*2.5. Cross-Validation*

A common issue in this area of work is the problem of overfitting. A model can theoretically be conditioned to precisely fit the training data, hence exhibiting very high accuracy in-sample. Nonetheless, such a model would be useless in forecasting, as it will likely exhibit a low fit in the test (out-of-sample) data. In such cases, the model is trained to only fit the training data and not the underlying phenomenon. To avoid this, in the empirical part of the study, we employed a *k*-fold cross validation procedure. The in-sample data, which are used to train the model, are divided into *k* parts (folds) of equal size. Then, in each of the *k* iterations, one fold is used as the testing set, while the remaining *k*-1 folds are used as the training set. This is repeated for all *k* folds. In this scheme, the model's accuracy is evaluated by the average performance over all of the *k* folds for each set of the model's parameters. Figure 3 provides a graphical representation of a 3-fold cross validation procedure.

#### *2.6. The Dataset*

For the training and the testing of our models, we compiled a dataset consisting of 2423 daily natural gas spot price values from the Energy Information Administration (EIA) database and 21 related economic variables from the Federal Reserve Bank of Saint Louis and Yahoo Finance databases. They span the period from 3 December 2010 to 18 September 2020 (Table 1). In addition, the momentum of the last 5 and 10 days (Momentum 5 and 10 are defined as the sum of the times that natural gas spot price increases

in the last 5 and 10 days, respectively) as well as the 5- and 10-day moving average was calculated and added to the independent variable set. With the exception of interest rates, all of the variables were converted to natural logarithms.

**Figure 3.** A three-fold cross validation for a given set of model parameter values. Each fold serves as a test sample, while the remaining folds are used to train the model. The average prediction accuracy for each set of parameters over the k-folds is used to assess the model [17].

**Table 1.** List of explanatory variables with mean, standard deviation, skewness, kurtosis, and variance.

In order to test the generalization ability of the trained models, the dataset was divided into two parts: the first 90% was used as the training data set (in-sample, consisting of 2180 observations), and the remaining 10% of the most recent observations was the test data set (out-of-sample, consisting of 243 observations).

#### **3. Empirical Results**

The prediction accuracy of each model for both the out-of-sample and in-sample data was measured using the Root Mean Square Error (RMSE) metric. Thus, the optimal model was selected as the one that minimizes the RMSE:

$$\text{RMSE} = \frac{\sqrt{\sum\_{t=1}^{T} \left(\hat{y}\_t - y\_t\right)^2}}{T} \tag{1}$$

where *yˆ* = the forecasted value, *y* = the actual value, and *T* = the number of observations.

Our forecasts were produced for several alternative forecasting horizons, i.e., *t* + 1, *t* + 3, *t* + 5, and *t* + 10. We completed the same task with a random walk model in order to compare our machine learning results to a naïve prediction model.

Before moving to structural models (The ones that include the independent variables of our data set.), we first tried to identify the best autoregressive representation, i.e., to produce the best AR(q) model (autoregressive model). The AR(q) model is a simple model that uses past (lagged) values of natural gas spot prices to forecast the future natural gas spot price.

$$\mathbf{X}\_t = \mathbf{c} + \sum\_{i=1}^q \varphi\_i \mathbf{X}\_{t-i} + \varepsilon\_t \tag{2}$$

where *X* is the natural gas spot price, *q* is the maximum number of lags, and *ϕi* the parameter vector of the lags to be estimated.

In order to identify the optimal number of lags, we train several linear SVM models by varying the number of lags we used each time, starting with an AR(1) up to an AR(15).

We concluded that by using the first 14 lags, we minimize the in-sample RMSE (0.04196). These results are presented in Figure 4.

