological factors could be a time- and cost-consuming activity. Furthermore, real-world applications must rely on theoretical paradigms easily implementable in order to allow the estimation of users’ choices in different technological scenarios.
As clearly stated in the current literature, the propensity to adopt a new technology, and, in particular, an alternative fuel vehicle (AFV), is mainly affected not only by instrumental attributes of the technology of interest (alternative) and of the competing technologies (other alternatives) and by personal attitudes (attitudes) of the consumer (user) not depending on the alternatives but also on the consumer personal feelings and on the socioeconomic context in which he/she lives. Several recent analyses have pointed out the necessity to take into account attributes considering the perceptions and the attitudes of the users. For example, see [1]. The main issues of the literature refer to:
How to better collect information about users’ attitudes, concerns and/or perceptions; an “ad-hoc” survey aiming to “collect” attitudes/perception needs to be designed.
The assessment of the methodology to be pursued in accordance with the RUT [2, 3, 4, 5], which is the richest and by far the most widely used theoretical paradigm for modeling transport-related choices and, more generally, with discrete choice modeling (i.e. models representing choices made among discrete alternatives).
Indeed, even though RUMs models usually adopted in demand modeling are suitable for the representation of the choice process, these are not applicable to represent the perceptions and attitudes [6]. The issue was addressed in the literature through the hybrid choice models (HCM) [7, 8, 9, 10, 11] based on attitude investigations trying to infer the role of psychological factors with latent variables within a discrete choice modeling framework.
In particular, several studies aiming to overstep the boundary of RUMs have been conducted by Ortúzar and Hutt [12] and by McFadden [13], which around the 1980s investigated the possibility to include subjective variables in a discrete choice modeling. Starting from the approach proposed by Jöreskog [14] focusing on the investigation between latent variables and the measurement of the perception indicators, several researchers contributed to the assessment of the methodological framework of hybrid choice models (e.g. see [15, 16, 17]).
The book chapter is organized as follows: Section 2 focuses on modeling overview, while Section 3 focuses on survey design; the quantitative preliminary analyses and the model specification are, respectively, discussed in Sections 4 and 5. The corresponding results of two case studies are displayed and discussed in Sections 2 to 5.
The HCM based on RUT is a discrete choice model which integrates and simultaneously estimates different types of sub-models into a unique structure. If the HCM includes a latent variable model, it is possible to take into account the effects of users’ latent attitudes, perceptions and concerns (i.e. integrated choice and latent variable (ICLV) model).
Adopting the standardized notation for path analysis, Figure 1 introduces the general structure of an ICLV and allows to comprehend the different sub-models that define an ICLV: the latent variable model and the discrete choice model. In particular, the ellipses represent the unobservable (latent) variables, the rectangles represent the observable variables, and the circles represent the error variance or disturbance terms.
Scheme of a hybrid choice model (HCM).
Since the latent variables (attitudes, perceptions and concerns) cannot be directly observed and measured from a revealed choice or a stated preference experiment [18], they have to be modeled and then indirectly identified starting from a set of indicators. The latent variable model allows to identify and measure these unobservable variables as a function of the indicators, in order to include them in a choice model.
Mathematically, a latent variable is treated as a random variable; the latent variable is specified through a structural equation formalizing it as a function of several parameters and a random error term. With regard to the relationship between indicators and latent variables, it can be formalized through a measurement equation, in which each observed psychological indicator is a function of a latent variable and a random error term. In general, each latent variable may be part of more than one measurement equation.
Finally, in accordance with the RUT, the utility
One of the main issues in the specification of an ICLV model consists of the observation and the measurement of the attitudes/concerns/perceptions. The survey design is a crucial part since it should allow to characterize the respondents, to come up with respondents’ attitudes/concerns/perceptions and to measure them.
Usually, a survey is structured in different subsections aiming to collect various information from the respondents. First, (a) socioeconomic and (b) household characteristics have to be inquired; then specific sections aimed to capture users’ attitudes, concerns and perceptions should be specifically designed, for instance, (c) the users’ attitudes and concerns that may influence the willingness to adopt/use/purchase a new technology and (d) the users’ perceptions with regard to the advantages and disadvantages of the technology under investigation. Finally, it is necessary to collect (e) the users’ propensity to adopt/use/purchase a new technology. In this case, different scenarios (usually not real but realistic) should be carefully designed in order to cover the possible range of the involved decisional variables.
Even though the literature is consolidated on survey design with respect to Sections (a), (b) and (e), Sections (c) and (d) need to be specifically discussed.
As introduced before, one of the main issues related to the specification and estimation of an HCM relies on how to collect users’ attitudes (to observe and quantify them). Since attitudes are entities constructed to represent certain underlying response tendencies, they cannot be measured directly, but they could be inferred by studying behaviour which, in turn, might be reasonably assumed to indicate the attitudes themselves.
The behaviour may be one that occurs in a natural setting or in a simulated situation. In general, different approaches to measure attitudes may be pursued:
Direct observation: this approach is based on either observing the actual behaviour of people or directly asking to state their feelings regarding the issue being studied. If the aim of the study requires the collection of information from a large number of individuals, this approach is not very practical. Moreover, the observation of peoples’ behaviour may reveal the direction of the underlying attitude (i.e. whether it is positive or negative), but it cannot as easily indicate the magnitude or strength of the attitude itself, even when the behaviour is the outcome of the attitude being studied.
Direct questioning: this approach consists of asking to a set of individuals what their feelings are, as a self-report technique. It serves only for a limited purpose of classifying respondents as favorable, unfavorable or indifferent towards a psychological object. An underlying issue with this technique is the fact that the individuals may possess certain attitudes and behave accordingly to them but may not be consciously aware of them, providing involuntary false statements to the direct questionings.
This method has two approaches to question the individuals: direct question on the investigated attitude (e.g. how important is the environment) and indirect question (e.g. do you normally buy…).
In general, direct questioning is the most pursued approach since it allows to control the investigated context and requires smaller times and costs. The method application requires the scale of measurement definition. Although the literature has proposed different scales as Thurstone, Likert and Bogardus, the Likert scale [19] has the most flexible, robust and easy to implement scale of measurement.
Latent variables may be classified as attitudes [20, 21], perceptions [22] and concerns [23]:
Attitudes refer to the users’ characteristics and to their approach in real-life society and can be related to the alternatives (alternative-related attitudes) or not (non-alternative-related attitudes). They can be collected through direct or indirect questioning, but indirect questioning seems the most appropriate approach [24, 25].
Perceptions are usually interpreted as alternative-related and refer to the users’ interpretation and reaction to a stimulus [21]. They can be gathered through direct questioning only.
Concerns may be related to a specific problem/issue. They may depend on the choice context (e.g. the concern towards the environment may depend on the specific problem/activity carried out). They can be collected through direct questioning only.
Within the aforementioned conceptual framework, it is suggested to design a direct questioning survey considering two different types of questions to be submitted to the respondents: direct and indirect questions. An overview of two examples of survey design is provided in the following.
A first example refers to the HySolarKit [26] case study. The questionnaire described in this section was designed [24] to investigate the role of latent factors in the choice of a new automotive technology which aims to electrify/hybridize existing vehicles through an aftermarket kit which can be recharged by the grid but also by solar power (the HySolarKit). The experiment was applied to the case study of the Salerno municipality which is the capital city of Salerno province (region of Campania, southern Italy).
The first section of the questionnaire aimed to collect users’ socioeconomic, activity-related attributes and household-vehicle characteristic information; therefore, respondents were provided with direct and indirect questions.
In particular, direct questions were about fuel consumption, vehicle reliability, vehicle design and the environmental impact; indirect questions were about three main latent behaviors: the fuel consumption, the vehicle design and the environmental impact. A detailed description is displayed in Tables 1 and 2.
Indicator | Statement |
---|---|
Qcons | The vehicle fuel consumption significantly influences my choice in purchasing a new car |
Qrel | The vehicle reliability significantly influences my choice in purchasing a new car |
Qdesign | The vehicle design significantly influences my choice in purchasing a new car |
Qenv | The evaluation of the environmental impact significantly influences my choice in purchasing a new car |
Qprice | The price significantly influences my choice in purchasing a new car |
Psychological statements—Direct questions.
Attitude towards fuel consumption | |
---|---|
Indicator | Statement |
Icons1 | The consumption and the energy class significantly influence my choice in purchasing an appliance |
Icons 2 | I am usually attentive to the special offers of electric operators |
Icons 3 | My home bulbs are energy efficient |
Icons 4 | I usually evaluate the car efficiency with respect to the car cost mileage |
Icons 5 | I normally compare the fuel prices among different petrol stations |
Icons 6 | When driving I am not willing to behave to reduce the environmental impacts (my driving behaviour is normally aggressive) |
Attitude towards vehicle design | |
Indicator | Statement |
Idesign1 | When parking I am usually careful to avoid having my car damaged |
Idesign2 | I often read journals of design |
Idesign3 | When furnishing I am willing to buy pieces with modern design features and original details |
Idesign4 | I am willing to go to the body shop mechanic not only for major damages |
Idesign5 | I am willing to install not standard equipment (such as antitheft block shaft) on my own car |
Attitude towards environmental impacts | |
Indicator | Statement |
Ienv1 | I often control the exhaust/emission system of my car |
Ienv2 | In consciously make the separate waste collection |
Ienv3 | I really enjoy spending my free time in parks and green areas to breathe clean area |
Ienv4 | How much do you agree with following sentence: We must act and make decisions to reduce emissions of greenhouse gases |
Ienv5 | How much do you agree with following sentence: The government should invest in low energy impact |
Ienv6 | I am not willing to use the car during weekend to protect the environment and then reduce air pollution |
Psychological statements—Indirect questions.