**Figure 4.** RMSE for AR models.

After identifying the best autoregressive representation, we built structural models. These include the 14 lags and all of the explanatory variables described earlier as independent variables to produce forecasts one day ahead. For this, we trained several alternative machine learning models and also produced the results for the random walk model. The in-sample and out-of-sample RMSEs of these models are presented in Table 2. An important issue in such forecasting models is to avoid overfitting in the in-sample or out-of-sample datasets. In the literature, this is known as the bias–variance trade-off. An efficient forecasting model is one that provides a balanced performance both in-sample and out-of-sample, i.e., the bias and variance are comparable. For this reason, we rejected all of models that provided evidence of overfitting and continued our empirical analysis with the rest. In the last column of Table 2, we note the models that overfit and are not used in the rest of our analysis. Interestingly, the tree models (plus bagged and boosted trees) do not overfit, and all of the GPR models overfit alongside most of the SVM models (with the exception of the linear SVM).

**Table 2.** In-sample and out-of-sample (OOS) RMSE of all models.

#### *3.1. Time Framet+1*

According to the results presented in Figure 5, we observed that for the time horizon *t* + 1, the optimal in-sample model was the linear regression model with RMSE = 0.038421 and that the best out-of-sample forecasting model was the linear SVM model with RMSE = 0.056694. The robust linear model also showed very good results, as it had the second lowest RMSE in the out-of-sample data and the third lowest in the in-sample data. Finally, the random walk model seemed to adequately predict the out-of-sample data. Therefore, we can generally conclude that linear models are able to predict the natural gas spot prices one day ahead with high accuracy and that the best model (linear regression) has good generalization ability (Figure 6).

#### *3.2. Time Framet+3*

The results for the forecasting window *t* + 3 are presented in Figure 7. We observed that for time horizon *t* + 3, the optimal in-sample model was the bagged trees model with RMSE = 0.057793 and that the best out-of-sample forecasting model was the boosted trees model with RMSE = 0.077136. According to the above, it is clear that the best models at time horizon *t* + 3 are tree based models. The out-of-sample performance of the bagged trees model is presented in Figure 8.

**Figure 5.** RMSE for *t* + 1 forecasting.

**Figure 6.** Comparison of the actual natural gas spot prices and the predicted prices with the linear regression model for *t* + 1 in the out-of-sample part of the dataset.

#### *3.3. Time Framet+5*

The results for the forecasting window at *t* + 5 are presented in Figure 9. In this window, we found that the optimal in-sample model was the bagged trees model with RMSE = 0.061787 and that the best out-of-sample forecasting model was the linear SVM model with RMSE = 0.083687. The bagged trees model also shows good generalization ability (Figure 10). It is worth noting that the random walk model also showed good performance, as it holds the second lowest out-of-sample RMSE = 0.087654.

**Figure 7.** RMSE for *t* + 3 forecasting.

**Figure 8.** Comparison of the actual natural gas spot prices and the predicted prices with the bagged trees model for *t* + 3 in the out-of-sample part of the dataset.

#### *3.4. Time Frame t + 10*

Finally, for the *t* + 10 forecasting window the results are presented in Figure 11. We observed that the optimal in-sample model was the bagged trees model with RMSE = 0.064968 and that the best out-of-sample forecasting model was the linear SVM model with RMSE = 0.102711. Additionally, the random walk model showed good results, as it achieved the second lowest out-of-sample RMSE = 0.109871. The best model for time horizon *t* + 10 (bagged trees) has also good generalization ability (Figure 12).

**Figure 9.** RMSE for *t* + 5 forecasting.

**Figure 10.** Comparison of the actual natural gas spot prices and the predicted prices with the bagged trees model for *t* + 5 in the out-of-sample part of the dataset.

Interestingly the random walk model showed very good results for the out-of-sample part of the dataset at all time instances, while at the same time, we can conclude that all of the linear models have the ability to predict natural gas prices with high accuracy and showed very good performance with small RMSE values. The bagged trees models also showed very good predictive ability, having the lowest in-sample RMSE error at all time instances except for *t* + 1.

**Figure 11.** RMSE for *t* + 10 forecasting.

**Figure 12.** Comparison of the actual natural gas spot prices and the predicted prices with the bagged trees model for *t* + 10 in the out-of-sample part of the dataset.