The questionnaire was completed through a second section based on installation cost scenarios. In particular, each respondent was faced with two scenarios based on different installation costs (ranging from 500 to 4000 €).
Respondents were provided with a brief description of the technology and its main characteristics: how it works, how it is installed, the different performances (e.g. power, acceleration, speed), the environmental and fuel consumption benefits that can be achieved and the operating time. A brief overview is displayed in more detail in Table 3 [27].
Overview of the two alternatives in the choice context.
The second example was about electric vehicle (EV) market penetration [28]. The questionnaire was designed with the aim to investigate the different attributes/determinants that may influence the decision to purchase an electric vehicle.
The first section of the questionnaire aims to gather information about the users’ socioeconomic characteristics, the characteristics of the owned household vehicles and the psychometric indicators of the latent variables. Particularly, two types of attitudes and two types of perceptions were inquired. The investigated attitudes were towards the environment and about the vehicle’s technical features, while the investigated perceptions referred to the advantages and disadvantages of electric vehicles that may affect users’ willingness to purchase them. To this end, several direct/indirect questions were specifically designed adopting a five-point Likert scale (ranging from strongly disagree to strongly agree). The psychological statements used as indicators of those unobservable latent variables are detailed in Table 4.
Attitudes towards the environment | |
---|---|
Indicator | Statement |
F_cons | The vehicle fuel consumption significantly influences my choice in purchasing a new car |
F_poll | I care about the amount of pollution generated by a car when it’s being used |
Attitudes about technical features | |
Indicator | Statement |
F_tech_fea | The vehicle technical features significantly influence my choice in purchasing a new car |
TF_power | I prefer driving a car with a powerful engine |
TF_top_speed | When I am choosing a car, I find myself spending a lot of time checking out differences in terms of top speed (km/h) |
TF_accel | The immediate acceleration increases my driving pleasure |
TF_range | The vehicle range is very significant with respect to my mobility needs in everyday life |
Advantages | |
Indicator | Statement |
ADRed_CO2 | I am interested in EV to contribute to the emissions reduction |
ADEfficiency | Compared to a normal car, EV are superior in terms of energy efficiency |
ADRed_poll | I believe using EV can significantly reduce the acoustic pollution in cities |
ADLess_parts | I prefer having a car with less moving parts |
Disadvantages | |
Indicator | Statement |
DISinfr | When driving an EV, I would always be worried about the lack of charging points |
DISred_fea | Compared to a normal car, EV are inferior in terms of performances |
DISbatt_range | The low range of the battery is a real disadvantage |
Psychological statements of the survey.
The second section of the questionnaire contains the users’ choice behaviour in buying a new car. To this end, a Renault Zoe as the electric alternative and a diesel-fuelled Renault Clio are considered as conventional vehicles (CV).
These two alternatives were compared in terms of their technical features and then hypothesized that the interviewee has a budget enough to buy a new car to be used in urban areas. The monthly cost of buying and fuelling the conventional vehicle is calculated, considering 8-year monthly payments to buy it and an estimated fuel cost to drive it for 10,000 km per year. This results in a total cost of 192 € per month (Table 5).
Comparison of the two alternatives in the choice context.
After the comparison, each respondent was faced with five monthly cost scenario setup for the electric vehicle. They started from the same value calculated for the diesel vehicle (i.e. 192 €/month), and then it was considered that the EV would have cost 10, 20, 30 and 40% more (€211, €230, €250 and €270 per month, respectively).
In the case of HCM, descriptive and statistical analyses of the collected answers regarding the perception indicators are necessary for two main reasons: firstly, they allow supporting the soundness of the experimental design setup; secondly, they allow deriving first correlations between observed behaviour, collected attributes and investigated attitudes/perceptions/concerns. Therefore, results from preliminary analyses may give important insights on the survey robustness and a useful guidance for the model specification. In general, the analyses may be divided in basic preliminary analyses based on the analysis of Cronbach’s alpha which focuses on the evaluation of the internal consistency of the answers and the evaluation of mean and standard deviations and other advanced analysis, as the factor analysis which aims to identify the latent variables. A further and more detailed explanation of the methods is provided below.
The Cronbach’s alpha, α (or coefficient alpha), is a measure of the internal consistency (or reliability) of the responses to multiple questions that are meant to measure a specific latent variable in a survey using a Likert scale. This indicator aims to tell whether the survey was accurately designed, and the questions were not answered randomly.