**2. Contributions highlight**

This book covers every areas mentioned in its title. Except this overview, each chapter contri‐ bution has been briefly highlighted in this section.

Starting with the design of heat exchangers, author Ezgi discussed the basic design methods of two‐fluid heat exchangers. The design techniques of two main types of heat exchangers namely recuperator and regenerators were analyzed and theoretical developments are pre‐ sented in his study. The solutions to recuperator problems are discussed in terms of LMTD, ε‐NTU, dimensionless mean temperature difference (Ψ‐P), and (P1‐P2) methods. It is reported that the rating or sizing problem of recuperator exchangers can be solved by any of these methods and will yield identical solution within the computation error. For example, if inlet temperatures, outlet temperature of one of the fluids, and fluids mass flow rates are known, LMTD method can suitably be employed to solve sizing problem. Otherwise, *ε*‐NTU method needs to be used. As the P1‐P2 method includes all major dimensionless heat exchanger parameters, the solution to the rating and sizing problems is noniterative and straightfor‐ ward. On the other hand, regenerators are basically classified into rotary and fixed matrix models, and the thermal design of these models can be realized by two methods, which are effectiveness‐modified number of transfer units (*ε*‐NTU0 ) and reduced length and reduced period (Λ‐π) methods. Usually, Λ‐π method is used for fixed matrix regenerators.

Authors Silva Rosa and Moraes Junior focused on the heat transfer surfaces in agitated vessels and their study was based on the determination of the heat exchange area which is necessary to abide by the process conditions as mixing quality and efficiency of heat transfer. Based on the overall heat transfer coefficient obtained from Nusselt number, they determined the heat transfer area. The authors also summarized literature studies related to heat transfer in agitated vessels. A numerical analysis was also made to project the heat transfer surface area in an agitated vessel using vertical tube baffles and a 45° pitched blade turbine. It was emphasized that the selection of a suitable type of heat transfer surface for the process to be projected in agitated vessels must be done through an analysis between the kind of impeller and its interaction with the adopted surface.

these heat exchangers are widely used because most of the practical cases fluids cannot be allowed to contact or mix. Common examples of indirect‐contact exchangers are shell and tube, bayonet, concentric tube, plate, spiral plate, radiator, storage or regenerators, and com‐ pact exchangers. On the other hand, two fluids streams come into direct contact with each other and exchange heat before separating. The direct‐contact heat exchangers include mov‐ ing bed contactor, fluidized bed, moving belt conveyor, immiscible fluids, boiling and immis‐

The analysis of heat exchangers is commonly performed through two well‐known methods, which are log mean temperature difference (LMTD) and effectiveness‐number of transfer units (ε‐NTU) [3]. LMTD is easy to use in heat exchanger analysis when the inlet and the out‐ let temperatures of the hot and cold fluids are known or can be determined from the energy balance, and it is very suitable for determining the size of a heat exchanger to realize certain/ required outlet temperature. On the other hand, NTU is directly a measure of the heat transfer surface area, and therefore, the smaller the NTU the smaller the heat exchanger. Heat transfer enhancement in heat exchangers is usually accompanied by increased pressure drop and thus by higher pumping power. Therefore, any gain from the enhancement in heat transfer should

This book covers every areas mentioned in its title. Except this overview, each chapter contri‐

Starting with the design of heat exchangers, author Ezgi discussed the basic design methods of two‐fluid heat exchangers. The design techniques of two main types of heat exchangers namely recuperator and regenerators were analyzed and theoretical developments are pre‐ sented in his study. The solutions to recuperator problems are discussed in terms of LMTD, ε‐NTU, dimensionless mean temperature difference (Ψ‐P), and (P1‐P2) methods. It is reported that the rating or sizing problem of recuperator exchangers can be solved by any of these methods and will yield identical solution within the computation error. For example, if inlet temperatures, outlet temperature of one of the fluids, and fluids mass flow rates are known, LMTD method can suitably be employed to solve sizing problem. Otherwise, *ε*‐NTU method needs to be used. As the P1‐P2 method includes all major dimensionless heat exchanger parameters, the solution to the rating and sizing problems is noniterative and straightfor‐ ward. On the other hand, regenerators are basically classified into rotary and fixed matrix models, and the thermal design of these models can be realized by two methods, which are

) and reduced length and reduced

cible fluids, and cooling tower exchangers.