For each latent variable, it is necessary to have at least two indirect questions. The higher the number of questions, the better the latent variable would be measured. These questions, if possible, should be a mix of “+keyed” and “-keyed”, depending if each one is positively correlated to the latent variable or negatively (Table 6).
Scoring | Very inaccurate | Moderately inaccurate | Neither inaccurate nor accurate | Moderately accurate | Very accurate |
---|---|---|---|---|---|
+ keyed | 1 | 2 | 3 | 4 | 5 |
− keyed | 5 | 4 | 3 | 2 | 1 |
Scores for a five-point Likert scale.
When a survey intends to measure more than one latent variable, it is recommended to alternate their questions. This strategy combined to the mix of “+ keyed” and “− keyed” is employed in order to encourage respondents to be more aware of each item and the response provided and, therefore, increases the probability of gathering valid responding. If not, respondents may realize which latent variable is being measured, and they might tend to answer with the same response if the questions are all equally keyed. In this case, the total variance will be lower, and the relation with other variables in the study will be underestimated.
The Cronbach’s alpha is calculated for each group of questions that measure a specific latent variable. Given
where:
A rule commonly accepted to interpret the values of Cronbach’s alpha when used with a Likert scale is (Table 7):
Cronbach’s alpha | Internal consistency |
---|---|
0.9 ≤ α | Excellent |
0.8 ≤ α < 0.9 | Good |
0.7 ≤ α < 0.8 | Acceptable |
0.6 ≤ α < 0.7 | Questionable |
0.5 ≤ α < 0.6 | Poor |
α < 0.5 | Unacceptable |
Scales of values for the Cronbach’s alpha.
An easy quantitative/qualitative analysis to perform on the data collected consists of calculating the mean value and the standard deviation of the responses to the Likert scale for each question and then evaluating whether the responses are consistent (they have a low deviation) or if the mean value corresponds to the one expected.
Given
In matrix terms
where
Assuming that:
Any solution for the unknown values
With these assumptions, the variance of
where
Given two variables,
The covariances can be calculated as
This shows that the covariance of two variables is equal to the scalar product of their loadings.
With the expressions in Eqs. (5) and (7), it is possible to construct a theoretical variance–covariance matrix, implied by the model’s assumptions. Then, with the data collected in the survey, an observed variance and covariance matrix can be calculated and constructed. If the model’s assumptions are correct, it is possible to estimate the loadings
To extract the first set of loadings and factors from the observed variables, there are different methods. However, principal component analysis (PCA) and common factor analysis (CFA) are the most preferred and most used:
Principal component method, or component factor analysis, determines the loadings
This model can be written as
Common factor analysis: the factors are linear combinations that maximize the common portion of the variance and put them into factors, underlying latent constructs. This method does not include the specific part of the variance to determine the factor, and it is used for structural equation modeling.
This model can be set up as
Once the factors are extracted, their eigenvalues (or characteristic roots) provides the amount of variance explained by every factor out of the total variance. Then, the number of factors is reduced by retaining only those which have an eigenvalue larger than 1, according to Kaiser’s criterion [29].
The factor loadings obtained represent the amount of variance explained by the variable on every factor. In structural equation modeling, a value of 0.7 or higher represents that the factor extracts sufficient variance from that particular variable.
The loading values may be hard to interpret at a first glance. So, in a second step of the analysis, the loadings obtained can be “rotated” in order to arrive at another set of loadings, which renders the values more understandable, while fitting the observed variances and covariances equally. The effect of rotating the factors produces that each variable loads more strongly only on one of the factors and weakly on the other factors, producing the eigenvalues to vary.
There are several rotation methods that provide different solutions, arising to different interpretation. The interpretation of each factor and the number of factors needed are very subjective, and the researcher has the task to identify what is the meaning of each factor (i.e. which is the unknown latent variable hidden in the indicators).
From a general point of view, the rotation methods can be subdivided in orthogonal (when the factors cannot correlate) or oblique (the factors are allowed to correlate). The most common methods within each one of two groups are listed below:
Orthogonal methods
Varimax: it aims to minimize the complexity of each factor by relating them to few variables while discouraging the detection of factors that influence all the variables. It produces the increase of the strongest loading values while decreasing the weaker ones in each factor.
Quartimax: it aims to find a general factor (or a reduced amount of them), on which most variables are loaded to, while minimizing the number of factors needed to explain each variable. This is done by increasing the strongest loading values while decreasing the weaker ones in each variable. This factor structure is usually not helpful to the research purpose.
Equimax: it is a method that attempts to simplify both factors and variables.
Oblique methods
Direct oblimin is the standard method when the factors are allowed to be correlated, resulting in higher eigenvalues, but the interpretability of the factors may be reduced.
Promax is an alternative to the previous one, used for large dataset as it is computationally more efficient.
An overview of the preliminary statistical analysis for the electric vehicle case study1 introduced in Section 3.2 is provided. The proposed example includes analysis of Cronbach’s alpha, mean and standard deviations, factor analysis with principal components as the extraction method and rotated component matrix using the Varimax method.
First of all, it may be observed that Cronbach’s alpha is not consistent for all answers; therefore, the survey reliability is confirmed only in the case of psychological statements referred to the environment, the technical features and the EV’s advantages (i.e. Cronbach’s alpha is higher than 0.5). Among them, a further analysis is provided in terms of mean and standard deviations: with respect to the attitude about the environment, for both statements the mean is higher than 3, and the standard deviation is lower than 1; regarding the attitude about technical features, higher values of mean (higher than 3) and lower values (lower than 1) of standard deviations are observed for the following statements (refer to Section 3.2 for the meaning of the following variables): <F_tech_fea>, <TF_power > and < TF_range>. Finally, the perceptions of EV’s advantages show mean values higher than 3 and standard deviations lower than 1 only for the following statements: <ADRed_CO2>, <ADEfficiency > and < ADRed_poll>. However, even though the Cronbach’s alpha is not satisfying for the perceptions of disadvantages of EVs, the mean values and the standard deviations for all statements (<DISinfr>, <DISred_fea>, <DISbatt_range>) highlight their relevance on users’ behaviour. All significant results are in bold in Table 8.
Attitudes about environment | ||
---|---|---|
Cronbach’s alpha = 0.551 | ||
mean | SD | |
F_cons | 4.32 | 0.71 |
F_poll | 3.13 | 0.98 |
Attitudes about technical features | ||
Cronbach’s alpha = 0.656 | ||
mean | SD | |
F_tech_fea | 3.68 | 0.85 |
TF_power | 3.51 | 0.84 |
TF_top_speed | 2.98 | 0.85 |
TF_accel | 3.14 | 1.00 |
TF_range | 4.53 | 0.73 |
Perceptions of advantages of EVs | ||
Cronbach’s alpha = 0.549 | ||
Mean | SD | |
ADRed_CO2 | 4.11 | 0.82 |
ADEfficiency | 3.42 | 1.08 |
ADRed_poll | 3.62 | 0.97 |
ADLess_parts | 3.01 | 1.14 |
Perceptions of disadvantages of EVs | ||
Cronbach’s alpha = 0.364 | ||
Mean | SD | |
DISinfr | 4.21 | 0.82 |
DISred_fea | 3.25 | 0.94 |
DISbatt_range | 4.05 | 0.87 |
Mean and standard deviations of the responses in the EV study.
The factor analysis carried out through the principal component analysis extraction method, allowed to identify the latent factors correlated to the psychological statements. The components extracted were also rotated using the Varimax method.
The results (significant values are highlighted in bold in Table 9) underline the correlations among the following statements referred to the advantage perceptions <ADRed_CO>, <ADEfficiency > and < ADRed_poll > and all statements regarding the environmental attitude, <F_consumption > and < F_pollution > .
Factor analysis | ||||
---|---|---|---|---|
Variables | Components | Rotated components | ||
1 | 2 | 1 | 2 | |
AD_red_CO2 | 0.721 | −0.306 | 0.773 | 0.125 |
AD_red_poll | 0.601 | −0.018 | 0.751 | −0.156 |
AD_efficiency | 0.553 | −0.532 | 0.519 | 0.305 |
F_consumption | 0.548 | 0.518 | −0.068 | 0.768 |
F_pollution | 0.352 | 0.686 | 0.188 | 0.731 |
Factor analysis of the EV study.
The utility choice function in the hybrid choice model is based on the assumption that each individual is faced with a set of alternatives,
With reference to the
In particular, if
where
Furthermore, let
where
The psychometric indicators that reveal the latent variables may be coded using a Likert scale [19]. These indicators can be considered to be a linear continuous expression of the LV’s or an ordered discrete variable. The first approach has been historically chosen because simpler and more practical with lower computational cost. However, assuming these indicators as continuous variables are in contrast with the real nature of the Likert scale (the Likert scale is a discrete measure) [30], such an approach may introduce some biases in the parameters’ estimation. In recent years, several studies have treated them as discrete variables, but with a higher computational cost [31]. In particular, if the measurement is represented by an ordered discrete variable
where
If the measurements use a Likert scale with M = 5 levels, four parameters
Then, the probability of a given response
For completeness, in the following section, the estimation results related to the HySolarKit and the electric vehicle case study are shown.
In this research report, the model parameters were estimated in accordance with the maximum simulated likelihood statistical approach.