2 Heat Exchangers– Design, Experiment and Simulation

**2. Contributions highlight**

bution has been briefly highlighted in this section.

effectiveness‐modified number of transfer units (*ε*‐NTU0

period (Λ‐π) methods. Usually, Λ‐π method is used for fixed matrix regenerators.

Authors Silva Rosa and Moraes Junior focused on the heat transfer surfaces in agitated vessels and their study was based on the determination of the heat exchange area which is necessary to abide by the process conditions as mixing quality and efficiency of heat transfer. Based on the overall heat transfer coefficient obtained from Nusselt number, they determined the heat transfer area. The authors also summarized literature studies related to heat transfer

be weighed against the cost of the accompanying pressure drop.

Topology optimization is a useful design tool for physical systems especially for structural ones, and its application in the field of thermal transport is slowly increasing. Topology opti‐ mization is as a physics‐based and automated lay‐out (best material) optimization method, which follows the governing equations taken into consideration under a user‐defined set of conditions and limitations. Authors Christian and Lin re‐introduced this topology opti‐ mization with special focus on the progress of heat exchanger design. In order to assess the current progress of topology optimization in the field of heat transfer and heat exchanger design, they provided a chronological review of available literature. Then, they have con‐ ceptually introduced different methods developed over the years in topology optimization. Heat exchanger designs arising from topology optimization have now been realized, and continuous efforts are still being made to further improve both methods and implementa‐ tion. Topology optimization is expected to play a bigger role in the near future for heat exchanger design.

Isafiade and Bahadori reported a new simplified synthesis method for designing small‐sized to medium‐sized flexible heat exchanger networks using a mixed integer nonlinear program‐ ming multi‐period synthesis approach. The methodology used involves a two‐step approach where in the first step, a multi‐period network is designed for a large number of critical oper‐ ating periods using a finite set of operating points while considering the impact of potential fluctuations in periodic durations of each of the chosen critical points on the network. In the second step, the flexibility of the resulting multi‐period network of the first step is tested using very large randomly generated set of finite potential operating points together with their periodic durations. The solutions obtained in their study using this new approach were compared favorably with those in the literature.

In a different study, author Naik highlighted issues related to the thermal design of gas tur‐ bine blades and examined several heat transfer technologies. Some of the basic heat transfer phenomenon associated with both the external hot gas side and the coolant's internal flows in turbine aerofoils has been discussed. By establishing the hot gas side external heat loads, which generally varies with different turbine design, it is possible to design efficient aerofoil internal cooling systems. Typical methods for validating the thermal designs of gas turbine aerofoils were also outlined.

As mentioned before, direct contact heat exchangers (DCHE) involve the exchange of heat between two immiscible fluids by bringing them into contact at different temperatures. Among some advantages of direct‐contact heat exchangers over indirect contact ones include achievable very high heat transfer rates, relatively inexpensive exchanger construction and absence of the fouling problems due to not having any wall between the two fluids. The direct‐contact heat exchangers have been comprehensively studied by Wang and co‐workers. Based on algebraic topology, they have developed a new technique for quantifying the effi‐ ciency of multiphase mixing. A novel method relying on image analysis and statistics was also developed to accurately estimate the mixing time in a DCHE. L2‐star discrepancy (UC‐ LD)–based uniformity coefficient (UC) method presented for assessing the uniformity and mixing time of bubbles behind the viewing windows in a DCHE was found to be effective. Furthermore, they have shown some advantages including rotation invariance (reflection invariance), permutation invariance, and the ability to measure projection uniformity. Their experimental results revealed that UC‐CD gives more sensitive performance than uniformity coefficient based on wrap‐around discrepancy (UC‐WD) and thus, the UC‐CD method is more appropriate for industry. Nonetheless, authors believe that the complexity of the bubble swarm patterns can be reduced, their mechanisms can be clarified, and the heat transfer per‐ formance in a DCHE can be elucidated.