The first results shown in this section refer to the HySolarKit case study. As already anticipated in Section 3.1, the choice set was composed of two alternatives: “install” and “not-install”. In the following the estimation results are presented, distinguishing the choice utility function, the structural equations and the measurement equations.
The utility choice functions were analytically specified in accordance with the following equation:
The results are shown in Table 10. In particular, the estimation results underline the following latent variables as statistically significant: attitudes towards fuel consumption (LV1), towards the vehicle design (LV2) and towards the environment (LV3).
Attributes | Attributes coefficients (betas) | |
---|---|---|
Install | Not-install | |
Age [def: age of the respondent] | +0.160 (+0.960) | |
Master’s degree [def: 1 for users achieved this educational attainment] | + 0.156 (+1.16) | |
ZonRes [def: 0 for users living to the historical centre, 1 if in the outskirts] | +0.0761 (+0.960) | |
CarAge [def: age of the owned car on which the respondent would install the kit] | +0.0272 (+1.55) | |
by Car-Shopping [def: mode choice “Car” and trip purpose “Shopping”] | +0.669 (+1.490) | |
by Car-Personal Services [def: mode choice “Car” and trip purpose “Personal Services”] | +0.192 (+0.53) | |
Δcost [def: In order to compare the scenarios with and without the kit, the financial gain was expressed in terms of weekly costs, rather than yearly costs or life costs based on the year of the vehicle owned by the respondent] | +0.0638 (+8.16) | |
LV1 [def: latent variable representing consumption attitude] | +0.548 (+2.55) | |
LV2 [def: latent variable representing design attitude] | +0.0682 (+0.46) | |
LV3 [def: latent variable representing environment attitude] | +0.104 (+0.98) | |
Statistics | ||
Number of respondents | 1364 | |
Number of observations | 1364 | |
Init-log-likelihood1 | −944.760 | |
Final log-likelihood | −779.81 | |
Rho-square | 0.212 |
Attribute coefficients of the choice model. HySolarKit case study.
in parenthesis the t-test values.
Only the log-likelihood associated with the discrete choice component is considered.
The coefficients related to the parameters in the measurement equation for an ordinal specification are estimated in the considered model. As the measurements are using a Likert scale with seven levels, six parameters
DELTA1 | + 1.46 (+ 38.90) |
DELTA2 | + 1.34 (+ 43.85) |
DELTA3 | + 1.52 (+ 46.11) |
Delta values of the calibrated measurement equations. HySolarKit case study.
in parenthesis the t-test values.
The coefficients in the structural model are analytically represented by the following equation:
This equation shows that each latent variable is a function of an intercept value
The estimation results displayed in below refers to the significant latent variables of the model, representing the attitude towards the fuel consumption (LV1), the vehicle design (LV2) and the environment (LV3) (Table 12).
Structural model | |
---|---|
Attributes | Attribute coefficients (betas) |
LV1: Attitude towards fuel consumption | |
−2.32 (−29.80) | |
ω1 | +0.787 (+16.37) |
by car-shopping [def: mode choice “Car” and trip purpose “Shopping”] | −0.448 (−2.52) |
by car-personal services [def: mode choice “Car” and trip purpose “Personal Services”] | +0.550 (+3.88) |
Master’s degree [def: 1 for users achieved this educational attainment] | +0.128 (+2.22) |
LV2: Attitude towards design issues | |
−3.39 (−42.17) | |
ω2 | +0.299 (+6.96) |
Age [def: age of the respondent] | −0.0955 (−2.37) |
SE_male [def: respondent’s gender (0 = female; 1 = male)] | −0.278 (−6.65) |
LV3: Attitude towards environment | |
— | |
ω3 | +1.34 (+27.18) |
Age [def: age of the respondent] | −1.06 (−15.60) |
SE_male [def: respondent’s gender (0 = female; 1 = male)] | −1.12 (−16.74) |
Coefficients of the calibrated structural model. HySolarKit case study.
in parenthesis the t-test values.
Finally, with regard to the measurement model depending on the perception indicators, they are analytically represented by the following equation:
where each perception indicator is a function of an intercept value
Table 13 shows the coefficients for each perception indicator, which were specified in accordance with the preliminary analyses (not shown for the sake of brevity). In particular, the first latent variable about fuel consumption (LV1) is described by two indicators <Icons0 > and < Icons2>. The second latent variable, about vehicle design (LV2), is described by four perception indicators, <Idesign0>, <Idesign1>, <Idesign3 > and < Idesign4>. Finally, the last latent variable representing the attitudes towards the environment (LV3) is described by four indicators < Ienv0>, < Ienv3>, < Ienv4 > and < Ienv6 > .
Measurement model | |||||
---|---|---|---|---|---|
Fuel consumption | Vehicle design | Environment | |||
Icons0 [def: The vehicle fuel consumption significantly influences my choice in purchasing a new car] | Idesign0 [def: The vehicle design significantly influences my choice in purchasing a new car] | Ienv0 [def: The evaluation of the environmental impact significantly influences my choice in purchasing a new car] | |||
α 10 | +0.567 (+3.46) | α 20 | −1.79 (−2.77) | α 30 | −1.89 (−24.48) |
λ 10 | +0.725 (+11.40) | λ 20 | +0.148 (+0.85) | λ 30 | +0.495 (+14.12) |
ν10 | +0.787 (+16.37) | ν20 | +1.38 (+33.16) | ν30 | +1.18 (+31.06) |
Icons2 [def: I am usually attentive to the special offers of electric operators] | Idesign1 [def: When parking I am usually careful to avoid having my car damaged] | Ienv3 [def: I really enjoy spending my free time in parks and green areas to breathe clean area] | |||
α 12 | 0 | α 21 | 0 | α 33 | −1.36 (−19.03) |
λ 12 | 1 | λ 21 | 1 | λ 33 | +0.729 (+20.82) |
ν12 | 1 | ν21 | 1 | ν33 | +0.983 (+25.99) |
Idesign3 [def: When furnishing I am willing to buy pieces with modern design features and original details] | Ienv4 [def: How much do you agree with following sentence: We must act and make decisions to reduce emissions of greenhouse gases] | ||||
α 23 | +2.79 (+2.72) | α 34 | 0 | ||
λ 23 | +1.60 (+5.72) | λ 34 | 1 | ||
ν23 | +1.43 (+31.24) | ν34 | 1 | ||
Idesign4 [def: I am willing to go to the body shop mechanic not only for major damages] | Ienv6 [def: I am not willing to use the car during weekend to protect the environment and then reduce air pollution] | ||||
α 24 | +2.79 (+2.69) | α 36 | −1.95 (−26.15) | ||
λ 24 | +1.84 (+6.52) | λ 36 | +0.396 (+12.18) | ||
ν24 | +1.65 (+28.17) | ν36 | +1.13 (+31.68) |
Coefficients of the calibrated measurement model. HySolarKit case study.
in parenthesis the t-test values.
In general, the estimation results underline the necessity to introduce two different kinds of questions: direct and indirect questions.
As described in Section 3.2, the considered choice set is composed of two alternatives: the alternative A representing the respondent’s willingness to buy an electric vehicle and the alternative B corresponding to the willingness of buying a conventional (diesel) vehicle. The provided results are referred to an ordered model specification.
In the following the estimation results are presented, distinguishing the choice of utility function, the structural equations and the measurement equations.
The attribute coefficients in the utility choice function are analytically specified by the following equation:
The results are shown in Table 14. In particular, the following attitudes were statistically significant: the attitudes towards the environment (LV1) and the perception of the advantages of EVs (LV2).
Attributes | Attribute coefficients (betas) | |
BUY - EV | BUY - CV | |
VAR_monthly_cost_abs [def: Variation in monthly cost [EUR] between an electric car and a conventional one] | +0.118 | |
(+17.65) | ||
SE_AutoSI [def: 1 if the respondent has at least 1 car in the household] | +0.865 | |
(+2.02) | ||
F_tech_fea [5] [def: The vehicle technical features significantly influence my choice in purchasing a new car (5 = strongly agree)] | +0.451 | |
(+1.67) | ||
DIS_red_fea [5] [def: Compared to a normal car, EV are inferior in terms of performances (5 = strongly agree)] | +0.778 | |
(+2.25) | ||
LV_1 | +2.10 | |
(+11.86) | ||
LV_2 | +0.435 | |
(+1.57) | ||
Statistics | ||
Number of respondents | 1462 | |
Number of observations | 1462 | |
Init-log-likelihood1 | −1013.38 | |
Final log-likelihood | −385.55 | |
Rho-square | 0.620 |
Attribute coefficients of the choice model. EV case study.
in parenthesis the t-test values.
Only the log-likelihood associated with the discrete choice component is considered
The coefficients related to the parameters in the measurement equation for an ordinal specification are estimated in the considered model. As the measurements are using a Likert scale with five levels, four parameters
DELTA1 | 0.531 |
(+30.25) | |
DELTA2 | 1.27 |
(+38.94) |
Delta values of the calibrated measurement equations. EV case study.
in parenthesis the t-test values.