Author Jaremkiewicz described a method for measuring the transient temperature of the flowing fluid in heat exchangers based on time‐temperature changes of the thermometer, which was considered as an inertial system of first and second order. To reduce the influence of random errors in the temperature measurement, the local polynomial approximation based on nine points was used. As a result, the first and second derivatives of a temperature that indicates how the temperature of the thermometer varies over time were determined very accurately. Then, the time constant was defined as a function of fluid velocity for sheathed thermocouples with different diameters. The applicability of their method was validated with real experimental data. They inferred that this method is mostly suitable for measuring the transient temperature of gases in the exchangers, and it can also be used for the online moni‐ toring of fluid temperature change with time.

A detailed description and comprehensive review of the transient effectiveness methodology for heat exchanger analysis are reported by authors Tianyi and co‐workers. Three important applications for transient effectiveness methodology are reported that include (i) character‐ ization of heat exchanger dynamic behaviors, (ii) characterization of the transient response of closed coupled cooling/heating systems with multiple heat exchanger units, and (iii) devel‐ opment of compact transient heat exchanger models. For studying heat exchangers' tran‐ sient characteristics, authors introduced novel transient effectiveness methodologies, which were found very useful for thermal dynamic characterization of heat exchangers as well as development of compact CFD transient models. The transient effectiveness curves capture the transient response and the impact of thermal capacitance of each heat exchanger unit. The two CFD compact modeling methodologies (i.e., a full transient effectiveness methodology and a partial transient effectiveness methodology) were also developed and validated in their study. These models were found to be accurate and fast and can be integrated into large‐scale models as well.

Salcedo and co‐authors carried out numerical simulations to study the unsteady laminar flow and mixed convection heat transfer characteristics around two identical isothermal semicyl‐ inders arranged in tandem and confined in a channel. Simulations were performed using the control‐volume method on a nonuniform orthogonal Cartesian grid. The immersed‐bound‐ ary method was employed to identify the semicylinders inside the channel. The variation of the mean and instantaneous nondimensional velocity, vorticity and temperature distribu‐ tions with Richardson number were presented along with the nondimensional oscillation frequencies (Strouhal number) and phase‐space portraits of flow oscillation from each semicylinder. In addition, local and averaged Nusselt numbers (Nu) over the surface of the semicylinders were also obtained. Their results demonstrated how the buoyancy and wall confinement affect the wake structure, vortex dynamics, and heat transfer characteristics.

Authors Çetin and co‐workers reported a study on computational modeling of vehicle radia‐ tors using porous medium approach in the last contribution. They believe that a successful implementation of such porous modeling can lead to a dramatic reduction in computational cost and time. The implementation of the computational methodology through a commercial software can also benefit from the powerful meshing, solving, and postprocessing capabili‐ ties. As demonstrated, a CFD analysis of a radiator by using porous medium approach gave reasonable and reliable results. Using CFD analysis, design cost may be decreased dramati‐ cally by easing the experimental testing process. They reported that the porous parameters of a given fin geometry can be obtained within a couple of hours which may enable the hydro‐ dynamic and thermal optimization of a radiator. Although the proposed methodology was discussed in the context of a vehicle radiator, it can be implemented to any compact heat exchanger with repetitive fin structures, which is an important problem for many industrial applications. Authors suggested that more realistic computational models may be developed, and the coupling of the flow and temperature field with the structural analysis may lead to way more efficient and robust radiator designs.


**5. Conclusion**

At the end of this discussion of the transformations a local community undertook in order to keep hold of money paid as compensation, one can draw a couple of conclusions. First, corporations impose changes on local communities not only by their physical presence in the locality but also in other intangible ways. One of such ways is the transformation of sociocultural practices of the local communities. This is because the recommendations of codes of corporate governance is hardly elaborate enough as to provide for the prevention of such impositions. The failure of the corporate governance discourse in general to recognize what is expected of key

participants in the relationship between companies, individuals and states in terms of values and behaviors exacerbates this tendency.

Secondly, in a bid to cope with changes imposed by corporations, communities can regenerate their creative capacity, especially when there are materials and tools that can be fixed together to produce something desirable. While this tendencies of communities are commendable, corporations need not take it for granted that such creative tendencies can be found in all local communities. Where such tendencies are difficult to come by, reactions to impositions can be unpredictable. The onus is on corporate governance codes to dispose companies towards flexibility in value and behavior to accommodate situations where impositions can engender violent rather than creative reactions.


**Algorithm for Diagonalizing a Matrix**

Find *n* linearly independent eigenvectors of *A,* marked as **x**1 , **x**2 , ⋯, **x***n*.

Form the matrix *P* having **x**1 , **x**2 , ⋯, **x***n*., at its column vectors.

The matrix *P*−**1** *AP* then will be diagonal with *λ*1, *λ*2, ⋯, *λn* successive diagonal entries, where *λi* is eigenvalue corresponding to **x**i , for *i* = 1, 2, ⋯, *n*.


**3. Stigma in previous pandemics**

Throughout history, human beings have been exposed to dangerous diseases that force them to change their behavior to adapt to new conditions. WHO has defined a pandemic as "the worldwide spread of a new disease". From smallpox of the 19th century to COVID-19 of the 21st century, epidemics and pandemics have always been associated with stigma and serious social consequences [12]. Apart from natural disasters or wars affecting a certain geographical region, infectious diseases affect the whole world and humanity without borders, as we witnessed in the COVID-19 epidemic. To date, there have been 21 pandemics affecting humanity. The most wellknown and most severe of these is the plague epidemic that emerged in the fourteenth century. In previous studies, it was reported that the population of the whole world decreased by 1/4 in the plague pandemic and the population of many important cities was completely destroyed [13, 14]. Other major pandemics are Spanish Flu (1918–1920), HIV epidemic, Smallpox in the former Yugoslavia (1972), severe acute respiratory syndrome (SARS) (2003), "Swine Flu" or H1N1/09 (2009), Middle East Respiratory Syndrome (MERS), Ebola (2014–2016), and ZIKA (2015–2016) pandemics. These pandemics have caused many casualties [15]. There are differences between the first known pandemics and more recent pandemics because during the first pandemics the population was independent of each other, that is, isolated. However, in the present times when human mobility has increased, the significant increase in interregional and even intercontinental communication and interaction has changed the course of today's pandemics. On the other hand, the development of transportation and communication in the global arena and increased contact with a different human, animal, and ecosystem populations facilitated the spread of the pandemic [16]. Medical stigma is seen in almost every period of history. Diseases such as leprosy, plague, syphilis, tuberculosis, cancer, AIDS, which affect societies, are the diseases that cause stigmatization [17, 18]. As diseases such as tuberculosis and syphilis became treatable, stigmatizing attitudes towards these diseases decreased over

time [19]. Leprosy, which has a history as old as human history and is one of the first stigmatized diseases, was described as an "evil" given to man by God [20]. Those who contracted syphilis in the fifteenth century were condemned by society. Tuberculosis, named in the eighteenth century, was seen as a disease belonging to the lower class. Since the 1900s, with the recognition of cancer types and the increasing number of people receiving this diagnosis, prejudiced behaviors have been made against cancer patients, and discriminatory approaches against cancer patients have continued until the last 20 years. There were comments about AIDS, which emerged in the 1980s and was formerly known as a homosexual disease, as "a punishment given by God to sinners". Along with cancer, tuberculosis, leprosy, syphilis, and epilepsy, AIDS has also become one of the diseases that create stigma [20].