The coefficients in the structural model are analytically represented by the following equation:
The estimation results displayed in Table 16 refer only to the two significant latent variables of the model, standing for the attitudes towards the environment (LV1) and the perception of the advantages of EVs (LV2). In particular, for each latent variable, the table displays the results of the intercept value
Structural model | |
---|---|
Attributes | Attribute coefficients (betas) |
LV1: Attitude towards the environment | |
+1.83 | |
(+33.70) | |
ω1 | +0.119 |
(+3.84) | |
SE_male [def: Interviewee’s gender (0 = female; 1 = male)] | −0.242 |
(−5.41) | |
LV2: Perception of EV’s advantages | |
+0.857 | |
(+11.57) | |
ω2 | +0.584 |
(+9.86) | |
SE_male [def: Interviewee’s gender (0 = female; 1 = male)] | −0.0445 |
(−1.01) | |
SE_delta_age [def: Age class of individuals (0 = 19 years; 1 = 20 years; 2 = 21 years; 3 = 22 years; 4 = 23 years; 5 = 24 years; 6 = 25 years; 7 = 26 years)] | −0.0246 |
(−1.39) |
Coefficients of the calibrated structural model. EV case study.
in parenthesis the t-test values.
Finally, the measurement model depending on the perception indicators is analytically represented by the following equation:
These parameters were specified in accordance with the preliminary analyses. In particular, the first latent variable about the environment (LV1) is described by two indicators, <F_cons > and < F_poll>, while the second latent variable, perception of EV’s advantages (LV2), is described by three perception indicators <ADRed_CO2>, <ADEfficiency > and < ADRed_poll>. In Table 17, the intercept value
Measurement model | |||
---|---|---|---|
LV 1: Environment | LV 2: Perception of EV’s advantages | ||
F_cons [def: The vehicle fuel consumption significantly influences my choice in purchasing a new car] | ADRed_CO2 [def: I am interested in EV to contribute to the emissions reduction] | ||
α 10 | −4.62 (−4.92) | α 20 | +0.391 (+2.96) |
λ 10 | +2.85 (+5.23) | λ 20 | +1.32 (+8.40) |
ν10 | +1.03 (+30.15) | ν20 | +0.742 (+11.55) |
F_poll [def: I care about the amount of pollution generated by a car when it’s being used] | ADEfficiency [def: Compared to a normal car, EV are superior in terms of energy efficiency] | ||
α 11 | 0 | α 21 | −0.143 (−1.37) |
λ 11 | 1 | λ 21 | +0.881 (+7.27) |
ν11 | 1 | ν21 | +1.22 (+27.41) |
ADRed_poll [def: I believe using EV can significantly reduce the acoustic pollution in cities] | |||
α 22 | 0 | ||
λ 22 | 1 | ||
ν22 | 1 |
Coefficients of the calibrated measurement model. EV case study.
in parenthesis the t-test values.
Depending on the context, several factors may affect users’ choices. In this chapter, the main focus refers to modeling users’ propensity to choose/adopt a new/innovative technology. This is a crucial task in order to increase the attractiveness of strategies that may be employed to achieve sustainable transportation. In particular, two related main issues are still open in the literature: (a) interpreting and modeling users’ behaviour towards these new technologies and (b) assessing the potential environmental impacts. It is widely recognized that traditional approaches used to interpret and model users’ choice behaviour may lead to neglect the numerous nonquantitative factors that may affect users’ behaviors. Indeed, users’ choices may be influenced by social and psychological factors, symbolic and affective factors, habits and the conflict between collective and individual interests (e.g. car use as a commons dilemma). These imply that changes in transportation modes may be achieved either by influencing individual motivations and perceptions (psychological strategies) or by changing the context in which decisions are made (structural strategies).
The book chapter provides an overview of the methodology to be adopted in order to support psychological-based strategies. In fact, psychological factors, such as attitudes, concerns and perceptions, may play a significant role which should be explicitly modeled.
Although several approaches may be identified in the literature able to address the above-mentioned issue, the hybrid choice modeling approach based on RUT can be considered a proper solution to explicitly consider the perceptions, attitudes and concerns in the modeling of the choice behaviour. The specification of such models requires a careful survey design, rigorous preliminary descriptive analyses and the model parameter estimation. The present chapter deals with all the cited issues, first introducing the main criticalities in modeling choice behaviour in new technological contexts, then proposing a methodological framework and finally introducing different explanatory examples on real case studies. In particular, Section 3 focuses the attention on the different approaches to collect users’ attitudes/perceptions and concludes the need for a mixed approach based on both direct and indirect questioning. Section 4 introduces the methodology to properly evaluate the consistency of the dataset and the latent variables identification. It evidences the need for basic analysis, such as the estimation of the mean and standard deviations, and the importance of the Cronbach’s alpha test and the principal components and the rotate component matrices for the identification of the latent variables. Section 5 deals with the model’s specification issues, pointing out the most robust approach for the specification and calibration of a hybrid choice model with latent variable. All the introduced sections are supported by real experimental results [24, 25] for explanatory and guideline purposes.
This research was partially supported by the University of Salerno, under local grant no. ORSA171328—2017.
No potential conflict of interest was reported by the authors.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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