#### **4. COVID-19 and stigma**

During the pandemic process, people's relationships with each other have changed. Newborn baby visits could not be made, university graduates could not share their graduation moments with their loved ones. Many people were not able to physically meet with their elderly family members. Many people were unable to attend the funerals of their closest relatives and friends, and could not adequately mourn their loss [21]. As a result of all these changes, stigma has been an important problem during the COVID-19 pandemic period. It has been modified through human interactions, social distancing, and other restrictions to limit the spread of the virus. Much more emphasis has been put on health systems, which are often under heavy load, and they have become inadequate. Inadequacies in health systems have led to inequalities among people in accessing health. As a result of health inequalities, the COVID-19 pandemic has rekindled or strengthened sensitive social issues such as stigma, discrimination, and racism [22]. In a qualitative study conducted in Pakistan, participants described their neighbors' hostile attitudes when COVID-19 was detected in their family members. Neighbors asked some people to leave the neighborhood. A person whose spouse had COVID-19 reported that other family members were reluctant to meet with him, even though his spouse's test result was negative. This stigma has even been reported among doctors. A 55-yearold doctor whose wife contracted the coronavirus explained that despite a negative test result and taking protective measures at work, he was treated as if he was a carrier of the infection. People who have been found to have COVID-19 in their relatives have also been exposed to discrimination in the workplace. Some participants felt that the stigma and discrimination they faced in their region were so great. They reported that they had a problem and that they were planning to change their houses [23]. We can say that this group is at high risk of stigmatization since the disease especially affects the population over the age of 65. At the same time, returnees from abroad face stigma [24]. Stigma has become a serious problem for healthcare professionals, especially during the COVID-19 pandemic. During such epidemics of widespread infectious disease, healthcare workers are often stigmatized by people in their own community [3]. During the COVID-19 pandemic, healthcare workers are hailed as "heroes" in the media. However, this does not eliminate the possibility of discriminatory attitudes towards healthcare professionals based on the fear that healthcare professionals are carriers of COVID-19. In a study conducted in the United States and Canada during the COVID-19 pandemic, an online questionnaire about stigmatizing healthcare professionals was administered to 3551 non-healthcare workers. More than a quarter of respondents reported that health workers should be kept separate from their communities and families. More than one-third of respondents reported avoiding healthcare workers for fear of infection. People

*Perspective Chapter: Psychosocial Impact of COVID-19 – Stigma and Xenophobia DOI: http://dx.doi.org/10.5772/intechopen.101527*

who stigmatize healthcare workers also tend to avoid other people, avoid pharmacies and supermarkets, and stay at home all the time [25]. In a study conducted in Egypt with 509 physicians, 138 of whom directly care for COVID-19 patients, 159 (31.2%) physicians reported severe COVID-19-related stigma. The overall COVID-19-related stigma score was higher in those working in the quarantine hospital. A significant number of physicians have experienced the stigma associated with COVID-19 [26]. In another study conducted with 529 physicians during the COVID-19 process, approximately one-third (31%) of the participants reported that they were concerned about stigma due to their profession as a healthcare workers. About 13.8% reported that they were worried about avoiding family members due to stigma [27]. Stigma adds an unnecessary burden to healthcare workers' lives and can contribute to healthcare worker burnout [28]. For this reason, protective measures should be taken against stigmatization, especially among healthcare professionals, and more support should be provided to healthcare professionals in this regard. In cases where stigma is high, people may tend to hide the disease as a coping strategy to avoid discrimination. Hiding infections and avoiding testing is a serious problem and can contribute to an increased risk of infection and a decrease in protective behaviors [29]. Additionally, the stigma associated with COVID-19 can become a barrier to control and prevent COVID-19. It is because people with high levels of stigma are less likely to explain their health status and seek treatment [13]. WHO Director-General Dr. As Tedros Adhanom Ghebreyesus stated that "Honestly, stigma is more dangerous than the coronavirus itself" [30]. The stigma associated with infectious diseases is associated with the clinical features of such diseases and socio-cultural factors [31, 32]. For example, hepatitis A survivors rarely suffer from stigma, but hepatitis B and C survivors often experience high levels of stigma due to more serious long-term effects [33]. Because COVID-19 disease is caused by a new virus, it is not clear how widespread or severe stigma is among survivors. The effect of stigma in the future is a matter of curiosity.

#### **5. Definition of xenophobia**

Xenophobia is literally a compound word formed by two Latin words. It found its semantic equivalent with the combination of the words xénos, which indicates the difference, and phóbos, which means fear and/or horror, and entered the literature in the sense of the individual or society's fear of the foreign and different from themselves. The state of being afraid of strangers, which is mentioned within the scope of the definition of xenophobia, also includes a discriminating, hateful, humiliating, and standardizing hostile attitude towards a different person. In this context, the target audience includes people who are not those from the relevant region, who are not citizens, or who differ in this context [34]. Xenophobia is defined as "attitudes, prejudices, and behaviors that reject, exclude, and often disparage people who are foreign to the society or national identity or who are perceived as a foreign" [35]. In order to understand the concept of xenophobia, it is useful to distinguish it from the concept of racism, which is often confused with each other. While xenophobia expresses a behavior based on the idea that the other is alien to the group; racism explains discrimination based on differences in physical characteristics such as skin color, hair type, and face shape. Racism is also considered the most extreme level of xenophobia [35]. In recent years, migration movements have increased and continue to increase in the world with the expectation of regional conflicts, climate change, security, employment, education, health, and generally higher welfare [36]. While the concept of alien can be considered a universal concept, this is not the case for xenophobia. Xenophobia is an emerging

concept: it was first used by Anatole France in 1901 in Monsieur Bergeret a Paris. Situations such as the term anti-Semitism of the Dreyfus affair that shook domestic politics in France at the turn of the twentieth century, and the violent form of nationalism that emerged at the time provided a social and political background for xenophobia [37]. In 1906—the full year of Dreyfus' rehabilitation - xenophobia was first listed in a French dictionary: Nou eau Larousse illustre. In the following years, it was included in the dictionary in many languages, especially in English [38]. The concept of xenophobia came to the fore again in the COVID-19 epidemic and became the subject of research.


**Author details**

Raid D. Hashim Al-Rasheed University College, Baghdad, Iraq

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**7. Lead in surface water**

At many points in the region where villagers used surface water sources (i.e. ponds, rivers, streams, lakes and pools), for the processing of lead-contaminated ore to obtain gold, the concentration levels of lead found in surface water bodies were mostly greater than 1000 μg/l. This concentration is said to be ten times higher than the exposure limit set by the FAO for livestock. Consequently, since livestock in the area usually drink from surface water sources, this could have been the reason for the frequent cases of illness and death of livestock often reported by the villagers. Consequently, the slaughter and consumption of any lead-poisoned animal would constitute an important exposure route for humans [17].


**Appendix A. Evaluation Criteria of the Cadastral System Organization**

Within the framework of the EFQM excellence model, nine criteria were developed and distributed to the respondents in the form of Likert scale questions. The concept behind this research is that people results, customer results, and societal results are achieved through leadership-driving cadastral policy and strategy, people, partnership and resources and processes, which leads ultimately to excellence in cadastral organizational performance achievements. There are included sub-criteria, though broad questions are only presented here.


Parkinson's Disease Understanding Pathophysiology and Developing Therapeutic Strategies

*Edited by Sarat Chandra Yenisetti*

**PARKINSON'S DISEASE -**

**PATHOPHYSIOLOGY AND**

**UNDERSTANDING**

Edited by **Sarat Chandra Yenisetti**

**DEVELOPING**

**THERAPEUTIC**

**STRATEGIES**